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What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper. In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis . 

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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10 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

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The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper.

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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How to Write a Great Hypothesis

Hypothesis Format, Examples, and Tips

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis, operational definitions, types of hypotheses, hypotheses examples.

• Collecting Data

Frequently Asked Questions

A hypothesis is a tentative statement about the relationship between two or more  variables. It is a specific, testable prediction about what you expect to happen in a study.

One hypothesis example would be a study designed to look at the relationship between sleep deprivation and test performance might have a hypothesis that states: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. It is only at this point that researchers begin to develop a testable hypothesis. Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore a number of factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk wisdom that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis.   In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in a number of different ways. One of the basic principles of any type of scientific research is that the results must be replicable.   By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. How would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

In order to measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming other people. In this situation, the researcher might utilize a simulated task to measure aggressiveness.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests that there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type of hypothesis suggests a relationship between three or more variables, such as two independent variables and a dependent variable.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative sample of the population and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• Complex hypothesis: "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "Children who receive a new reading intervention will have scores different than students who do not receive the intervention."
• "There will be no difference in scores on a memory recall task between children and adults."

Examples of an alternative hypothesis:

• "Children who receive a new reading intervention will perform better than students who did not receive the intervention."
• "Adults will perform better on a memory task than children." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when it would be impossible or difficult to  conduct an experiment . These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a correlational study can then be used to look at how the variables are related. This type of research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

A Word From Verywell

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Some examples of how to write a hypothesis include:

• "Staying up late will lead to worse test performance the next day."
• "People who consume one apple each day will visit the doctor fewer times each year."
• "Breaking study sessions up into three 20-minute sessions will lead to better test results than a single 60-minute study session."

The four parts of a hypothesis are:

• The research question
• The independent variable (IV)
• The dependent variable (DV)
• The proposed relationship between the IV and DV

Castillo M. The scientific method: a need for something better? . AJNR Am J Neuroradiol. 2013;34(9):1669-71. doi:10.3174/ajnr.A3401

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Educational resources and simple solutions for your research journey

What is a research hypothesis: How to write it, types, and examples

Any research begins with a research question and a research hypothesis . A research question alone may not suffice to design the experiment(s) needed to answer it. A hypothesis is central to the scientific method. But what is a hypothesis ? A hypothesis is a testable statement that proposes a possible explanation to a phenomenon, and it may include a prediction. Next, you may ask what is a research hypothesis ? Simply put, a research hypothesis is a prediction or educated guess about the relationship between the variables that you want to investigate.  

It is important to be thorough when developing your research hypothesis. Shortcomings in the framing of a hypothesis can affect the study design and the results. A better understanding of the research hypothesis definition and characteristics of a good hypothesis will make it easier for you to develop your own hypothesis for your research. Let’s dive in to know more about the types of research hypothesis , how to write a research hypothesis , and some research hypothesis examples .  

Table of Contents

What is a hypothesis ?  

A hypothesis is based on the existing body of knowledge in a study area. Framed before the data are collected, a hypothesis states the tentative relationship between independent and dependent variables, along with a prediction of the outcome.  

What is a research hypothesis ?  

Young researchers starting out their journey are usually brimming with questions like “ What is a hypothesis ?” “ What is a research hypothesis ?” “How can I write a good research hypothesis ?”   

A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation.     

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

• Clearly formulated and free of language errors and ambiguity  
• Concise and not unnecessarily verbose  
• Has clearly defined variables  
• Testable and stated in a way that allows for it to be disproven  
• Can be tested using a research design that is feasible, ethical, and practical   
• Specific and relevant to the research problem  
• Rooted in a thorough literature search  
• Can generate new knowledge or understanding.  

How to create an effective research hypothesis  

A study begins with the formulation of a research question. A researcher then performs background research. This background information forms the basis for building a good research hypothesis . The researcher then performs experiments, collects, and analyzes the data, interprets the findings, and ultimately, determines if the findings support or negate the original hypothesis.  

Let’s look at each step for creating an effective, testable, and good research hypothesis :  

• Identify a research problem or question: Start by identifying a specific research problem.   
• Review the literature: Conduct an in-depth review of the existing literature related to the research problem to grasp the current knowledge and gaps in the field.   
• Formulate a clear and testable hypothesis : Based on the research question, use existing knowledge to form a clear and testable hypothesis . The hypothesis should state a predicted relationship between two or more variables that can be measured and manipulated. Improve the original draft till it is clear and meaningful.  
• State the null hypothesis: The null hypothesis is a statement that there is no relationship between the variables you are studying.   
• Define the population and sample: Clearly define the population you are studying and the sample you will be using for your research.  
• Select appropriate methods for testing the hypothesis: Select appropriate research methods, such as experiments, surveys, or observational studies, which will allow you to test your research hypothesis .  

Remember that creating a research hypothesis is an iterative process, i.e., you might have to revise it based on the data you collect. You may need to test and reject several hypotheses before answering the research problem.  

How to write a research hypothesis  

When you start writing a research hypothesis , you use an “if–then” statement format, which states the predicted relationship between two or more variables. Clearly identify the independent variables (the variables being changed) and the dependent variables (the variables being measured), as well as the population you are studying. Review and revise your hypothesis as needed.  

An example of a research hypothesis in this format is as follows:  

“ If [athletes] follow [cold water showers daily], then their [endurance] increases.”  

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

You may have understood the characteristics of a good hypothesis . But note that a research hypothesis is not always confirmed; a researcher should be prepared to accept or reject the hypothesis based on the study findings.  

Research hypothesis checklist  

Following from above, here is a 10-point checklist for a good research hypothesis :  

• Testable: A research hypothesis should be able to be tested via experimentation or observation.  
• Specific: A research hypothesis should clearly state the relationship between the variables being studied.  
• Based on prior research: A research hypothesis should be based on existing knowledge and previous research in the field.  
• Falsifiable: A research hypothesis should be able to be disproven through testing.  
• Clear and concise: A research hypothesis should be stated in a clear and concise manner.  
• Logical: A research hypothesis should be logical and consistent with current understanding of the subject.  
• Relevant: A research hypothesis should be relevant to the research question and objectives.  
• Feasible: A research hypothesis should be feasible to test within the scope of the study.  
• Reflects the population: A research hypothesis should consider the population or sample being studied.  
• Uncomplicated: A good research hypothesis is written in a way that is easy for the target audience to understand.  

By following this research hypothesis checklist , you will be able to create a research hypothesis that is strong, well-constructed, and more likely to yield meaningful results.  

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

A null hypothesis states that there is no change in the dependent variable due to changes to the independent variable. This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states.   

Example: “ The newly identified virus is not zoonotic .”  

2. Alternative hypothesis:

This states that there is a significant difference or relationship between the variables being studied. It is denoted as H1 or Ha and is usually accepted or rejected in favor of the null hypothesis.  

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

This specifies the direction of the relationship or difference between variables; therefore, it tends to use terms like increase, decrease, positive, negative, more, or less.   

Example: “ The inclusion of intervention X decreases infant mortality compared to the original treatment .”   

4. Non-directional hypothesis:

While it does not predict the exact direction or nature of the relationship between the two variables, a non-directional hypothesis states the existence of a relationship or difference between variables but not the direction, nature, or magnitude of the relationship. A non-directional hypothesis may be used when there is no underlying theory or when findings contradict previous research.  

Example, “ Cats and dogs differ in the amount of affection they express .”  

5. Simple hypothesis :

A simple hypothesis only predicts the relationship between one independent and another independent variable.  

Example: “ Applying sunscreen every day slows skin aging .”  

6 . Complex hypothesis :

A complex hypothesis states the relationship or difference between two or more independent and dependent variables.   

Example: “ Applying sunscreen every day slows skin aging, reduces sun burn, and reduces the chances of skin cancer .” (Here, the three dependent variables are slowing skin aging, reducing sun burn, and reducing the chances of skin cancer.)  

7. Associative hypothesis:  

An associative hypothesis states that a change in one variable results in the change of the other variable. The associative hypothesis defines interdependency between variables.  

Example: “ There is a positive association between physical activity levels and overall health .”  

8 . Causal hypothesis:

A causal hypothesis proposes a cause-and-effect interaction between variables.  

Example: “ Long-term alcohol use causes liver damage .”  

Note that some of the types of research hypothesis mentioned above might overlap. The types of hypothesis chosen will depend on the research question and the objective of the study.  

Research hypothesis examples  

Here are some good research hypothesis examples :  

“The use of a specific type of therapy will lead to a reduction in symptoms of depression in individuals with a history of major depressive disorder.”  

“Providing educational interventions on healthy eating habits will result in weight loss in overweight individuals.”  

“Plants that are exposed to certain types of music will grow taller than those that are not exposed to music.”  

“The use of the plant growth regulator X will lead to an increase in the number of flowers produced by plants.”  

Characteristics that make a research hypothesis weak are unclear variables, unoriginality, being too general or too vague, and being untestable. A weak hypothesis leads to weak research and improper methods.   

Some bad research hypothesis examples (and the reasons why they are “bad”) are as follows:  

“This study will show that treatment X is better than any other treatment . ” (This statement is not testable, too broad, and does not consider other treatments that may be effective.)  

“This study will prove that this type of therapy is effective for all mental disorders . ” (This statement is too broad and not testable as mental disorders are complex and different disorders may respond differently to different types of therapy.)  

“Plants can communicate with each other through telepathy . ” (This statement is not testable and lacks a scientific basis.)  

Importance of testable hypothesis  

If a research hypothesis is not testable, the results will not prove or disprove anything meaningful. The conclusions will be vague at best. A testable hypothesis helps a researcher focus on the study outcome and understand the implication of the question and the different variables involved. A testable hypothesis helps a researcher make precise predictions based on prior research.  

To be considered testable, there must be a way to prove that the hypothesis is true or false; further, the results of the hypothesis must be reproducible.  

Frequently Asked Questions (FAQs) on research hypothesis  

1. What is the difference between research question and research hypothesis ?  

A research question defines the problem and helps outline the study objective(s). It is an open-ended statement that is exploratory or probing in nature. Therefore, it does not make predictions or assumptions. It helps a researcher identify what information to collect. A research hypothesis , however, is a specific, testable prediction about the relationship between variables. Accordingly, it guides the study design and data analysis approach.

2. When to reject null hypothesis ?

A null hypothesis should be rejected when the evidence from a statistical test shows that it is unlikely to be true. This happens when the test statistic (e.g., p -value) is less than the defined significance level (e.g., 0.05). Rejecting the null hypothesis does not necessarily mean that the alternative hypothesis is true; it simply means that the evidence found is not compatible with the null hypothesis.  

3. How can I be sure my hypothesis is testable?  

A testable hypothesis should be specific and measurable, and it should state a clear relationship between variables that can be tested with data. To ensure that your hypothesis is testable, consider the following:  

• Clearly define the key variables in your hypothesis. You should be able to measure and manipulate these variables in a way that allows you to test the hypothesis.  
• The hypothesis should predict a specific outcome or relationship between variables that can be measured or quantified.   
• You should be able to collect the necessary data within the constraints of your study.  
• It should be possible for other researchers to replicate your study, using the same methods and variables.   
• Your hypothesis should be testable by using appropriate statistical analysis techniques, so you can draw conclusions, and make inferences about the population from the sample data.  
• The hypothesis should be able to be disproven or rejected through the collection of data.  

4. How do I revise my research hypothesis if my data does not support it?  

If your data does not support your research hypothesis , you will need to revise it or develop a new one. You should examine your data carefully and identify any patterns or anomalies, re-examine your research question, and/or revisit your theory to look for any alternative explanations for your results. Based on your review of the data, literature, and theories, modify your research hypothesis to better align it with the results you obtained. Use your revised hypothesis to guide your research design and data collection. It is important to remain objective throughout the process.  

5. I am performing exploratory research. Do I need to formulate a research hypothesis?  

As opposed to “confirmatory” research, where a researcher has some idea about the relationship between the variables under investigation, exploratory research (or hypothesis-generating research) looks into a completely new topic about which limited information is available. Therefore, the researcher will not have any prior hypotheses. In such cases, a researcher will need to develop a post-hoc hypothesis. A post-hoc research hypothesis is generated after these results are known.  

6. How is a research hypothesis different from a research question?

A research question is an inquiry about a specific topic or phenomenon, typically expressed as a question. It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis.

7. Can a research hypothesis change during the research process?

Yes, research hypotheses can change during the research process. As researchers collect and analyze data, new insights and information may emerge that require modification or refinement of the initial hypotheses. This can be due to unexpected findings, limitations in the original hypotheses, or the need to explore additional dimensions of the research topic. Flexibility is crucial in research, allowing for adaptation and adjustment of hypotheses to align with the evolving understanding of the subject matter.

8. How many hypotheses should be included in a research study?

The number of research hypotheses in a research study varies depending on the nature and scope of the research. It is not necessary to have multiple hypotheses in every study. Some studies may have only one primary hypothesis, while others may have several related hypotheses. The number of hypotheses should be determined based on the research objectives, research questions, and the complexity of the research topic. It is important to ensure that the hypotheses are focused, testable, and directly related to the research aims.

9. Can research hypotheses be used in qualitative research?

Yes, research hypotheses can be used in qualitative research, although they are more commonly associated with quantitative research. In qualitative research, hypotheses may be formulated as tentative or exploratory statements that guide the investigation. Instead of testing hypotheses through statistical analysis, qualitative researchers may use the hypotheses to guide data collection and analysis, seeking to uncover patterns, themes, or relationships within the qualitative data. The emphasis in qualitative research is often on generating insights and understanding rather than confirming or rejecting specific research hypotheses through statistical testing.

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How to Develop a Good Research Hypothesis

The story of a research study begins by asking a question. Researchers all around the globe are asking curious questions and formulating research hypothesis. However, whether the research study provides an effective conclusion depends on how well one develops a good research hypothesis. Research hypothesis examples could help researchers get an idea as to how to write a good research hypothesis.

This blog will help you understand what is a research hypothesis, its characteristics and, how to formulate a research hypothesis

Table of Contents

What is Hypothesis?

Hypothesis is an assumption or an idea proposed for the sake of argument so that it can be tested. It is a precise, testable statement of what the researchers predict will be outcome of the study.  Hypothesis usually involves proposing a relationship between two variables: the independent variable (what the researchers change) and the dependent variable (what the research measures).

What is a Research Hypothesis?

Research hypothesis is a statement that introduces a research question and proposes an expected result. It is an integral part of the scientific method that forms the basis of scientific experiments. Therefore, you need to be careful and thorough when building your research hypothesis. A minor flaw in the construction of your hypothesis could have an adverse effect on your experiment. In research, there is a convention that the hypothesis is written in two forms, the null hypothesis, and the alternative hypothesis (called the experimental hypothesis when the method of investigation is an experiment).

Essential Characteristics of a Good Research Hypothesis

As the hypothesis is specific, there is a testable prediction about what you expect to happen in a study. You may consider drawing hypothesis from previously published research based on the theory.

A good research hypothesis involves more effort than just a guess. In particular, your hypothesis may begin with a question that could be further explored through background research.

To help you formulate a promising research hypothesis, you should ask yourself the following questions:

• Is the language clear and focused?
• What is the relationship between your hypothesis and your research topic?
• Is your hypothesis testable? If yes, then how?
• What are the possible explanations that you might want to explore?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate your variables without hampering the ethical standards?
• Does your research predict the relationship and outcome?
• Is your research simple and concise (avoids wordiness)?
• Is it clear with no ambiguity or assumptions about the readers’ knowledge
• Is your research observable and testable results?
• Is it relevant and specific to the research question or problem?

The questions listed above can be used as a checklist to make sure your hypothesis is based on a solid foundation. Furthermore, it can help you identify weaknesses in your hypothesis and revise it if necessary.

Source: Educational Hub

How to formulate an effective research hypothesis.

A testable hypothesis is not a simple statement. It is rather an intricate statement that needs to offer a clear introduction to a scientific experiment, its intentions, and the possible outcomes. However, there are some important things to consider when building a compelling hypothesis.

1. State the problem that you are trying to solve.

Make sure that the hypothesis clearly defines the topic and the focus of the experiment.

2. Try to write the hypothesis as an if-then statement.

Follow this template: If a specific action is taken, then a certain outcome is expected.

3. Define the variables

Independent variables are the ones that are manipulated, controlled, or changed. Independent variables are isolated from other factors of the study.

Dependent variables , as the name suggests are dependent on other factors of the study. They are influenced by the change in independent variable.

4. Scrutinize the hypothesis

The types of research hypothesis are stated below:

1. Simple Hypothesis

It predicts the relationship between a single dependent variable and a single independent variable.

2. Complex Hypothesis

It predicts the relationship between two or more independent and dependent variables.

3. Directional Hypothesis

It specifies the expected direction to be followed to determine the relationship between variables and is derived from theory. Furthermore, it implies the researcher’s intellectual commitment to a particular outcome.

4. Non-directional Hypothesis

It does not predict the exact direction or nature of the relationship between the two variables. The non-directional hypothesis is used when there is no theory involved or when findings contradict previous research.

5. Associative and Causal Hypothesis

The associative hypothesis defines interdependency between variables. A change in one variable results in the change of the other variable. On the other hand, the causal hypothesis proposes an effect on the dependent due to manipulation of the independent variable.

6. Null Hypothesis

Null hypothesis states a negative statement to support the researcher’s findings that there is no relationship between two variables. There will be no changes in the dependent variable due the manipulation of the independent variable. Furthermore, it states results are due to chance and are not significant in terms of supporting the idea being investigated.

7. Alternative Hypothesis

It states that there is a relationship between the two variables of the study and that the results are significant to the research topic. An experimental hypothesis predicts what changes will take place in the dependent variable when the independent variable is manipulated. Also, it states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

Research Hypothesis Examples of Independent and Dependent Variables:

Research Hypothesis Example 1 The greater number of coal plants in a region (independent variable) increases water pollution (dependent variable). If you change the independent variable (building more coal factories), it will change the dependent variable (amount of water pollution).

Research Hypothesis Example 2 What is the effect of diet or regular soda (independent variable) on blood sugar levels (dependent variable)? If you change the independent variable (the type of soda you consume), it will change the dependent variable (blood sugar levels)

You should not ignore the importance of the above steps. The validity of your experiment and its results rely on a robust testable hypothesis. Developing a strong testable hypothesis has few advantages, it compels us to think intensely and specifically about the outcomes of a study. Consequently, it enables us to understand the implication of the question and the different variables involved in the study. Furthermore, it helps us to make precise predictions based on prior research. Hence, forming a hypothesis would be of great value to the research. Here are some good examples of testable hypotheses.

More importantly, you need to build a robust testable research hypothesis for your scientific experiments. A testable hypothesis is a hypothesis that can be proved or disproved as a result of experimentation.

Importance of a Testable Hypothesis

To devise and perform an experiment using scientific method, you need to make sure that your hypothesis is testable. To be considered testable, some essential criteria must be met:

• There must be a possibility to prove that the hypothesis is true.
• There must be a possibility to prove that the hypothesis is false.
• The results of the hypothesis must be reproducible.

Without these criteria, the hypothesis and the results will be vague. As a result, the experiment will not prove or disprove anything significant.

What are your experiences with building hypotheses for scientific experiments? What challenges did you face? How did you overcome these challenges? Please share your thoughts with us in the comments section.

Frequently Asked Questions

The steps to write a research hypothesis are: 1. Stating the problem: Ensure that the hypothesis defines the research problem 2. Writing a hypothesis as an 'if-then' statement: Include the action and the expected outcome of your study by following a ‘if-then’ structure. 3. Defining the variables: Define the variables as Dependent or Independent based on their dependency to other factors. 4. Scrutinizing the hypothesis: Identify the type of your hypothesis

Hypothesis testing is a statistical tool which is used to make inferences about a population data to draw conclusions for a particular hypothesis.

Hypothesis in statistics is a formal statement about the nature of a population within a structured framework of a statistical model. It is used to test an existing hypothesis by studying a population.

Research hypothesis is a statement that introduces a research question and proposes an expected result. It forms the basis of scientific experiments.

The different types of hypothesis in research are: • Null hypothesis: Null hypothesis is a negative statement to support the researcher’s findings that there is no relationship between two variables. • Alternate hypothesis: Alternate hypothesis predicts the relationship between the two variables of the study. • Directional hypothesis: Directional hypothesis specifies the expected direction to be followed to determine the relationship between variables. • Non-directional hypothesis: Non-directional hypothesis does not predict the exact direction or nature of the relationship between the two variables. • Simple hypothesis: Simple hypothesis predicts the relationship between a single dependent variable and a single independent variable. • Complex hypothesis: Complex hypothesis predicts the relationship between two or more independent and dependent variables. • Associative and casual hypothesis: Associative and casual hypothesis predicts the relationship between two or more independent and dependent variables. • Empirical hypothesis: Empirical hypothesis can be tested via experiments and observation. • Statistical hypothesis: A statistical hypothesis utilizes statistical models to draw conclusions about broader populations.

Wow! You really simplified your explanation that even dummies would find it easy to comprehend. Thank you so much.

Thanks a lot for your valuable guidance.

I enjoy reading the post. Hypotheses are actually an intrinsic part in a study. It bridges the research question and the methodology of the study.

Useful piece!

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It very interesting to read the topic, can you guide me any specific example of hypothesis process establish throw the Demand and supply of the specific product in market

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It is really a useful for me Kindly give some examples of hypothesis

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clear and concise. thanks.

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Research Hypothesis: Definition, Types, & Examples

Saul Mcleod, PhD

Educator, Researcher

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, Ph.D., is a qualified psychology teacher with over 18 years experience of working in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

A hypothesis (plural hypotheses) is a precise, testable statement of what the researcher(s) predict will be the outcome of the study. It is stated at the start of the study.

This usually involves proposing a possible relationship between two variables: the independent variable (what the researcher changes) and the dependent variable (what the research measures).

In research, there is a convention that the hypothesis is written in two forms, the null hypothesis, and the alternative hypothesis (called the experimental hypothesis when the method of investigation is an experiment ).

A fundamental requirement of a hypothesis is that is can be tested against reality, and can then be supported or rejected.

To test a hypothesis the researcher first assumes that there is no difference between populations from which they are taken. This is known as the null hypothesis. The research hypothesis is often called the alternative hypothesis.

Table of Contents

Types of research hypotheses

Alternative hypothesis.

The alternative hypothesis states that there is a relationship between the two variables being studied (one variable has an effect on the other).

An experimental hypothesis predicts what change(s) will take place in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

Null Hypothesis

The null hypothesis states that there is no relationship between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to the manipulation of the independent variable.

It states results are due to chance and are not significant in terms of supporting the idea being investigated.

Nondirectional Hypothesis

A non-directional (two-tailed) hypothesis predicts that the independent variable will have an effect on the dependent variable, but the direction of the effect is not specified. It just states that there will be a difference.

E.g., there will be a difference in how many numbers are correctly recalled by children and adults.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e. greater, smaller, less, more)

E.g., adults will correctly recall more words than children.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory to be considered scientific it must be able to be tested and conceivably proven false.

However many confirming instances there are for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory, rather than attempt to continually support theoretical hypotheses.

Can a hypothesis be proven?

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to write a hypothesis

• 1. To write the alternative and null hypotheses for an investigation, you need to identify the key variables in the study.The independent variable is manipulated by the researcher and the dependent variable is the outcome which is measured.
• 2. Operationalized the variables being investigated.Operationalisation of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression you might count the number of punches given by participants
• 3. Decide on a direction for your prediction. If there is evidence in the literature to support a specific effect on the independent variable on the dependent variable, write a directional (one-tailed) hypothesis.If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• 4. Write your hypothesis. A good hypothesis is short (i.e. concise) and comprises clear and simple language.

What are examples of a hypothesis?

Let’s consider a hypothesis that many teachers might subscribe to: that students work better on Monday morning than they do on a Friday afternoon (IV=Day, DV=Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and on a Friday afternoon and then measuring their immediate recall on the material covered in each session we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

The null hypothesis is, therefore, the opposite of the alternative hypothesis in that it states that there will be no change in behavior.

At this point, you might be asking why we seem so interested in the null hypothesis. Surely the alternative (or experimental) hypothesis is more important?

Well, yes it is. However, we can never 100% prove the alternative hypothesis. What we do instead is see if we can disprove, or reject, the null hypothesis.

If we reject the null hypothesis, this doesn’t really mean that our alternative hypothesis is correct – but it does provide support for the alternative / experimental hypothesis.

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What is and How to Write a Good Hypothesis in Research?

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Table of Contents

One of the most important aspects of conducting research is constructing a strong hypothesis. But what makes a hypothesis in research effective? In this article, we’ll look at the difference between a hypothesis and a research question, as well as the elements of a good hypothesis in research. We’ll also include some examples of effective hypotheses, and what pitfalls to avoid.

What is a Hypothesis in Research?

Simply put, a hypothesis is a research question that also includes the predicted or expected result of the research. Without a hypothesis, there can be no basis for a scientific or research experiment. As such, it is critical that you carefully construct your hypothesis by being deliberate and thorough, even before you set pen to paper. Unless your hypothesis is clearly and carefully constructed, any flaw can have an adverse, and even grave, effect on the quality of your experiment and its subsequent results.

Research Question vs Hypothesis

It’s easy to confuse research questions with hypotheses, and vice versa. While they’re both critical to the Scientific Method, they have very specific differences. Primarily, a research question, just like a hypothesis, is focused and concise. But a hypothesis includes a prediction based on the proposed research, and is designed to forecast the relationship of and between two (or more) variables. Research questions are open-ended, and invite debate and discussion, while hypotheses are closed, e.g. “The relationship between A and B will be C.”

A hypothesis is generally used if your research topic is fairly well established, and you are relatively certain about the relationship between the variables that will be presented in your research. Since a hypothesis is ideally suited for experimental studies, it will, by its very existence, affect the design of your experiment. The research question is typically used for new topics that have not yet been researched extensively. Here, the relationship between different variables is less known. There is no prediction made, but there may be variables explored. The research question can be casual in nature, simply trying to understand if a relationship even exists, descriptive or comparative.

How to Write Hypothesis in Research

Writing an effective hypothesis starts before you even begin to type. Like any task, preparation is key, so you start first by conducting research yourself, and reading all you can about the topic that you plan to research. From there, you’ll gain the knowledge you need to understand where your focus within the topic will lie.

Remember that a hypothesis is a prediction of the relationship that exists between two or more variables. Your job is to write a hypothesis, and design the research, to “prove” whether or not your prediction is correct. A common pitfall is to use judgments that are subjective and inappropriate for the construction of a hypothesis. It’s important to keep the focus and language of your hypothesis objective.

An effective hypothesis in research is clearly and concisely written, and any terms or definitions clarified and defined. Specific language must also be used to avoid any generalities or assumptions.

Use the following points as a checklist to evaluate the effectiveness of your research hypothesis:

• Predicts the relationship and outcome
• Simple and concise – avoid wordiness
• Clear with no ambiguity or assumptions about the readers’ knowledge
• Observable and testable results
• Relevant and specific to the research question or problem

Research Hypothesis Example

Perhaps the best way to evaluate whether or not your hypothesis is effective is to compare it to those of your colleagues in the field. There is no need to reinvent the wheel when it comes to writing a powerful research hypothesis. As you’re reading and preparing your hypothesis, you’ll also read other hypotheses. These can help guide you on what works, and what doesn’t, when it comes to writing a strong research hypothesis.

Here are a few generic examples to get you started.

Eating an apple each day, after the age of 60, will result in a reduction of frequency of physician visits.

Budget airlines are more likely to receive more customer complaints. A budget airline is defined as an airline that offers lower fares and fewer amenities than a traditional full-service airline. (Note that the term “budget airline” is included in the hypothesis.

Workplaces that offer flexible working hours report higher levels of employee job satisfaction than workplaces with fixed hours.

Each of the above examples are specific, observable and measurable, and the statement of prediction can be verified or shown to be false by utilizing standard experimental practices. It should be noted, however, that often your hypothesis will change as your research progresses.

Language Editing Plus

Elsevier’s Language Editing Plus service can help ensure that your research hypothesis is well-designed, and articulates your research and conclusions. Our most comprehensive editing package, you can count on a thorough language review by native-English speakers who are PhDs or PhD candidates. We’ll check for effective logic and flow of your manuscript, as well as document formatting for your chosen journal, reference checks, and much more.

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Scientific Hypotheses: Writing, Promoting, and Predicting Implications

Armen yuri gasparyan.

1 Departments of Rheumatology and Research and Development, Dudley Group NHS Foundation Trust (Teaching Trust of the University of Birmingham, UK), Russells Hall Hospital, Dudley, West Midlands, UK.

Lilit Ayvazyan

2 Department of Medical Chemistry, Yerevan State Medical University, Yerevan, Armenia.

Ulzhan Mukanova

3 Department of Surgical Disciplines, South Kazakhstan Medical Academy, Shymkent, Kazakhstan.

Marlen Yessirkepov

4 Department of Biology and Biochemistry, South Kazakhstan Medical Academy, Shymkent, Kazakhstan.

George D. Kitas

5 Arthritis Research UK Epidemiology Unit, University of Manchester, Manchester, UK.

Scientific hypotheses are essential for progress in rapidly developing academic disciplines. Proposing new ideas and hypotheses require thorough analyses of evidence-based data and predictions of the implications. One of the main concerns relates to the ethical implications of the generated hypotheses. The authors may need to outline potential benefits and limitations of their suggestions and target widely visible publication outlets to ignite discussion by experts and start testing the hypotheses. Not many publication outlets are currently welcoming hypotheses and unconventional ideas that may open gates to criticism and conservative remarks. A few scholarly journals guide the authors on how to structure hypotheses. Reflecting on general and specific issues around the subject matter is often recommended for drafting a well-structured hypothesis article. An analysis of influential hypotheses, presented in this article, particularly Strachan's hygiene hypothesis with global implications in the field of immunology and allergy, points to the need for properly interpreting and testing new suggestions. Envisaging the ethical implications of the hypotheses should be considered both by authors and journal editors during the writing and publishing process.

INTRODUCTION

We live in times of digitization that radically changes scientific research, reporting, and publishing strategies. Researchers all over the world are overwhelmed with processing large volumes of information and searching through numerous online platforms, all of which make the whole process of scholarly analysis and synthesis complex and sophisticated.

Current research activities are diversifying to combine scientific observations with analysis of facts recorded by scholars from various professional backgrounds. 1 Citation analyses and networking on social media are also becoming essential for shaping research and publishing strategies globally. 2 Learning specifics of increasingly interdisciplinary research studies and acquiring information facilitation skills aid researchers in formulating innovative ideas and predicting developments in interrelated scientific fields.

Arguably, researchers are currently offered more opportunities than in the past for generating new ideas by performing their routine laboratory activities, observing individual cases and unusual developments, and critically analyzing published scientific facts. What they need at the start of their research is to formulate a scientific hypothesis that revisits conventional theories, real-world processes, and related evidence to propose new studies and test ideas in an ethical way. 3 Such a hypothesis can be of most benefit if published in an ethical journal with wide visibility and exposure to relevant online databases and promotion platforms.

Although hypotheses are crucially important for the scientific progress, only few highly skilled researchers formulate and eventually publish their innovative ideas per se . Understandably, in an increasingly competitive research environment, most authors would prefer to prioritize their ideas by discussing and conducting tests in their own laboratories or clinical departments, and publishing research reports afterwards. However, there are instances when simple observations and research studies in a single center are not capable of explaining and testing new groundbreaking ideas. Formulating hypothesis articles first and calling for multicenter and interdisciplinary research can be a solution in such instances, potentially launching influential scientific directions, if not academic disciplines.

The aim of this article is to overview the importance and implications of infrequently published scientific hypotheses that may open new avenues of thinking and research.

Despite the seemingly established views on innovative ideas and hypotheses as essential research tools, no structured definition exists to tag the term and systematically track related articles. In 1973, the Medical Subject Heading (MeSH) of the U.S. National Library of Medicine introduced “Research Design” as a structured keyword that referred to the importance of collecting data and properly testing hypotheses, and indirectly linked the term to ethics, methods and standards, among many other subheadings.

One of the experts in the field defines “hypothesis” as a well-argued analysis of available evidence to provide a realistic (scientific) explanation of existing facts, fill gaps in public understanding of sophisticated processes, and propose a new theory or a test. 4 A hypothesis can be proven wrong partially or entirely. However, even such an erroneous hypothesis may influence progress in science by initiating professional debates that help generate more realistic ideas. The main ethical requirement for hypothesis authors is to be honest about the limitations of their suggestions. 5

EXAMPLES OF INFLUENTIAL SCIENTIFIC HYPOTHESES

Daily routine in a research laboratory may lead to groundbreaking discoveries provided the daily accounts are comprehensively analyzed and reproduced by peers. The discovery of penicillin by Sir Alexander Fleming (1928) can be viewed as a prime example of such discoveries that introduced therapies to treat staphylococcal and streptococcal infections and modulate blood coagulation. 6 , 7 Penicillin got worldwide recognition due to the inventor's seminal works published by highly prestigious and widely visible British journals, effective ‘real-world’ antibiotic therapy of pneumonia and wounds during World War II, and euphoric media coverage. 8 In 1945, Fleming, Florey and Chain got a much deserved Nobel Prize in Physiology or Medicine for the discovery that led to the mass production of the wonder drug in the U.S. and ‘real-world practice’ that tested the use of penicillin. What remained globally unnoticed is that Zinaida Yermolyeva, the outstanding Soviet microbiologist, created the Soviet penicillin, which turned out to be more effective than the Anglo-American penicillin and entered mass production in 1943; that year marked the turning of the tide of the Great Patriotic War. 9 One of the reasons of the widely unnoticed discovery of Zinaida Yermolyeva is that her works were published exclusively by local Russian (Soviet) journals.

The past decades have been marked by an unprecedented growth of multicenter and global research studies involving hundreds and thousands of human subjects. This trend is shaped by an increasing number of reports on clinical trials and large cohort studies that create a strong evidence base for practice recommendations. Mega-studies may help generate and test large-scale hypotheses aiming to solve health issues globally. Properly designed epidemiological studies, for example, may introduce clarity to the hygiene hypothesis that was originally proposed by David Strachan in 1989. 10 David Strachan studied the epidemiology of hay fever in a cohort of 17,414 British children and concluded that declining family size and improved personal hygiene had reduced the chances of cross infections in families, resulting in epidemics of atopic disease in post-industrial Britain. Over the past four decades, several related hypotheses have been proposed to expand the potential role of symbiotic microorganisms and parasites in the development of human physiological immune responses early in life and protection from allergic and autoimmune diseases later on. 11 , 12 Given the popularity and the scientific importance of the hygiene hypothesis, it was introduced as a MeSH term in 2012. 13

Hypotheses can be proposed based on an analysis of recorded historic events that resulted in mass migrations and spreading of certain genetic diseases. As a prime example, familial Mediterranean fever (FMF), the prototype periodic fever syndrome, is believed to spread from Mesopotamia to the Mediterranean region and all over Europe due to migrations and religious prosecutions millennia ago. 14 Genetic mutations spearing mild clinical forms of FMF are hypothesized to emerge and persist in the Mediterranean region as protective factors against more serious infectious diseases, particularly tuberculosis, historically common in that part of the world. 15 The speculations over the advantages of carrying the MEditerranean FeVer (MEFV) gene are further strengthened by recorded low mortality rates from tuberculosis among FMF patients of different nationalities living in Tunisia in the first half of the 20th century. 16

Diagnostic hypotheses shedding light on peculiarities of diseases throughout the history of mankind can be formulated using artefacts, particularly historic paintings. 17 Such paintings may reveal joint deformities and disfigurements due to rheumatic diseases in individual subjects. A series of paintings with similar signs of pathological conditions interpreted in a historic context may uncover mysteries of epidemics of certain diseases, which is the case with Ruben's paintings depicting signs of rheumatic hands and making some doctors to believe that rheumatoid arthritis was common in Europe in the 16th and 17th century. 18

WRITING SCIENTIFIC HYPOTHESES

There are author instructions of a few journals that specifically guide how to structure, format, and make submissions categorized as hypotheses attractive. One of the examples is presented by Med Hypotheses , the flagship journal in its field with more than four decades of publishing and influencing hypothesis authors globally. However, such guidance is not based on widely discussed, implemented, and approved reporting standards, which are becoming mandatory for all scholarly journals.

Generating new ideas and scientific hypotheses is a sophisticated task since not all researchers and authors are skilled to plan, conduct, and interpret various research studies. Some experience with formulating focused research questions and strong working hypotheses of original research studies is definitely helpful for advancing critical appraisal skills. However, aspiring authors of scientific hypotheses may need something different, which is more related to discerning scientific facts, pooling homogenous data from primary research works, and synthesizing new information in a systematic way by analyzing similar sets of articles. To some extent, this activity is reminiscent of writing narrative and systematic reviews. As in the case of reviews, scientific hypotheses need to be formulated on the basis of comprehensive search strategies to retrieve all available studies on the topics of interest and then synthesize new information selectively referring to the most relevant items. One of the main differences between scientific hypothesis and review articles relates to the volume of supportive literature sources ( Table 1 ). In fact, hypothesis is usually formulated by referring to a few scientific facts or compelling evidence derived from a handful of literature sources. 19 By contrast, reviews require analyses of a large number of published documents retrieved from several well-organized and evidence-based databases in accordance with predefined search strategies. 20 , 21 , 22

The format of hypotheses, especially the implications part, may vary widely across disciplines. Clinicians may limit their suggestions to the clinical manifestations of diseases, outcomes, and management strategies. Basic and laboratory scientists analysing genetic, molecular, and biochemical mechanisms may need to view beyond the frames of their narrow fields and predict social and population-based implications of the proposed ideas. 23

Advanced writing skills are essential for presenting an interesting theoretical article which appeals to the global readership. Merely listing opposing facts and ideas, without proper interpretation and analysis, may distract the experienced readers. The essence of a great hypothesis is a story behind the scientific facts and evidence-based data.

ETHICAL IMPLICATIONS

The authors of hypotheses substantiate their arguments by referring to and discerning rational points from published articles that might be overlooked by others. Their arguments may contradict the established theories and practices, and pose global ethical issues, particularly when more or less efficient medical technologies and public health interventions are devalued. The ethical issues may arise primarily because of the careless references to articles with low priorities, inadequate and apparently unethical methodologies, and concealed reporting of negative results. 24 , 25

Misinterpretation and misunderstanding of the published ideas and scientific hypotheses may complicate the issue further. For example, Alexander Fleming, whose innovative ideas of penicillin use to kill susceptible bacteria saved millions of lives, warned of the consequences of uncontrolled prescription of the drug. The issue of antibiotic resistance had emerged within the first ten years of penicillin use on a global scale due to the overprescription that affected the efficacy of antibiotic therapies, with undesirable consequences for millions. 26

The misunderstanding of the hygiene hypothesis that primarily aimed to shed light on the role of the microbiome in allergic and autoimmune diseases resulted in decline of public confidence in hygiene with dire societal implications, forcing some experts to abandon the original idea. 27 , 28 Although that hypothesis is unrelated to the issue of vaccinations, the public misunderstanding has resulted in decline of vaccinations at a time of upsurge of old and new infections.

A number of ethical issues are posed by the denial of the viral (human immunodeficiency viruses; HIV) hypothesis of acquired Immune deficiency Syndrome (AIDS) by Peter Duesberg, who overviewed the links between illicit recreational drugs and antiretroviral therapies with AIDS and refuted the etiological role of HIV. 29 That controversial hypothesis was rejected by several journals, but was eventually published without external peer review at Med Hypotheses in 2010. The publication itself raised concerns of the unconventional editorial policy of the journal, causing major perturbations and more scrutinized publishing policies by journals processing hypotheses.

WHERE TO PUBLISH HYPOTHESES

Although scientific authors are currently well informed and equipped with search tools to draft evidence-based hypotheses, there are still limited quality publication outlets calling for related articles. The journal editors may be hesitant to publish articles that do not adhere to any research reporting guidelines and open gates for harsh criticism of unconventional and untested ideas. Occasionally, the editors opting for open-access publishing and upgrading their ethics regulations launch a section to selectively publish scientific hypotheses attractive to the experienced readers. 30 However, the absence of approved standards for this article type, particularly no mandate for outlining potential ethical implications, may lead to publication of potentially harmful ideas in an attractive format.

A suggestion of simultaneously publishing multiple or alternative hypotheses to balance the reader views and feedback is a potential solution for the mainstream scholarly journals. 31 However, that option alone is hardly applicable to emerging journals with unconventional quality checks and peer review, accumulating papers with multiple rejections by established journals.

A large group of experts view hypotheses with improbable and controversial ideas publishable after formal editorial (in-house) checks to preserve the authors' genuine ideas and avoid conservative amendments imposed by external peer reviewers. 32 That approach may be acceptable for established publishers with large teams of experienced editors. However, the same approach can lead to dire consequences if employed by nonselective start-up, open-access journals processing all types of articles and primarily accepting those with charged publication fees. 33 In fact, pseudoscientific ideas arguing Newton's and Einstein's seminal works or those denying climate change that are hardly testable have already found their niche in substandard electronic journals with soft or nonexistent peer review. 34

CITATIONS AND SOCIAL MEDIA ATTENTION

The available preliminary evidence points to the attractiveness of hypothesis articles for readers, particularly those from research-intensive countries who actively download related documents. 35 However, citations of such articles are disproportionately low. Only a small proportion of top-downloaded hypotheses (13%) in the highly prestigious Med Hypotheses receive on average 5 citations per article within a two-year window. 36

With the exception of a few historic papers, the vast majority of hypotheses attract relatively small number of citations in a long term. 36 Plausible explanations are that these articles often contain a single or only a few citable points and that suggested research studies to test hypotheses are rarely conducted and reported, limiting chances of citing and crediting authors of genuine research ideas.

A snapshot analysis of citation activity of hypothesis articles may reveal interest of the global scientific community towards their implications across various disciplines and countries. As a prime example, Strachan's hygiene hypothesis, published in 1989, 10 is still attracting numerous citations on Scopus, the largest bibliographic database. As of August 28, 2019, the number of the linked citations in the database is 3,201. Of the citing articles, 160 are cited at least 160 times ( h -index of this research topic = 160). The first three citations are recorded in 1992 and followed by a rapid annual increase in citation activity and a peak of 212 in 2015 ( Fig. 1 ). The top 5 sources of the citations are Clin Exp Allergy (n = 136), J Allergy Clin Immunol (n = 119), Allergy (n = 81), Pediatr Allergy Immunol (n = 69), and PLOS One (n = 44). The top 5 citing authors are leading experts in pediatrics and allergology Erika von Mutius (Munich, Germany, number of publications with the index citation = 30), Erika Isolauri (Turku, Finland, n = 27), Patrick G Holt (Subiaco, Australia, n = 25), David P. Strachan (London, UK, n = 23), and Bengt Björksten (Stockholm, Sweden, n = 22). The U.S. is the leading country in terms of citation activity with 809 related documents, followed by the UK (n = 494), Germany (n = 314), Australia (n = 211), and the Netherlands (n = 177). The largest proportion of citing documents are articles (n = 1,726, 54%), followed by reviews (n = 950, 29.7%), and book chapters (n = 213, 6.7%). The main subject areas of the citing items are medicine (n = 2,581, 51.7%), immunology and microbiology (n = 1,179, 23.6%), and biochemistry, genetics and molecular biology (n = 415, 8.3%).

Interestingly, a recent analysis of 111 publications related to Strachan's hygiene hypothesis, stating that the lack of exposure to infections in early life increases the risk of rhinitis, revealed a selection bias of 5,551 citations on Web of Science. 37 The articles supportive of the hypothesis were cited more than nonsupportive ones (odds ratio adjusted for study design, 2.2; 95% confidence interval, 1.6–3.1). A similar conclusion pointing to a citation bias distorting bibliometrics of hypotheses was reached by an earlier analysis of a citation network linked to the idea that β-amyloid, which is involved in the pathogenesis of Alzheimer disease, is produced by skeletal muscle of patients with inclusion body myositis. 38 The results of both studies are in line with the notion that ‘positive’ citations are more frequent in the field of biomedicine than ‘negative’ ones, and that citations to articles with proven hypotheses are too common. 39

Social media channels are playing an increasingly active role in the generation and evaluation of scientific hypotheses. In fact, publicly discussing research questions on platforms of news outlets, such as Reddit, may shape hypotheses on health-related issues of global importance, such as obesity. 40 Analyzing Twitter comments, researchers may reveal both potentially valuable ideas and unfounded claims that surround groundbreaking research ideas. 41 Social media activities, however, are unevenly distributed across different research topics, journals and countries, and these are not always objective professional reflections of the breakthroughs in science. 2 , 42

Scientific hypotheses are essential for progress in science and advances in healthcare. Innovative ideas should be based on a critical overview of related scientific facts and evidence-based data, often overlooked by others. To generate realistic hypothetical theories, the authors should comprehensively analyze the literature and suggest relevant and ethically sound design for future studies. They should also consider their hypotheses in the context of research and publication ethics norms acceptable for their target journals. The journal editors aiming to diversify their portfolio by maintaining and introducing hypotheses section are in a position to upgrade guidelines for related articles by pointing to general and specific analyses of the subject, preferred study designs to test hypotheses, and ethical implications. The latter is closely related to specifics of hypotheses. For example, editorial recommendations to outline benefits and risks of a new laboratory test or therapy may result in a more balanced article and minimize associated risks afterwards.

Not all scientific hypotheses have immediate positive effects. Some, if not most, are never tested in properly designed research studies and never cited in credible and indexed publication outlets. Hypotheses in specialized scientific fields, particularly those hardly understandable for nonexperts, lose their attractiveness for increasingly interdisciplinary audience. The authors' honest analysis of the benefits and limitations of their hypotheses and concerted efforts of all stakeholders in science communication to initiate public discussion on widely visible platforms and social media may reveal rational points and caveats of the new ideas.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Gasparyan AY, Yessirkepov M, Kitas GD.
• Methodology: Gasparyan AY, Mukanova U, Ayvazyan L.
• Writing - original draft: Gasparyan AY, Ayvazyan L, Yessirkepov M.
• Writing - review & editing: Gasparyan AY, Yessirkepov M, Mukanova U, Kitas GD.

Research Hypothesis: Elements, Format, Types

When a proposition is formulated for empirical testing, we call it a hypothesis. Almost all studies begin with one or more hypotheses.

Let’s Understand Research Hypothesis.

What is a hypothesis.

A hypothesis, specifically a research hypothesis, is formulated to predict an assumed relationship between two or more variables of interest.

If we reasonably guess that a relationship exists between the variables of interest, we first state it as a hypothesis and then test it in the field.

Hypotheses are stated in terms of the particular dependent and independent variables that are going to be used in the study.

Research Hypothesis Definition

A research hypothesis is a conjectural statement, a logical supposition, a reasonable guess, and an educated prediction about the nature of the relationship between two or more variables that we expect to happen in our study.

Unless you are creating an exploratory study, your hypothesis should always explain what you expect to happen during your experiment or research.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the research aims to determine whether this guess is right or wrong.

When experimenting, researchers might explore different factors to determine which ones might contribute to the outcome.

In many cases, researchers may find that the results of an experiment do not support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

Elements of a Good Hypothesis

Regardless of the type of hypothesis, the goal of a good hypothesis is to help explain the focus and direction of the experiment or research. As such, a good hypothesis will

• State the purpose of the research.
• Identify which variables are to be used.

A good hypothesis;

• Needs to be logical.
• Must be precise in language.
• It should be testable with research or experimentation.

A hypothesis is usually written in a form where it proposes that if something is done, then something will occur.

Finally, when you are trying to come up with a good hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on any previous research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research on your topic.

Once you have completed a literature review, start thinking of potential questions you still have. Pay attention to the discussion section in the journal articles you read. Many authors will suggest questions that still need to be explored.

Basic Format of a Good Hypothesis

A hypothesis often follows a basic format of “If {this happens}, then {this will happen}.” One way to structure your hypothesis is to describe what will happen to the dependent variable if you change the independent variable.

The basic format might be:

“If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}.”

A few examples:

• Students who eat breakfast will perform better on a math test than students who do not eat breakfast.
• Students who experience test anxiety before an exam get higher scores than students who do not experience test anxiety.
• Drivers who talk on their mobile phones while driving will be more likely to make errors when driving than those who do not talk on the phone.
• People with high exposure to ultraviolet light will have a higher frequency of skin cancer than those who do not have such exposure.

Look at the last example.

Here is the independent variable (exposure to ultraviolet light)) is specified, and the dependent variable (skin cancer) is also specified.

Notice also that this research hypothesis specifies a direction in that it predicts that people exposed to ultraviolet light will have a higher risk of cancer.

This is not always the case. Research hypotheses can also specify a difference without saying which group will be better or higher than the other.

For example, one might formulate a hypothesis of the type: ‘Religion does not make any significant difference in the performance of cultural activities.’

In general, however, it is considered a better hypothesis if you can specify a direction.

Research hypotheses serve several important functions. The most important one is to direct and guide the research.

A few of the other functions of the research hypothesis are enumerated below:

• A research hypothesis indicates the major independent variables to be included in the study;
• A research hypothesis suggests the type of data that must be collected and the type of analysis that must be conducted to measure the relationship;
• A research hypothesis identifies facts that are relevant and that are not;
• A research hypothesis suggests the type of research design to be employed.

Types of Research Hypothesis

Two types of research hypotheses are;

• Descriptive hypothesis.
• Relational hypothesis.

Descriptive Hypotheses

Descriptive hypotheses are propositions that typically state some variables’ existence, size, form, or distribution.

These hypotheses are formulated in the form of statements in which we assign variables to cases.

For example,

• The prevalence of contraceptive use among currently married women in India exceeds 60%.

In this example, the case is ‘currently married women,’ and the variable is ‘prevalence of contraceptives.’ As a second example,

• The public universities are currently experiencing budget difficulties.

Here,’ public universities’ is the case, and ‘budget difficulties’ is the variable.

• The National Board of Revenue claims that over 15% of potential taxpayers falsify in their income tax returns.
• At most, 75% of the pre-school children in community A have a protein-deficient diet.
• The average sales in a superstore exceed taka 25 lac per month.
• Smoking increases the risk of lung cancer.
• The average longevity of women is higher among females than among males.
• Gainfully employed women tend to have lower than average fertility.
• Women with child loss experience will have higher fertility than those who do not have such experiences.

All examples of descriptive hypotheses.

It is important to note that the Descriptive hypothesis does not always have variables that can be designated as independent or dependent.

Relational Hypotheses

Relational hypotheses, on the other hand, are statements that describe the relationship between variables concerning some cases.

• Communities with many modern facilities will have a higher rate of contraception than communities with few modern facilities.

In this instance, the case is ‘communities,’ and the variables are ‘rate of contraception’ and ‘modern facilities.’

Similarly, “People who use chewing tobacco have a higher risk of oral carcinoma than people who have never used chewing tobacco” is a relational hypothesis.

A relational hypothesis is again of two types: correlational hypothesis and the causal hypothesis.

A correlational hypothesis states that variables occur in some predictable relationships without implying that one variable causes the other to change or take on different values.

Here is an example of a co-relational hypothesis:

• Males are more efficient than their female counterparts in typing.

In making such a statement, we do not claim that sex (male-female) as a variable influences the other variable,’ typing efficiency’ (less efficient-more efficient). Here is one more example of a correlational hypothesis:

• Saving habit is more pronounced among Christians than the people of other religions.

Once again, religion is not believed to be a factor in saving habits, although a positive relationship has been observed.

Look at the following example:

• The participation of women in household decision making increases with age, their level of education, and the number of surviving children.

Here too, women’s education, several surviving children, or education does not guarantee their decision-making autonomy.

With causal hypotheses (also called explanatory hypotheses), on the other hand, there is an implication that a change in one variable causes a change or leads to an effect on the other variable.

A causal variable is typically called an independent variable, and the other is the dependent variable. It is important to note that the term “cause” roughly means “help make happen.” So, the independent variable need not be the sole reason for the existence of or change in the dependent variable. Here are some examples of causal hypotheses:

• An increase in family income leads to an increase in the income saved.
• Exposure of mothers to mass media increases their knowledge of malnutrition among their children.
• An offer of a discount in a department store enhances the sales volume.
• Chewing tobacco increases the risk of oral carcinoma.
• Goat farming contributes to poverty alleviation of rural people.
• The utilization of child welfare clinics is the lowest in those clinics in which the clinic personnel are poorly motivated to provide preventive services.
• An increase in bank interest rate encourages the customers for increased savings.

In the above example, we have ample reasons to believe that one variable (family income and savings, misuse of credit, and farm size) has a bearing on the other variable.

We cite two more examples to illustrate the hypothesis, general objective, ultimate objective, and a few specific objectives.

General objective:

• To compare the complications of acceptors of laparoscopic sterilization and mini-laparotomy among American women.

Research hypothesis:

• The risk of complications is higher in the mini-laparotomy method of sterilization than in laparoscopic sterilization.

Specific objectives:

• To assess the complications of laparoscopic sterilization and mini-laparotomy.
• To assess service providers’ knowledge and perception regarding the complications, preferences, and convenience of the two methods.

Ultimate Objectives:

• To introduce and popularize the laparoscopic female sterilization method in the National Family Planning Program to reduce the rapid population growth rate.

In a study designed to examine the living and working conditions of the overseas migrant workers from India and the pattern of remittances from overseas migrant workers, the general objective, specific objectives, and the ultimate objective were formulated as follows:

• To examine the living and working conditions of the overseas migrant workers from India.”
• Characteristics of migrant workers by significant migration channels;
• Countries of destination;
• The occupational skill of the workers;
• Pattern and procedures of remittances;
• Impact of remittances on government revenue;
• Better utilization of remittances.

Ultimate objective:

• To suggest ways and means to minimize the differences in the policy adopted by the public and private sectors in their recruitment process in the interest of the workers;
• To ascertain the possible exploitation of the workers by the private agencies and suggest remedies for such exploitation.
• Private agencies, in most cases, exploit migrant workers.

What are the elements of a good hypothesis?

A good hypothesis should state the purpose of the research, identify which variables are to be used, be logical, precise in language, and be testable with research or experimentation.

How is a hypothesis typically structured?

A hypothesis often follows a basic format of “If {this happens}, then {this will happen}.” It proposes that if something is done, then a specific outcome will occur.

What is a Descriptive hypothesis?

Descriptive hypotheses are propositions that typically state some variables’ existence, size, form, or distribution. They are formulated in the form of statements in which variables are assigned to cases.

What distinguishes a Relational hypothesis?

Relational hypotheses describe the relationship between variables concerning some cases. They can be correlational, where variables occur in a predictable relationship without implying causation, or causal, where a change in one variable causes a change in another.

What is the difference between a correlational hypothesis and a causal hypothesis?

A correlational hypothesis states that variables occur in some predictable relationships without implying that one variable causes the other to change. A causal hypothesis, on the other hand, implies that a change in one variable causes a change or leads to an effect on the other variable.

What are the two main types of research hypotheses?

The two main types of research hypotheses are Descriptive hypothesis and Relational hypothesis

What is a hypothesis in the context of academic research?

A hypothesis is a statement about an expected relationship between variables or an explanation of an occurrence that is clear, specific, and testable.

How does a research hypothesis differ from a general hypothesis?

A research hypothesis is more specific and clear about what’s being assessed and the expected outcome. It must also be testable, meaning there should be a way to prove or disprove it.

What are the essential attributes of a good research hypothesis?

A good research hypothesis should have specificity, clarity, and testability.

Why is testability crucial for a research hypothesis?

Testability ensures that empirical research can prove or disproven the hypothesis. If a statement isn’t testable, it doesn’t qualify as a research hypothesis.

What is the null hypothesis?

The null hypothesis is the counter-proposal to the original hypothesis. It predicts that there is no relationship between the variables in question.

How can one ensure that a hypothesis is clear and specific?

A hypothesis should clearly identify the variables involved, the parties involved, and the expected relationship type, leaving no ambiguity about its intent or meaning.

Why is it essential to avoid value judgments in a research hypothesis?

Value judgments are subjective and not appropriate for a hypothesis. A research hypothesis should strive to be objective, avoiding personal opinions.

What is the basic definition of a hypothesis in research?

A research hypothesis is a statement about an expected relationship between variables, or an explanation of an occurrence, that is clear, specific, and testable.

While a general hypothesis is an idea or explanation based on known facts but not yet proven, a research hypothesis is a clear, specific, and testable statement about the expected outcome of a study.

What are the essential characteristics of a good research hypothesis?

A good research hypothesis should possess specificity, clarity, and testability. It should clearly define what’s being assessed and the expected outcome, and it must be possible to prove or disprove the statement through experimentation.

How can one ensure that a hypothesis is testable?

A hypothesis is testable if there’s a possibility to prove both its truth and falsity. The results of the hypothesis should be reproducible, and it should be specific enough to allow for clear testing procedures.

What is the difference between a null hypothesis and an alternative hypothesis?

The null hypothesis proposes that no statistical significance exists in a set of observations, suggesting any differences are due to chance alone. The alternative hypothesis, on the other hand, predicts a relationship between the variables of the study and states that the results are significant to the research topic.

How should one formulate an effective research hypothesis?

To formulate an effective research hypothesis, one should state the problem clearly, use an ‘if-then’ statement structure, define the variables as dependent or independent, and scrutinize the hypothesis to ensure it meets the criteria of specificity, clarity, and testability.

What are some types of hypotheses in research?

Types of hypotheses include simple, complex, directional, non-directional, associative and causal, empirical, and statistical hypotheses. Each type serves a specific purpose and is used based on the nature of the research question or problem.

As you now covered research hypothesis; check out explore complete guideline on research and research methodology concepts .

• Hypothesis Testing: Definition, Examples
• Computer-Assisted Personal Interviewing (CAPI)
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Hypothesis Testing | A Step-by-Step Guide with Easy Examples

Published on November 8, 2019 by Rebecca Bevans . Revised on June 22, 2023.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics . It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories.

There are 5 main steps in hypothesis testing:

• State your research hypothesis as a null hypothesis and alternate hypothesis (H o ) and (H a  or H 1 ).
• Collect data in a way designed to test the hypothesis.
• Perform an appropriate statistical test .
• Decide whether to reject or fail to reject your null hypothesis.
• Present the findings in your results and discussion section.

Though the specific details might vary, the procedure you will use when testing a hypothesis will always follow some version of these steps.

Table of contents

Step 1: state your null and alternate hypothesis, step 2: collect data, step 3: perform a statistical test, step 4: decide whether to reject or fail to reject your null hypothesis, step 5: present your findings, other interesting articles, frequently asked questions about hypothesis testing.

After developing your initial research hypothesis (the prediction that you want to investigate), it is important to restate it as a null (H o ) and alternate (H a ) hypothesis so that you can test it mathematically.

The alternate hypothesis is usually your initial hypothesis that predicts a relationship between variables. The null hypothesis is a prediction of no relationship between the variables you are interested in.

• H 0 : Men are, on average, not taller than women. H a : Men are, on average, taller than women.

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For a statistical test to be valid , it is important to perform sampling and collect data in a way that is designed to test your hypothesis. If your data are not representative, then you cannot make statistical inferences about the population you are interested in.

There are a variety of statistical tests available, but they are all based on the comparison of within-group variance (how spread out the data is within a category) versus between-group variance (how different the categories are from one another).

If the between-group variance is large enough that there is little or no overlap between groups, then your statistical test will reflect that by showing a low p -value . This means it is unlikely that the differences between these groups came about by chance.

Alternatively, if there is high within-group variance and low between-group variance, then your statistical test will reflect that with a high p -value. This means it is likely that any difference you measure between groups is due to chance.

Your choice of statistical test will be based on the type of variables and the level of measurement of your collected data .

• an estimate of the difference in average height between the two groups.
• a p -value showing how likely you are to see this difference if the null hypothesis of no difference is true.

Based on the outcome of your statistical test, you will have to decide whether to reject or fail to reject your null hypothesis.

In most cases you will use the p -value generated by your statistical test to guide your decision. And in most cases, your predetermined level of significance for rejecting the null hypothesis will be 0.05 – that is, when there is a less than 5% chance that you would see these results if the null hypothesis were true.

In some cases, researchers choose a more conservative level of significance, such as 0.01 (1%). This minimizes the risk of incorrectly rejecting the null hypothesis ( Type I error ).

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The results of hypothesis testing will be presented in the results and discussion sections of your research paper , dissertation or thesis .

In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p -value). In the discussion , you can discuss whether your initial hypothesis was supported by your results or not.

In the formal language of hypothesis testing, we talk about rejecting or failing to reject the null hypothesis. You will probably be asked to do this in your statistics assignments.

However, when presenting research results in academic papers we rarely talk this way. Instead, we go back to our alternate hypothesis (in this case, the hypothesis that men are on average taller than women) and state whether the result of our test did or did not support the alternate hypothesis.

If your null hypothesis was rejected, this result is interpreted as “supported the alternate hypothesis.”

These are superficial differences; you can see that they mean the same thing.

You might notice that we don’t say that we reject or fail to reject the alternate hypothesis . This is because hypothesis testing is not designed to prove or disprove anything. It is only designed to test whether a pattern we measure could have arisen spuriously, or by chance.

If we reject the null hypothesis based on our research (i.e., we find that it is unlikely that the pattern arose by chance), then we can say our test lends support to our hypothesis . But if the pattern does not pass our decision rule, meaning that it could have arisen by chance, then we say the test is inconsistent with our hypothesis .

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

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Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

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• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1: ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2: Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise more complex constructs.

Step 3: Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

Step 4: Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

Step 5: Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

Step 6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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Research Hypothesis

A research hypothesis (H 1 ) is the statement created by researchers when they speculate upon the outcome of a research or experiment.

This article is a part of the guide:

• Null Hypothesis
• Defining a Research Problem
• Selecting Method
• Test Hypothesis

Browse Full Outline

• 1 Scientific Method
• 2.1.1 Null Hypothesis
• 2.1.2 Research Hypothesis
• 2.2 Prediction
• 2.3 Conceptual Variable
• 3.1 Operationalization
• 3.2 Selecting Method
• 3.3 Measurements
• 3.4 Scientific Observation
• 4.1 Empirical Evidence
• 5.1 Generalization
• 5.2 Errors in Conclusion

Every true experimental design must have this statement at the core of its structure, as the ultimate aim of any experiment.

The hypothesis is generated via a number of means, but is usually the result of a process of inductive reasoning where observations lead to the formation of a theory. Scientists then use a large battery of deductive methods to arrive at a hypothesis that is testable , falsifiable and realistic.

The precursor to a hypothesis is a research problem , usually framed as a question . It might ask what, or why, something is happening.

For example, we might wonder why the stocks of cod in the North Atlantic are declining. The problem question might be ‘Why are the numbers of Cod in the North Atlantic declining?’

This is too broad as a statement and is not testable by any reasonable scientific means. It is merely a tentative question arising from literature reviews and intuition. Many people would think that instinct and intuition are unscientific, but many of the greatest scientific leaps were a result of ‘hunches’.

The research hypothesis is a paring down of the problem into something testable and falsifiable. In the above example, a researcher might speculate that the decline in the fish stocks is due to prolonged over fishing. Scientists must generate a realistic and testable hypothesis around which they can build the experiment.

This might be a question, a statement or an ‘If/Or’ statement. Some examples could be:

Over-fishing affects the stocks of cod.

If over-fishing is causing a decline in the numbers of Cod, reducing the amount of trawlers will increase cod stocks.

These are acceptable statements and they all give the researcher a focus for constructing a research experiment. The last example formalizes things and uses an ‘If’ statement, measuring the effect that manipulating one variable has upon another. Though the other one is perfectly acceptable, an ideal research hypothesis should contain a prediction, which is why the more formal ones are favored.

A scientist who becomes fixated on proving a research hypothesis loses their impartiality and credibility. Statistical tests often uncover trends, but rarely give a clear-cut answer, with other factors often affecting the outcome and influencing the results .

Whilst gut instinct and logic tells us that fish stocks are affected by over fishing, it is not necessarily true and the researcher must consider that outcome. Perhaps environmental factors or pollution are causal effects influencing fish stocks.

A hypothesis must be testable , taking into account current knowledge and techniques, and be realistic. If the researcher does not have a multi-million dollar budget then there is no point in generating complicated hypotheses. A hypothesis must be verifiable by statistical and analytical means, to allow a verification or falsification .

In fact, a hypothesis is never proved, and it is better practice to use the terms ‘supported’ or ‘verified’. This means that the research showed that the evidence supported the hypothesis and further research is built upon that.

Your hypothesis should... Be written in clear, concise language Have both an independent and dependent variable Be falsifiable – is it possible to prove or disprove the statement? Make a prediction or speculate on an outcome Be practicable – can you measure the variables in question? Hypothesize about a proposed relationship between two variables, or an intervention into this relationship

A research hypothesis , which stands the test of time, eventually becomes a theory, such as Einstein’s General Relativity. Even then, as with Newton’s Laws, they can still be falsified or adapted.

The research hypothesis is often also callen H 1 and opposes the current view, called the null hypothesis (H 0 ).

Consider the following hypotheses. Are they likely to lead to sound research and conclusions, and if not, how could they be improved?

Adding mica to a plastic compound will decrease its viscosity.

Those who drink a cup of green tea daily experience enhanced wellness.

Prolonged staring into solar eclipses confers extrasensory powers.

A decline in family values is lowering the marriage rate.

Children with insecure attachment style are more likely to engage in political dissent as adults.

Sub-Saharan Africa experiences more deaths due to Tuberculosis because the HIV rate is higher there.

This is an ideal hypothesis statement. It is well-phrased, clear, falsifiable and merely by reading it, one gets an idea of the kind of research design it would inspire.

This hypothesis is less clear, and the problem is with the dependent variable. Cups of green tea can be easily quantified, but how will the researchers measure “wellness”? A better hypothesis might be: those who drink a cup of green tea daily display lower levels of inflammatory markers in the blood.

Though this hypothesis looks a little ridiculous, it is actually quite simple, falsifiable and easy to operationalize. The obvious problem is that scientific research seldom occupies itself with supernatural phenomenon and worse, putting this research into action will likely cause damage to its participants. When it comes to hypotheses, not all questions need to be answered!

Provided the researchers have a solid method for quantifying “family values” this hypothesis is not too bad. However, scientists should always be alert for their own possible biases creeping into research, and this can occur right from the start. Normative topics with moral elements are seldom neutral. A better hypothesis will remove any contentious, subjective elements. A better hypothesis: decrease in total discretionary income corresponds to lower marriage rate in people 20 – 30 years of age.

This hypothesis may yield very interesting and useful results, but practically, how will the researchers gather the data? Even if research is logically sound, it may not be feasible in the real world. A researcher might instead choose to make a more manageable hypothesis: high scores on an insecure attachment style questionnaire will correlate with high scores on a political dissention questionnaire.

Though complex, this is a good hypothesis. It is falsifiable, has clearly identified variables and can be supported or rejected using the right statistical methods.

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• Published: 22 August 2023

Dynamic light filtering over dermal opsin as a sensory feedback system in fish color change

• Lorian E. Schweikert   ORCID: orcid.org/0000-0002-8639-3612 1 , 2   nAff8 ,
• Laura E. Bagge   ORCID: orcid.org/0000-0001-8803-3955 3 , 4 ,
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• Jacob R. Bolin 5 ,
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Nature Communications volume  14 , Article number:  4642 ( 2023 ) Cite this article

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• Animal behaviour
• Animal physiology
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Dynamic color change has evolved multiple times, with a physiological basis that has been repeatedly linked to dermal photoreception via the study of excised skin preparations. Despite the widespread prevalence of dermal photoreception, both its physiology and its function in regulating color change remain poorly understood. By examining the morphology, physiology, and optics of dermal photoreception in hogfish ( Lachnolaimus maximus ), we describe a cellular mechanism in which chromatophore pigment activity (i.e., dispersion and aggregation) alters the transmitted light striking SWS1 receptors in the skin. When dispersed, chromatophore pigment selectively absorbs the short-wavelength light required to activate the skin’s SWS1 opsin, which we localized to a morphologically specialized population of putative dermal photoreceptors. As SWS1 is nested beneath chromatophores and thus subject to light changes from pigment activity, one possible function of dermal photoreception in hogfish is to monitor chromatophores to detect information about color change performance. This framework of sensory feedback provides insight into the significance of dermal photoreception among color-changing animals.

Introduction

Dynamic color change is a rapid, variable, and context-dependent behavior with shared physiological characteristics among diverse animals 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 . Supporting processes such as thermoregulation, sexual selection, and camouflage 6 , 7 , 8 , this behavior is employed among animals in habitats as diverse as desert mountaintops and the deep sea 1 . Relative to morphological color change, occurring over days to months 3 , 4 , dynamic or physiological color change can occur within minutes or less 5 , 8 . These rate differences are based on regulation mechanisms, with the most rapid forms of color change due to neuronal rather than hormonal primary inputs of control 5 , 8 , 9 , 10 . Animals capable of dynamic color change include cephalopods 11 , amphibians 7 , reptiles 1 , fish 7 and other ectotherms 1 , all achieving this feat using specialized skin cells called chromatophores 1 , 8 , 12 . Several major types of chromatophores exist, changing color through the intracellular reorganization of pigment granules, crystals, or reflective platelets 8 , 12 . For the pigmentary chromatophores of vertebrates, pigment organelles are reversibly aggregated and dispersed within these cells by molecular motors over an extensive microtubule network 5 , 8 . As a result, incident light strikes either the underlying (typically white) tissue or the exposed pigment (Fig.  1 ), which gives the skin its light or colored appearance, respectively 8 .

Hogfish are capable of undergoing rapid changes in skin coloration between at least three chromatic appearances ( a ). Color change is achieved by aggregating ( b ) and dispersing ( c ) pigment granules in chromatophores to generate light and dark cell appearances, respectively. Scale bars equal 100 μm. Panel ( a ) is modified from Schweikert and colleagues (2018) 20 .

Despite diverse evolutionary histories, another commonality among color-changing animals is the intrinsic photosensitivity of their skin and the predicted coupling of this sense to their ability to change color 13 . Evidence of dermal photoreception, such as for cephalopods and fish, includes phototransduction proteins (e.g., opsins) of the retina co-occurring in the skin 14 , 15 , 16 and incident light on excised skin patches inducing a color-change response 16 , 17 . In live animals, however, support is lacking for the direct capacity of dermal photoreception to regulate color change, leaving the function of dermal photoreception in dynamic color change unknown. One hypothesis states that dermal photosensitivity may allow for the regulation of color change independently of inputs from ocular vision 13 . The putative benefits of this strategy include reduced demands of sensory processing for color change or the possibility of light detection outside of the field of view or spectral sensitivity of the eyes 13 . Another hypothesis states that dermal photoreception may locally affect color change within a broader system of control that may coordinate with the central nervous system 15 . This possibility could allow monitoring of chromatophore color change within a feedback system, not unlike the intrinsic photosensitivity of light organs in certain mesopelagic shrimps and the bobtail squid ( Euprymna scolopes ) thought to help regulate outputs of bioluminescence 18 , 19 . For color change, however, evidence is lacking to support these possibilities, leading to questions about how and why dermal photoreception and color change may be linked.

Our understanding of dermal photoreception in color change is largely based on studies of gene expression (e.g., refs. 20 , 21 ), which have indicated that opsins and other phototransduction components expressed in the skin have varying similarities to the phototransduction components of the retina. In color-changing vertebrates, including the hogfish ( Lachnolaimus maximus ), dermal photoreception may incorporate several opsins types and mediate phototransduction via a cAMP-dependent cascade 20 , 22 , 23 , 24 . Both ‘non-visual’ type (e.g., melanopsin) and ‘visual’ type opsins (e.g., RH1, SWS1) have been identified and implicated in chromatophore activation among vertebrates 16 , 25 , 26 , with a particular role evident for SWS1 (short-wavelength-sensitive-1) opsin. In the hogfish ( Lachnolaimus maximus ) 20 , Moorish gecko ( Tarentola mauritanica ) 14 , summer flounder ( Paralichthys dentatus ) 26 , and Nile tilapia ( Oreochromis niloticus ) 16 , regardless of expressing a single or multiple dermal opsins, SWS1 opsin has been consistently identified in chromatophore-containing skin. These studies have shown that, relative to other opsins, SWS1 opsin can have the highest expression levels in the skin (in hogfish and others) 16 , 20 and that SWS1 activation, at least within in vitro skin preparations of the Nile tilapia, can directly mediate chromatophore responses to light 27 . Though these studies evidence a relationship between dermal opsins and dynamic color change 13 , we still lack knowledge about the functional organization of this system—perhaps critical for understanding the significance of dermal photoreception in living animals.

In addition to studies of gene expression, those examining the arrangement of dermal opsins relative to other components in skin provide key insights into the potential functions of dermal photoreception. We know from a limited number of studies that visual-type opsins can be expressed either within chromatophores or more diffusely, in surrounding cell types 15 , 16 . In the Nile tilapia ( Oreochromis niloticus ), such opsins have been localized to chromatophores using single-cell reverse transcription polymerase chain reaction (RT-PCR) 16 . Protein localization of opsin and inferred function of dermal photoreception, however, is better described for certain invertebrates. In the inshore squid ( Doryteuthis pealeii ), rhodopsin has been localized to several cell types comprising chromatophore organs: the pigment cells, radial muscle fibers, and sheath cells, which may individually or synergistically respond to incident light 15 . In the ophiuroid, Ophiocoma wendtii , other dermal opsins (previously implicated in echinoderm vision) 28 , 29 were localized to putative photoreceptor cells found between chromatophores, which may serve as screening pigments that confer directionality to dermal photoreception 30 . Despite the widespread prevalence and shared physiological characteristics of dermal photoreception among diverse vertebrates, a similar study examining the optical organization of opsin within skin is lacking for any vertebrate system. Our goal was to conduct such a study, examining design principles of dermal photoreception to better understand the functional significance of this sense in color-changing skin.

The subject of our study, the hogfish (Perciformes: Labridae; Fig.  1 ), is the largest and most economically valuable wrasse of the western North Atlantic Ocean 31 . Its distinguishing features include hermaphroditic and haremic reproductive strategies 32 , which may incorporate color change as a form of social signaling in addition to background-matching camouflage 33 . Post-settlement, both males and females are capable of dynamic color change 33 (within one second or less, Schweikert pers. observation) between at least three chromatic morphs 34 : uniform white, uniform reddish-brown, and a mottled coloration (Fig.  1 ). Studies are lacking however, on the underlying physiology of hogfish color change.

In this work, we used approaches in immunohistochemistry, confocal and transmission electron microscopy, sequenced-based spectral sensitivity estimation, and microspectrophotometry (MSP) to investigate the physical and optical relationship between SWS1 opsin and chromatophores in hogfish skin. Our results show that SWS1-opsin expression is localized to a morphologically specialized population of cells existing beneath chromatophores and that chromatophore pigment selectively absorbs the wavelengths of SWS1 peak spectral sensitivity. As SWS1 receptors appear subject to light changes from pigment activity (aggregation and dispersion), the predicted function of dermal photoreception in hogfish is to detect these shifts in chromatophore pigment in order to obtain sensory feedback about color change performance.

Chromatophores and color change

Three types of chromatophores with differing pigments were identified by light microscopy of en face preparations of hogfish skin: black melanophores, red erythrophores, and yellow xanthophores (Fig.  1 ). Chromatophores were arranged in a horizontal array, existing within a thin dermal tissue layer found on top of the fish’s scales. The light white, dark red, and mottled appearances of hogfish skin (Fig.  1a ) are achieved by the aggregation and dispersion (Fig.  1b, c ) of chromatophore pigment, respectively. Observation of white reflectivity and blue iridescence in the aggregated pigment preparations (Fig.  1b ) suggests the presence of leucophores or iridophores in hogfish skin; however, the presence of these chromatophore types has yet to be observed in our analyses by transmission electron microscopy.

SWS1 immunofluorescence

We performed anti-opsin immunofluorescence to localize SWS1 expression in hogfish skin (Fig.  2 ). Skin cross sections revealed a dense layer of epidermal cell nuclei overlaying chromatophores and surrounding cell types on top of the fish’s scales. SWS1-immunolabeling was localized directly beneath the pigment of chromatophores, not in a continuous layer in skin, but in discrete positions found beneath individual, contiguous chromatophores (Fig.  2a–c ). Using differential interference contrast (DIC) microscopy, SWS1 expression was indicated beneath melanophores (Fig.  2c, d ); however, the low optical density of erythrophores and xanthophores made it difficult to identify these cells in micrographs by pigment color. Thus, SWS1 expression beneath these chromatophore types was inferred from the adjacent positioning of these cells as shown by light microscopy (Fig.  1 ) and transmission electron microscopy (Fig.  3 ). Specificity and sensitivity of the SWS1-opsin antibody were validated by the lack of expression in control preparations and positive labeling of a cone photoreceptor population in cross section of hogfish retina (Fig.  2e, f ).

In skin cross section, immunolabeling of SWS1 opsin (green) is found beneath the chromatophore layer (white arrows; a ). SWS1 expression is shown beneath melanophores (black-filled triangles) and erythrophores (white triangles; b – d ). In control preparations without primary antibody, immunolabeling of SWS1 opsin in skin cross section is absent ( e ). In retinal cross section, SWS1 immunolabeling (green) of cone photoreceptor outer segments serves as a positive control ( f ). Rod photoreceptor outer segments are indicated by anti-rhodopsin immunolabeling (red), and all cell nuclei are stained with DAPI (blue). All scale bars equal 10 μm.

A reticulated membrane structure is contained within a population of cells located beneath chromatophores ( a , b ). These membrane-containing cells are shown beneath a melanophore (black-filled triangle) and an erythrophore (white triangle; a , b ). c = collagen fibers, rm = reticulated membrane structure, n = nuclei. Scale bars equal 2 μm.

Skin ultrastructure and SWS1 immunogold labeling

To assess the subcellular ultrastructure supporting SWS1-opsin expression, we conducted transmission electron microscopy (TEM) of ultrathin cross sections of hogfish skin (Fig.  3 ). Electron micrographs revealed expected elements of fish skin morphology, including the presence of orthogonal collagen lamina found immediately above the chromatophore cells, which showed characteristic differences in the electron density of their pigment 9 (Fig.  3b ). Further, micrographs revealed a distinct population of an unknown cell type existing directly beneath chromatophores (Fig.  3 ). These cells were densely filled with a reticulated membrane, bearing a morphology unlike that known for cell organelles. Section orientation did not change this observation, as the reticulated membrane had the same morphology in both cross-sectional and en face planes (Fig.  3 and Supplementary Fig.  1 ). As was shown for SWS1 immunofluorescence, the membrane-filled cells were found beneath each adjacent chromatophore cell (Fig.  3b and Supplementary Fig.  2 ). The two were always coupled, with no instances of membrane-filled cells lacking an overlying chromatophore. Notably, the membrane-filled cells did not appear wider or offset from chromatophores; rather, the margins of both cell types were vertically aligned (Fig.  3b and Supplementary Fig.  2 ). The SWS1 immunofluorescence (Fig.  2 ) was, therefore, colocalized to the reticulated membrane structure, suggesting expression of SWS1 opsin within these cells and their function as putative photoreceptors. To further explore this possibility, we conducted anti-SWS1 opsin immunogold labeling of skin cross sections and found positive immunoreactivity in the reticulated membrane structure of these cells (Fig.  4a, b ). Though the ultrastructural resolution was limited due to conflict between the tissue treatments that preserve ultrastructure and those that permit antibody binding, positive SWS1 immunoreactivity was observed in the underlying cells but not within control preparations (Fig.  4c, d ).

Immunogold labeling of SWS1 opsin (black arrows) is shown within the cells beneath melanophores ( a , b ). In control preparations without primary antibodies, immunogold labeling of SWS1 opsin is absent ( c , d ). Black-filled triangles = melanophores, rm = reticulated membrane structure. Scale bars equal 1 μm in ( a – c ) and 2 μm in ( d ).

SWS1 spectral sensitivity estimate

To begin exploring the optical effects of chromatophores overlying the SWS1 receptors, we used a sequence alignment technique to estimate the spectral sensitivity of the hogfish SWS1 opsin. Our goal was to identify the known sequence with the highest homology SWS1 gene to that of hogfish, encoding an SWS1 cone opsin with a previously published wavelength of peak sensitivity (λ max ). Using data from the skin transcriptome reported by Schweikert and colleagues 20 , we assessed the similarity of the hogfish SWS1 opsin to archived genes using the BLASTx feature provided by the National Center for Biotechnology Information (NCBI). From this output, the SWS1 opsin with the highest homology sequence to that of hogfish came from the night aulonocara ( Aulonocara hueseri ; family Cichlidae, Genbank accession AY775100.1 ), which has a known SWS1 λ max of 415 nm 35 . The alignment of these two sequences indicated amino acid residues at known SWS1 spectral tuning sites that were nearly identical between the species (Fig.  5 ) 36 . Of the 13 spectral tuning sites identified 37 , 38 , the only substitutions were S97C and M116V (Fig.  5 ), which from related studies of site-directed mutagenesis, are predicted to confer small effects on SWS1 spectral sensitivity, shifting λ max at most a few nanometers 37 , 38 . Thus, the hogfish SWS1 opsin has an estimated λ max centering on 415 nm, falling within the known λ max range for all vertebrate SWS1 opsins (i.e., 360–440 nm) 38 .

The deduced amino acid sequences of the retinal SWS1 opsin from A. hueseri (Genbank accession AY775100.1 ) and dermal SWS1 opsin from L. maximus (Genbank Accession: PRJNA386691 ) are shown. The known spectral tuning sites of vertebrate SWS1 opsins are indicated by the asterisks 37 , 38 , with two amino acid substitutions (red asterisks) identified between the two species, positions S97C and M116V. Percent conservation indicates amino acid similarity.

Chromatophore microspectrophotometry

The alignment of chromatophores over putative photoreceptors (Fig.  3 ) indicates that ambient light must first pass through chromatophores before striking SWS1 opsin in hogfish skin, and thus, we were interested in determining the effects of chromatophore pigment on light transmission using microspectrophotometry (MSP). We passed broad-spectrum white light through the dispersed pigment of each chromatophore type ( n  = 60 cells each for melanophores, erythrophores, and xanthophores) to measure transmittance ranging from 400 to 700 nm wavelengths. Light was passed through unpigmented tissue between chromatophores as a reference, allowing the spectral transmittance of the chromatophore alone to be calculated. For all three chromatophore types, transmittance was positively correlated with wavelength. The mean spectra of erythrophores and xanthophores revealed sharp transitions between regions of low and high transmittance, occurring at roughly 550 nm and 488 nm, respectively (Fig.  6 ). By comparison, melanophores had relatively low transmittance that increased slowly and uniformly. All of the chromatophores types, however, strongly attenuated light over the spectral range of known vertebrate SWS1 opsin sensitivity, and specifically of the predicted hogfish SWS1 sensitivity curve (λ max  = 415 nm) as revealed by visual pigment template fitting, with a ~50%, 85%, and 90% reduction of short-wavelength light transmission shown for xanthophores, erythrophores, and melanophores, respectively (Fig.  6 ).

The mean percent transmission (±s.e.m.) of light wavelengths spanning 400–700 nm is shown for the dispersed pigment of melanophores (black line), erythrophores (red line), and xanthophores (yellow line); n  = 60 per cell type. A fitted template for a vitamin-A-based photoreceptor action spectrum, with a peak wavelength of sensitivity (λ max ) at 415 nm, is indicated by the gray dashed line.

The expression of SWS1 opsin in hogfish skin suggests dermal spectral sensitivity that coincides with the availability of short-wavelength light that predominates their coral reef habitat 32 but spectrally contrasts with the long-wavelength pigmentation of their skin. This gives way to two possible, though not mutually exclusive, functions of dermal photoreception as previously posited for color change 13 , 15 , which are: (1) to monitor extrinsic information about environmental light, or (2) to monitor intrinsic information about skin coloration. The position of SWS1 receptors beneath chromatophores lends support to the latter possibility, providing insights into how and why dermal photoreception is coupled to color change.

Functional organization of dermal photoreception

As opsins are transmembrane proteins 39 , membrane surface area is correlated with sensitivity. In the vertebrate retina, opsin expression can occur within free-floating discs or laminar invaginations of cell membrane as are found in rod and cone photoreceptors, respectively 40 . Based on the localized SWS1-immunolabeling in hogfish skin (Fig.  2 ) and previous studies identifying opsins directly within chromatophores (e.g., ref. 16 ), we expected to see similar modifications of the chromatophore cell membrane. Surprisingly however, we found a distinct and unknown cell type with significant membrane specialization existing beneath the chromatophores, which was both colocalized to and immunogold-labeled as the location of SWS1 expression. Though the undifferentiated morphology of this reticulated membrane is unlike the derived morphology of ciliary photoreceptors, it is not unlike that of cnidarian photoreceptors, for example 41 , and may exist to provide a large surface area for opsin expression. The reason for this high surface area (and putative enhancement in sensitivity) is unknown but may relate to maintaining dermal photosensitivity under dim light levels that hogfish may experience when reaching oceanic depths of 20 to 45 m or more 42 . Similar to retinal photoreceptors, these cells may be morphologically and functionally specialized for light reception in the skin. To our knowledge, specialized photoreceptor cells have not been reported in the skin of vertebrates, and thus, the discovery of this cell opens up avenues of research in comparative photoreceptor physiology. Although these data do not rule out the possibility that other non-visual-type dermal opsins (e.g., melanopsin) are expressed within chromatophores or surrounding cells, these findings provide insights into the function of the most abundant opsin in hogfish skin 20 .

Our findings are in line with those of Sumner-Rooney and colleagues 30 , who found a distinct population of putative photoreceptors between chromatophores in the skin of brittle stars ( Ophiocoma wendtii and O. pumila ) that appear subject to light changes from chromatophore pigment migration 30 . In contrast to our study, they found that photoreceptors exist adjacent to chromatophores, with pigment migration creating separate sampling stations over photoreceptors that might confer coarse spatial vision. In hogfish, the putative photoreceptors were found beneath (not adjacent to) chromatophores, with cell boundaries that were vertically aligned (Fig.  3 and Supplementary Fig.  2 ). The arrangement of this system, with photoreceptors close to one another and directly beneath chromatophores, decreases the likelihood that pigment migration creates separate sampling stations that confer directional vision. This arrangement, however, could allow photoreceptors to detect changes in overlying pigment in order to monitor color change performance—a possibility that is only true if pigment alters the light in a way that is physiologically relevant for SWS1 activation.

Previous site-directed mutagenesis experiments have revealed the key spectral tuning sites that alter SWS1 spectral sensitivity 37 , 38 , and as ciliary opsins are highly conserved, a sequence alignment technique provides a tractable method for λ max estimation. Amino acid substitutions at positions 86, 90, and 93 (relative to a bovine rhodopsin standard) are known to generate the largest changes in SWS1 sensitivity, shifting λ max from ultraviolet to blue light 37 . Here, the alignment of the hogfish SWS1 to that of another teleost fish (the night aulonocara) indicated high homology of spectral tuning sites, with only two substitutions at positions 97 and 116. Though the effects of these exact amino acid substitutions have never been independently studied using site-directed mutagenesis, similar switches, at least for position 116, are reported to shift SWS1 λ max by 0 to −3 nm 38 . Together, these findings provide a reasonable estimate of the hogfish SWS1 λ max at 415 nm, which was fitted to a vitamin A1-based opsin absorbance spectrum that revealed an overall sensitivity range from <350 to 500 nm.

The chromatophore transmission spectra showed that short-wavelength light required to activate the skin’s SWS1 opsin is the same light that is selectively absorbed, and thus suppressed, by the pigment of each chromatophore type (Fig.  3 ). The degree of light attenuation varied between the types according to optical density, with melanophores being the strongest attenuators followed by erythrophores then xanthophores—a finding that is in line with a previous study of chromatophore light transmission in the Japanese Medaka ( Oryzias latipes ) 43 . Their study also showed that each chromatophore type attenuates the ultraviolet portion of the spectrum, following the same trend according to chromatophore optical density 43 .

Functional implications of dermal photoreception

In summary, this system of dermal photoreception in hogfish suggests that dispersion of pigment in chromatophores suppresses short-wavelength irradiation of SWS1 photoreceptors and that aggregation of pigment increases irradiation (and therefore, putative activation) of the SWS1 photoreceptors, making them sensitive to changes in chromatophore color state (Fig.  7 ). This organization suggests a cellular mechanism for how dermal photoreception governs color change and why it does so, perhaps to provide sensory feedback to chromatophores to fine-tune color change performance. One missing piece, however, is determining how dermal photoreceptors communicate with chromatophores to exert feedback control on skin color change. For example, the activation characteristics of the SWS1 receptors are unknown, along with the synapses and signaling molecules that may connect the two cell types. Though more research is needed, the sensory feedback model offered here helps explain both the lack of behavioral support for the direct control of color change by dermal photoreception and the widespread evolution of this sense across color-changing taxa. Specifically, environmental cues for color change may be captured by the eyes and integrated with feedback information from dermal photoreceptors about skin color state to fine-tune color change output (Supplementary Fig.  3 ). Such closed-loop feedback systems are common in physiology and behavior 44 and may be required by color change as they are by other outputs where fitness is coupled to the precision of performance 45 , 46 .

Dispersed chromatophore pigment suppresses short-wavelength irradiation of SWS1 receptors (left), whereas aggregated pigment permits short-wavelength irradiation (and, therefore, putative opsin activation) of SWS1 receptors (right). The predicted functional significance of dermal photoreception is, therefore, to monitor shifts in chromatophore pigment in order to detect feedback information about color change performance. Illustrated by M.D. Smith. Chromatophores = white cells at the top; SWS1 dermal photoreceptors = gray cells at the bottom.

Lastly, the physiological characteristics of dermal photoreceptors, such as their cellular activation characteristics and vitamin-A chromophore content for visual pigment function 40 , remain unknown. We can look to retinal photoreceptor physiology, however, to gain some insight into how this system might work. For example, rod and cone characteristics, such as cell stimulation over graded membrane potentials that scale with exposure to light 47 , would be particularly relevant to this system where the intensity of incident light upon dermal photoreceptors is dependent on the degree of pigment aggregation and chromatophore pigment type. Again, though dermal photoreception in hogfish and other species requires further research, our findings suggest a future area of study for extraocular photoreception related to sensory feedback, providing a framework for understanding the widespread prevalence of dermal photoreception in the skin of color-changing animals.

The study specimens were hogfish ( Lachnolaimus maximus ; family Labridae) ranging in total length from 16.5 to 35.5 cm ( N  = 16 fish, total). Hogfish is a protogynous hermaphroditic reef fish, switching from female to male as required at roughly 30.5 cm fork length 32 . Thus, the specimens included in this study are primarily female, representing subadult to adult life-history stages. Wild-caught hogfish were collected under a Florida Fish and Wildlife Conservation Commission special activity license (SAL-16-1822A-SR), by the approval of the Institutional Animal Care and Use Committees at Duke University (protocol #A233-16-10), Florida International University (protocol #IACUC-19-024), and the University of North Carolina Wilmington (protocol # A2020-016). Commercially-obtained hogfish were purchased from Dynasty Marine Associates (Marathon, FL) and Gulf Specimen Marine Laboratories, Inc. (Panacea, FL). All animals were humanely euthanized by either overdose of MS-222 (Tricaine) or eugenol (clove oil) according to approved IACUC procedures. For microspectrophotometry only, fresh carcasses provided tissues of adequate quality, which were obtained from recreational fisherman via Wrightsville Beach Diving Spearfishing Charter (Wrightsville Beach, NC).

Light microscopy

We used light microscopy to identify the types of pigmented chromatophores present in hogfish skin ( n  = 3 fish). Whole-mounted scales were placed under an Olympus SZX16 Stereomicroscope and imaged using Olympus DP71 camera (Olympus Scientific Solutions Americas, Waltham, MA). Images were taken of hogfish scales that were light and dark in appearance. Chromatophores were identified by morphology and pigment color.

Anti-opsin immunofluorescence

The scales of hogfish are covered with a thin layer of integument that contains the chromatophores cells that mediate color change. Thus, to examine SWS1 opsin expression in hogfish skin ( n  = 5 fish), scales were selected at random from different body regions (e.g., dorsal, ventral, and caudal body regions) and processed using conventional immunohistochemical techniques. To serve as a positive control, the left eye of a hogfish ( n  = 1) was taken and processed with the cornea, lens, and humors removed. These samples (either scales or eyecup) were then fixed in a solution of 4% paraformaldehyde in 1X phosphate-buffered saline (PBS), pH 7.4. Following a minimum of 48 h of fixation, samples were transferred to 25% sucrose in 1X PBS for cryoprotection, then embedded in Tissue-Tek O.C.T. compound at a 20 °C. Frozen cross sections (18-μm thick) were cut on a Leica CryoCut 1800 cryostat, thaw-mounted onto gelatin-coated glass microscope slides, and dried at room temperature overnight. Slides were then placed into fixative for 1 h, followed by four 15-min washes in 1X PBS (pH 7.4). The primary antisera (details below) were diluted in PBS containing 0.25% λ-carrageenan, 1% bovine serum albumin, and 0.3% Triton X-100 and applied to the slides for overnight incubation (minimum 8 h) at room temperature. After four 15-min rinses in PBS, slides were incubated for 1 h at room temperature with a fluorophore-conjugated secondary antiserum. Following four rinses in PBS, slides were coverslipped with Slow-fade Gold mounting medium with DAPI nucleic acid stain (Life Technologies, Grand Island, NY) and imaged either on a Nikon C1Si upright laser-scanning confocal microscope (Nikon Instruments, Melville, NY) or on a Leica SP8 upright laser-scanning confocal microscope (Leica Microsystems, Buffalo Grove, IL). Preparations also were imaged on a Zeiss Axioskop2 light and epifluorescence microscope mounted with an 89 North PhotoFluor LM-75 fluorescence light source (Carl Zeiss Microscopy, Peabody, MA) and a Pixera Penguin 600CL camera (Pixera Corporation, Santa Clara, CA). Images were post-processed in Adobe Photoshop to enhance contrast to intact images and add annotations.

All primary and secondary antisera were commercially obtained. The SWS1 opsin antiserum was raised against a recombinant human SWS1 immunogen (1:200-1000 concentration, polyclonal, EMD Millipore catalog# AB5407) and has known specificity and cross-reactivity to SWS1 opsins in diverse species (e.g., refs. 48 , 49 ). For the retina, rod opsin (rhodopsin; RH1) antiserum (1:500 concentration, monoclonal, EMD Millipore catalog# MAB5316) was used to counterstain rod photoreceptor outer segments. The primary antisera were labeled with secondary antisera that were conjugated to Alexa Fluor fluorescent dyes (1:500, Thermofisher Scientific catalog # A-11008 and A-21422).

Transmission electron microscopy

Scales taken from hogfish ( n  = 2 fish) originally fixed as described above were then immersed in modified Karnovsky’s fixative (2.5% glutaraldehyde and 2% paraformaldehyde in 0.15 M sodium cacodylate buffer, pH 7.4) for at least 4 h, postfixed in 1% osmium tetroxide in 0.15 M cacodylate buffer for 1 to 2 h and stained en bloc in 2% uranyl acetate for 1 h. Samples were taken through two iterations of serial dehydrations in ethanol (50%, 70%, 90%, 100%), embedded in Durcupan epoxy resin (Sigma-Aldrich, St. Louis, MO), sectioned (both transverse and sagittal) at 50 to 60 nm on a Leica UCT ultramicrotome, and picked up using Formvar and carbon-coated copper grids. Sections were stained with 2% uranyl acetate for 5 min and Sato’s lead stain for 1 min. Grids were viewed using a Tecnai G2 Spirit BioTWIN transmission electron microscope equipped with an Eagle 4k HS digital camera (FEI, Hillsboro, OR).

Anti-opsin immunogold labeling

Hogfish scales ( n  = 2) were fixed in 4% paraformaldehyde in 1x PBS for 24 h and stored in 1x PBS at 4 °C before processing. Samples were taken through a dehydration series with ethanol (50% and 70% for 15 min, 80% for 10 min), infiltrated with a 2:1 mixture of LR White Resin (Electron Microscopy Sciences, Hatfield, PA) to 80% ethanol, and embedded in resin with four changes of LR White (1 h, overnight, and twice for 30 min). Resin was cured in Beem capsules in a vacuum oven at 50 °C for 4 days. Cross sections of the embedded scales were cut at 90 nm using a Lecia UC7 ultramicrotome and picked up on Formvar-coated nickel mesh grids. Grids were floated on drops of the SWS1 primary antiserum described above (EMD Millipore catalog# AB5407) diluted in 1% bovine serum albumin (BSA) in 1x PBS (1:300) for 2 h at room temperature in a humidity chamber. After washing on drops of 1x PBS four times for 5 min each, grids were floated on drops of a 25-nm colloidal gold secondary antiserum diluted in the BSA solution (1:40, Electron Microscopy Sciences, Hatfield, PA) for 2 h at room temperature in a humidity chamber. As expected, the use of these relatively large gold particles permitted the low-magnification imaging required to visualize the target cells but resulted in sparse labeling by the secondary antibody as explained by Cornford and colleagues (2003) 50 . Grids were again washed in 1x PBS and subsequently in DI water. Sections were imaged using a Tecnai G2 Spirit Bio Twin transmission electron microscope at 80 keV with an Eagle 2k HR 200 kV CCD camera (FEI, Hillsboro, OR). Images were post-processed in Adobe Photoshop to enhance contrast and add annotations.

Spectral sensitivity estimation

We evaluated the deduced amino acid sequence of the SWS1 opsin found in hogfish skin to estimate its wavelength of peak sensitivity (λ max ). Previous studies using site-directed mutagenesis and other methods have identified the amino acid sites that affect spectral tuning of ciliary opsins (such as SWS1) 37 , 38 . Evaluating amino acid substitutions at these positions relative to their known consequences on opsin sensitivity allows λ max values to be inferred. Using data from the hogfish skin transcriptome reported by Schweikert and colleagues 20 , we first assessed the SWS1 opsin gene using the BLASTx feature provided by the National Center for Biotechnology Information (NCBI). From this output, the animal with the highest homology SWS1 gene to that of hogfish, which also had a known SWS1 λ max , was identified as the night aulonocara ( Aulonocara hueseri ; family Cichlidae, Genbank accession AY775100.1 ). The spectral tuning sites of ciliary opsins are commonly reported relative to the positions of a bovine rhodopsin gene standard. To locate these tuning sites, the deduced amino acid sequences of SWS1 from hogfish and the night aulonocara were aligned to bovine rhodopsin using CLC Viewer Software (Qiagen, Redwood City, CA). We then compared the amino acid residues at all known SWS1 spectral tuning sites between these fishes to estimate the λ max of SWS1 in hogfish skin. The full absorbance spectrum was calculated from the λ max using the template found in Stavenga and colleagues (1993) 51 .

Microspectrophotometry

We measured the transmission spectra of hogfish chromatophores types (i.e., melanophores, erythrophores, and xanthophores) using microspectrophotometry (MSP). Scales were selected at random from different body regions (e.g., dorsal, ventral, and caudal body regions) for analysis within 12 h of hogfish euthanasia ( n  = 3 fish). Skin tissue was removed from scales using a razor blade and was then mounted on a #1.5 glass coverslip preparation (Electron Microscopy Sciences, Hatfield, PA) using 30% glycerol in 0.1 M sodium phosphate buffer, pH 7.4. A ring of silicon grease was placed around the tissue, then a second coverslip was pressed on top of the preparation. MSP was performed on a Nikon Diaphot-TMD inverted compound microscope (Melville, NY). A 20-Watt quartz tungsten halogen lamp (Optometrics LLC, San Francisco, CA) provided white light, which was passed through a 400-µm diameter fiber (Ocean Optics, Dunedin, FL, USA) and focused by a condensing objective through a single chromatophore cell (to the margins of dispersed pigment). The transmitted light was collected by a Zeiss 16x Neofluar microscope objective before passing a 1-mm diameter fiber (Ocean Optics Inc., Dunedin, FL) connected to a USB2000 spectrometer (Ocean Optics Inc., Dunedin, FL). Reference scans were taken in areas of unpigmented tissue, in the space between chromatophores with aggregated pigment. Transmittance spectra of each chromatophore cell type (20 cells per type and fish and thus, n  = 60 cells per chromatophore type) were measured for each fish using OceanView Software (v1.6.7; Ocean Optics Inc., Dunedin, FL). Spectra for each cell type were averaged across fish at ~0.3 nm optical resolution to generate transmission spectra of hogfish chromatophores spanning from 400 to 700 nm. The spectra were averaged using Microsoft Excel and were visually inspected in order to describe transitions between regions of low and high transmittance. The reported wavelengths reflect the point where the slope of these transitions would intersect with the x-axis.

Statistics and reproducibility

Experiments associated with each method were replicated multiple times, independently. For light microscopy, micrographs were collected across six independent sample preparations across three fish. For immunofluorescence, experiments were run three times independently on separate days at the Florida Institute of Technology and replicated an additional two times at Duke University. The TEM and immunogold images were collected from two separate sample preparations, which had been analyzed at UCSD and UNCW, respectively. For microspectrophotometry, data were collected across three fish that had been sampled over separate days. Spectra for n  = 60 cells/chromatophore type were collected using no less than 10 scales sampled from each fish body.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

As part of this study, we examined the SWS1 opsin gene sequence of Aulonocara hueseri (Genbank accession AY775100.1 ) and aligned it to the SWS1 opsin sequence of Lachnolaimus maximus (Genbank Accession: PRJNA386691 ). All other data included in this study are available in the main text and supplemental materials.

Code availability

There is no code to report.

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Acknowledgements

We thank the microscopy facilities of Duke University, Florida Institute of Technology (FIT), University of North Carolina Wilmington (UNCW) and the University of California San Diego (UCSD) Cell and Molecular Medicine (CMM) Department for shared resources and staff support. At UCSD/CMM, we thank M. Farquhar for the use of the TEM facility, Y. Jones for help with sample preparation, and T. Meerlo for general assistance. We also thank L. Elliot and A. Taylor for research support in the Richard M. Dillaman Bioimaging Facility at UNCW. We also thank H.F. Nijhout (Duke) and T. Frank (Nova Southeastern University) for loaning equipment for this study. We acknowledge M. Bolton, T. Holford, N. Kamasawa at the MPFI for Neuroscience and J. Fasick (University of Tampa) for providing helpful insights into our study. We thank Captain C. Slog for providing specimens, D. Kimberly for hogfish photographs, and M.A. Schweikert for help in hogfish collection. We thank W.M. Kier, E.M. Caves, R.R. Fitak, A.L. Davis, and D.E. Speiser for comments on earlier versions of the manuscript. We also thank the Center for Marine Sciences at UNCW, namely R. Moore and J. White for help with animal husbandry and M.D. Smith for scientific illustration. This is contribution #1595 from the Institute of Environment at Florida International University. This study was supported by Duke University’s Charles W. Hargitt Fellowship, the FIU CASE Distinguished Scholar Award, and monies from the FIU Center for Coastal Oceans Research in the Institute for Environment awarded to L.E.S. Additional support came from the NSF Division of Environmental Biology Grant # 1556059 awarded to H.B.G. This study was also supported by the Marine Biological Laboratory Neurobiology Post Course Research award to L.F.N. Further, this material is based on research sponsored by AFRL/RW under agreement number FA8651-22-0036 awarded to L.E.S. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of AFRL/RW or the U.S. Government. This work is approved for public release (Distribution A), case #AFRL-2023-2347.

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Lorian E. Schweikert

Present address: Department of Biology and Marine Biology, University of North Carolina Wilmington, Wilmington, 28403, USA

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Institute of the Environment, Department of Biological Sciences, Florida International University, North Miami, FL, 33181, USA

Lorian E. Schweikert & Heather D. Bracken-Grissom

Biology Department, Duke University, Durham, NC, 27708, USA

Lorian E. Schweikert, Benjamin R. Wheeler & Sönke Johnsen

Torch Technologies, Shalimar, FL, 32579, USA

Laura E. Bagge

Air Force Research Laboratory/RWTCA, Eglin Air Force Base, FL, 32542, USA

Department of Biology and Marine Biology, University of North Carolina Wilmington, Wilmington, 28403, USA

Lydia F. Naughton & Jacob R. Bolin

College of Engineering and Science, Florida Institute of Technology, Melbourne, FL, 32901, USA

Michael S. Grace

Department of Invertebrate Zoology, National Museum of Natural History, Smithsonian Institution, Washington, DC, 20560, USA

Heather D. Bracken-Grissom

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Contributions

L.E.S. and S.J. conceived of the study and experimental design; L.E.S., L.F.N. and J.R.B. immunofluorescence (aided by B.R.W., M.S.G., and H.B.G.); L.E.S. carried out the sequence analysis and microspectrophotometry (aided by S.J.); L.E.B. and L.F.N. completed the transmission electron microscopy; B.R.W. managed hogfish procurement and husbandry; L.E.S. drafted the manuscript, with L.E.S. and L.F.N. conceptualizing Fig. 7; All authors contributed to the interpretation of the results and completion of the final manuscript.

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Schweikert, L.E., Bagge, L.E., Naughton, L.F. et al. Dynamic light filtering over dermal opsin as a sensory feedback system in fish color change. Nat Commun 14 , 4642 (2023). https://doi.org/10.1038/s41467-023-40166-4

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• Gastrointestinal syndromes preceding a diagnosis of Parkinson’s disease: testing Braak’s hypothesis using a nationwide database for comparison with Alzheimer’s disease and cerebrovascular diseases
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• http://orcid.org/0000-0003-3889-6853 Bo Konings 1 ,
• Luisa Villatoro 2 ,
• Jef Van den Eynde 3 ,
• Guillermo Barahona 2 ,
• Robert Burns 4 ,
• Megan McKnight 4 ,
• Ken Hui 4 ,
• Gayane Yenokyan 5 ,
• Jan Tack 1 ,
• Pankaj Jay Pasricha 2
• 1 Translational Research Centre for Gastrointestinal Disorders (TARGID) , KU Leuven University Hospitals , Leuven , Belgium
• 2 Department of Medicine , Mayo Clinic Arizona , Scottsdale , Arizona , USA
• 3 Department of Cardiology , KU Leuven University Hospitals , Leuven , Belgium
• 4 Department of Gastroenterology , Johns Hopkins Medicine , Baltimore , Maryland , USA
• 5 Department of Biostatistics , Johns Hopkins University Bloomberg School of Public Health , Baltimore , Maryland , USA
• Correspondence to Dr Pankaj Jay Pasricha, Department of Medicine, Mayo Clinic Arizona, Scottsdale AZ 85259, Arizona, USA; Pasricha.Jay{at}mayo.edu

Objective Braak’s hypothesis states that Parkinson’s disease (PD) originates in the gastrointestinal (GI) tract, and similar associations have been established for Alzheimer’s disease (AD) and cerebrovascular diseases (CVD). We aimed to determine the incidence of GI syndromes and interventions preceding PD compared with negative controls (NCs), AD and CVD.

Design We performed a combined case-control and cohort study using TriNetX, a US based nationwide medical record network. Firstly, we compared subjects with new onset idiopathic PD with matched NCs and patients with contemporary diagnoses of AD and CVD, to investigate preceding GI syndromes, appendectomy and vagotomy. Secondly, we compared cohorts with these exposures to matched NCs for the development of PD, AD and CVD within 5 years.

Results We identified 24 624 PD patients in the case-control analysis and matched 18 cohorts with each exposure to their NCs. Gastroparesis, dysphagia, irritable bowel syndrome (IBS) without diarrhoea and constipation showed specific associations with PD (vs NCs, AD and CVD) in both the case-control (odds ratios (ORs) vs NCs 4.64, 3.58, 3.53 and 3.32, respectively, all p<0.0001) and cohort analyses (relative risks (RRs) vs NCs 2.43, 2.27, 1.17 and 2.38, respectively, all p<0.05). While functional dyspepsia, IBS with diarrhoea, diarrhoea and faecal incontinence were not PD specific, IBS with constipation and intestinal pseudo-obstruction showed PD specificity in the case-control (OR 4.11) and cohort analysis (RR 1.84), respectively. Appendectomy decreased the risk of PD in the cohort analysis (RR 0.48). Neither inflammatory bowel disease nor vagotomy were associated with PD.

Conclusion Dysphagia, gastroparesis, IBS without diarrhoea and constipation might specifically predict Parkinson’s disease.

• FUNCTIONAL BOWEL DISORDER
• BRAIN/GUT INTERACTION
• ENTERIC NEURONES

Data availability statement

Data are available upon reasonable request. Data exported from TriNetX were saved in Excel files and archived. Every co-author affiliated to Johns Hopkins University was granted access to the TriNetX Research network by the Institute of Clinical and Translational Research (ICTR). The online supplemental materials contain an extensive list of tables representing the original data; researchers will be granted access to the original aggregated data upon reasonable request, with agreement of the corresponding author (PJP).

http://dx.doi.org/10.1136/gutjnl-2023-329685

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Braak’s hypothesis states that Parkinson’s disease (PD) originates in the gut in a subset of patients, but no studies to date have systematically investigated a broad range of gastrointestinal (GI) symptoms and diagnoses before a diagnosis of PD.

WHAT THIS STUDY ADDS

This is the first multicentre study to establish that dysphagia, gastroparesis, constipation and irritable bowel syndrome without diarrhoea specifically increase the risk of a subsequent new onset diagnosis of idiopathic Parkinson’s disease, even compared with other neurological diseases, such as Alzheimer’s disease and cerebrovascular diseases.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

Early detection of GI syndromes might contribute to the identification of patients at risk of PD during a phase were disease modifying therapies could prevent the progression of α-synuclein pathology.

Introduction

Parkinsonism is a clinical syndrome characterised by bradykinesia, rest tremor, rigidity and postural instability. 1 Its most common cause is Parkinson’s disease (PD), the pathological hallmark of which is thought to be cytoplasmatic eosinophilic Lewy body (LB) depositions. These depositions, mainly consisting of misfolded α-synuclein, have not only been found in the CNS but also in the vagus nerve and enteric nervous system (ENS) of patients with PD. 1

These findings led Braak et al to state the neuroanatomical hypothesis that α-synuclein pathology progresses from peripheral sites such as the ENS to the CNS via vagal or olfactory pathways, thereby introducing the concept that the gastrointestinal (GI) tract might serve as a gateway for environmental factors that induce α-synuclein misfolding and lead to PD. 2 A large body of evidence has since then accumulated to support this claim. Even in early untreated stages of the disease, neuropathological studies have found that α-synuclein concentrations in the ENS of patients with PD were higher than those of otherwise healthy individuals, in a characteristic rostrocaudal gradient following visceromotor projections of the vagus nerve. 3–5 Complementary studies have shown that various motility disorders 5–7 and inflammatory bowel disease (IBD) 8 can precede PD and therefore may be risk factors for its development. Moreover, since Gray and colleagues 9 first identified the vermiform appendix as a potential source of misfolded α-synuclein, conflicting observational studies have been published about the impact of an appendicectomy on the risk of idiopathic PD. 9–14 Finally, two recent registry based studies have strengthened the concept of retrograde vagal α-synuclein propagation by showing that a truncal vagotomy might be protective against the development of PD. 15 16 Apart from the bottom up link formulated by Braak, a top down aetiology in which GI symptoms are present in early phases when neurological manifestations are still unnoticed is also supported by experimental evidence. 1 Even if no causal link exists, GI syndromes might still represent a risk factor through other mechanisms, or both might be related to a yet unknown third factor.

Apart from PD, other neurological disorders have also been hypothesised to have GI precedents, either through similar neuroimmune pathways or translocation of microbiome derived neurotoxins into the CNS. 17 A strong pathological link between microbiome derived neurotoxins, including Escherichia coli derived Lipopolysaccharide, has been established with disrupted intestinal cell adhesion, impaired synaptic signalling in the Alzheimer’s disease (AD) brain and exacerbation of inflammatory neuropathology. 18 19 Additionally, given the prominent role of reactive oxygen species induced inflammation in cerebrovascular diseases (CVD), 20 proinflammatory intestinal 21 and extraintestinal 22 diseases have been linked to a higher risk of CVD than that predicted by conventional risk factors. 20

Previous studies on this topic have been limited by small sample sizes and inadequate controls. Therefore, we used a nationwide electronic health record (EHR) network to investigate the incidence of various GI syndromes and interventions, such as appendectomy and vagotomy, before the onset of PD. Because previous studies lacked specificity for exposures associated with PD, we used a case-control study design to compare patients with PD not only with negative controls (NCs), but also with patients diagnosed with AD and CVD. Additionally, we established a cohort study design for each exposure in the case-control design to validate these findings and establish relative risk (RR) estimates relevant in clinical practice.

Study design and data source

To investigate the association between various GI syndromes and interventions with the subsequent development of new onset PD, we analysed electronic medical records from the TriNetX Analytics Research Network (Cambridge, Massachusetts, USA). At the moment of data collection, the network consisted of more than 80 million patients from 57 predominantly academic medical centres in the USA. Additional information can be found in the online supplemental methods .

Supplemental material

Study population and variables of interest.

In the case control analysis, we examined the incidence of exposures retrospectively (ie, before an initial diagnosis of PD compared with matched controls). Patients with PD were captured using a previously validated method. 23 Patients were queried using the International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10) diagnosis of PD (G20), if documented between 1 January 2005 and 1 July 2021; the first ever diagnosis was used as the index event. Only those with at least two prescriptions of an antiparkinsonian drug and a documented ambulatory visit at least 2 years before the first diagnosis of PD were included; secondary causes of PD were excluded. To determine PD specific exposures, control subjects comprised three groups: NCs, and patients with a diagnosis of AD and CVD. NCs consisted of patients without a recorded ICD-10 diagnosis of PD, with at least two documented ambulatory visits between the ages of 50 and 90 years, at least 2 years apart, recorded between 1 January 2005 and 1 July 2021. A minimum of 2 years of retrospective follow-up was ensured by using the second of these visits as the index event. Similarly, 2 years of follow-up was ensured for the AD and CVD groups, and the first ever documented respective ICD-10 diagnosis in the medical records after 1 January 2005 was chosen as the index event. In a pairwise fashion, these groups where then matched to the PD group for age, sex, race and ethnicity using a propensity score matching algorithm.

To cover the entirety of the GI tract, 18 exposures were investigated: achalasia, dysphagia, gastro-oesophageal reflux disease (GORD), gastroparesis (GP), functional dyspepsia (FD), paralytic ileus (PI), diarrhoea, irritable bowel syndrome (IBS) with constipation (IBS-C), IBS with diarrhoea (IBS-D), IBS without diarrhoea, intestinal pseudo-obstruction (approximate synonym of K59.8: other specified functional GI disorders), faecal incontinence (FI), Crohn’s disease (CD), ulcerative colitis (UC), microscopic colitis (MC), appendectomy and vagotomy. We conducted additional sensitivity analyses in the network, which included stratified analyses based on sex and age at the diagnosis of the index event. A detailed breakdown of the query, inclusion and exclusion criteria, stratified analyses and coding can be found in the online supplemental methods .

To validate the results from the case-control analyses, we set up a complementary cohort study design. Eighteen cohorts, each diagnosed with one of the investigated exposures in the case-control analysis, were queried and compared with a respective NC cohort (ie, without the exposure) for the prospective risk of developing PD, AD or CVD. Only those with at least 5 years of prospective follow-up were included, and were propensity score matched for age, sex, race and ethnicity, and additionally for a set of potential risk factors and risk modifiers for the development of PD, AD and CVD: arterial hypertension, diabetes mellitus, atrial fibrillation and flutter, and nicotine dependence.

Statistical analysis

In the case-control analyses, patients were counted as positive for an exposure if the respective ICD-10 code was documented any time before the first diagnosis of PD or the control health event. To approximate the diagnostic interval between each exposure and the first PD diagnosis, a yearly cross sectional prevalence for each exposure was calculated for the PD and NC groups, up to 6 years before the index event. To detect and quantify potential surveillance bias in our case-control analyses, we collected an agnostic set of negative exposures (Charlson comorbidities). This allowed us to determine the OR that should be considered as indicative of no association. Additionally, we collected positive exposures based on a previous case-control study that identified prodromal motor and non-motor symptoms of PD. 24 This enabled us to assess the ability of our dataset to reproduce existing associations. The coding can be found in the online supplemental methods .

In the cohort analyses, patients diagnosed with the exposure of interest and their NCs were counted as positive for an outcome (PD, AD or CVD) if the respective new onset ICD-10 diagnosis occurred within a 5 year follow-up. Subjects who already had the outcome of interest before the index event were excluded after propensity score matching.

Exposures and outcomes were collected as absolute numbers; ORs and RRs were calculated with 95% CIs. Standardised mean differences (SMDs) were used to compare baseline characteristics; an SMD of <0.2 was considered well balanced. A Pearson χ 2 test was calculated to compare outcomes, and a two sided P value of <0.05 was used to indicate statistical significance. Correction for false discovery rate (FDR) was performed using the step up procedure by Benjamini and Yekutieli, with the Stats package in R (V.4.3.0). 25

For the case control-study, 24 624 patients with PD met all of the criteria and were matched with 8 267 744 NCs, and 36 187 AD and 528 207 CVD patients, giving 24 624 patients in the comparison with NCs, 19 046 with AD and 23 942 with CVD. Baseline characteristics after pairwise matching are presented in table 1 ; minimal differences in age at index persisted. SMDs and p values before and after matching can be found in online supplemental tables 1,2 .

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Baseline characteristics for subjects with PD and controls in the case-control analyses after pairwise matching.

The results of the case-control analyses are presented in figure 1 and online supplemental table 3 . All GI syndromes were significantly increased in the PD group compared with NCs (OR >1; p<0.05). However, only dysphagia (OR 3.58), GP (OR 4.64), FD (OR 3.39), intestinal pseudo-obstruction (OR 3.01), diarrhoea (OR 2.85), constipation (OR 3.32), IBS-C (OR 4.11), IBS-D (OR 4.31), IBS without diarrhoea (OR 3.53) and FI (OR 3.76) gave ORs that were numerically greater than the upper limit of the negative exposures (OR range 1.20–2.79; online supplemental tables 4–6 and online supplemental figures 1–3 ). Furthermore, only dysphagia, GP, IBS-C, IBS without diarrhoea and constipation were specific for PD (OR >1; p<0.05) when compared with both the AD and CVD group. After correcting for FDR, GP and constipation did not remain significantly different (p>0.05) compared with the CVD and AD groups, respectively. Other exposures were not only significantly associated with PD, but also showed strong associations with the AD or CVD group. For example, FI appeared to be equally increased before AD, and diarrhoea was even more increased before the onset of both AD and CVD.

For FD, IBS-D and intestinal pseudo-obstruction, the risk of PD, AD and CVD did not differ significantly (p>0.05). The remaining exposures, including achalasia (OR 1.92), GORD (OR 2.18), PI (OR 2.63), CD (OR 1.99), UC (OR 1.87), MC (OR 2.19) and appendectomy (OR 2.40) showed positive associations with PD compared with NCs, but gave ORs below the upper limit of what is expected by surveillance bias (OR range 1.20–2.79). Only for GORD and appendectomy we observed significant differences compared with AD (OR 1.14, p<0.0001) and CVD (OR 0.57, p=0.03), respectively. The latter did not remain significant after correction for FDR (p=0.21). Prior vagotomy did not impact the risk of PD.

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Case-control analyses. Odds ratios (ORs) of previous exposures in patients with Parkinson’s disease (PD) compared with matched negative controls (NCs), or patients with Alzheimer’s disease (AD) and cerebrovascular diseases (CVD). ᵃIn the case-control analysis, the incidence of exposures was examined retrospectively (ie, before an initial diagnosis of PD compared with matched NCs, and AD and CVD patients). Patients were diagnosed with PD or the respective control health event between 1 January 2005 and 1 July 2021, and NCs were queried using two ambulatory visits during the same time window. Patients and exposures were identified with the International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10) codes, and Current Procedural Terminology (CPT) codes, using electronic medical health record data from the TriNetX research network ( online supplemental methods ). Exposures were included if they were documented any time before the diagnosis of PD or the control health event, in the available medical records. ᵇORs were calculated as follows: odds of documented exposure in the PD cohort/odds of documented exposure in the control cohort. Absolute rates can be found in online supplemental table 3 . ᶜP values were calculated using a Pearson χ 2 test, after matching for baseline characteristics (ie, age, sex, race and ethnicity). d Correction for false discovery rate was performed using the step up procedure by Benjamini and Yekutieli, with the Stats package in R. *The term intestinal pseudo-obstruction was used as an approximate synonym for the ICD-10 code K59.8 other specified functional intestinal disorders.**The term IBS not otherwise specified is commonly used as an approximate synonym for the ICD-10 code K58.9 IBS without diarrhoea. IBS, irritable bowel syndrome.

For GP, women were approximately twice as likely as men to develop PD (OR 7.3 for women, 3.05 for men, both p<0.0001 ( online supplemental tables 7–9 and online supplemental figure 4 ), and the OR for GP was especially high for early onset PD ( online supplemental tables 10–11 ). Exclusion of previous antidopaminergic drug use did not significantly alter any associations with PD compared with NCs ( online supplemental table 12 and online supplemental figure 5 ). The ORs of all PD specific exposures were positioned well within the range of those of established motor and non-motor prodromes of PD (ie, positive exposures; OR PD vs NCs 2.04–7.41). An approximation of the diagnostic interval for each exposure can be found in online supplemental figure 6 .

Figure 2 and online supplemental tables 13–14 show the results of the cohort analyses. A significantly increased RR of new onset PD (RR >1; p<0.05) was found after a diagnosis of dysphagia (RR 2.27), GORD (RR 1.13), GP (RR 2.43), FD (RR 1.15), intestinal pseudo-obstruction (RR 1.84), diarrhoea (RR 1.32), constipation (RR 2.38), IBS without diarrhoea (RR 1.17) and FI (RR 1.74); all except for FD (p=0.09) remained significant after correction for FDR. However, only for dysphagia, GP, intestinal pseudo-obstruction, IBS without diarrhoea and constipation was this RR numerically higher than the RR of developing AD and CVD, and statistical significance (p<0.05) was achieved only for constipation and dysphagia ( online supplemental table 15 ). In line with the case control analyses, FI equally increased the risk of PD and AD (PD: RR 1.74; AD: RR 1.76, both p<0.0001), and in the GORD cohort the RR of developing CVD was higher than that of PD (CVD: RR 1.38; PD: RR 1.13, both p<0.0001). FD and diarrhoea were associated with all three disorders (p≤0.05), and PI, CD and MC only increased the risk of CVD (RR 1.22, p<0.0001; RR 1.20, p<0.0001; RR 1.26, p=0.02, respectively). Although no specific association was found in the case-control analysis, appendectomy significantly reduced the risk of developing PD (RR 0.48, p=0.05), which did not remain significant after correction for FDR. For all other exposures (ie, achalasia, UC, IBS-C, IBS-D and vagotomy), no significant associations were found.

Cohort analyses. Relative risks (RRs) of developing Parkinson’s disease (PD), Alzheimer’s disease (AD) or cerebrovascular diseases (CVD) within 5 years of the diagnosis of a given exposure, compared with negative control (NCs) without the respective exposure. ᵃFor each analysis, a cohort of patients identified by the diagnosis of a given exposure was compared with their respective NCs for the prospective risk of PD, AD and CVD within 5 years of the index event (ie, diagnosis of the given exposure, or a visit for NCs). After propensity score matching, patients that already had the investigated outcome (ie, PD, AD or CVD) documented before the index event were excluded from the analysis. Exposures and outcomes were identified using the International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10,) and Current Procedural Terminology (CPT) codes. Electronic medical health record data were collected from the TriNetX research network. Diagnostic coding can be found in the online supplemental methods . ᵇRRs were calculated as follows: risk of outcome in the exposure cohort/risk of outcome in the control cohort. Absolute rates can be found in online supplemental table 13 . ᶜP values were calculated with a Pearson χ 2 test, after matching for baseline characteristics and risk factors. Baseline characteristics included age, sex, race and ethnicity; risk factors included arterial hypertension, diabetes mellitus, atrial fibrillation and flutter, and nicotine dependence. d Correction for false discovery rate was performed with the step up procedure by Benjamini and Yekutieli, using the Stats package in R. *The term intestinal pseudo-obstruction was used as an approximate synonym for the ICD-10 code K59.8 other specified functional intestinal disorders. **The term IBS not otherwise specified is commonly used as an approximate synonym for the ICD-10 code K58.9 IBS without diarrhoea. IBS, Irritable bowel syndrome.

We used a nationwide EHR network to comprehensively investigate disorders across the entire GI tract before a diagnosis of PD. We used two complementary study designs to establish that dysphagia, gastroparesis, constipation and IBS without diarrhoea specifically increase the risk of a subsequent new onset diagnosis of idiopathic PD, even compared with other neurological diseases, such as AD and CVD.

Surveillance bias is an inherent problem in observational studies. When not addressed appropriately, it can compromise the validity of causal inference and lead to irreproducible results. 26 A broader implementation of empirical approaches to evaluate and correct for the presence of systematic error in observational studies is needed. 27 Therefore, we set up an approach to understand the true extent of surveillance bias in our study and its potential contribution to implicating premorbid factors for PD. Hence we collected data on all diagnoses included in the Charlson comorbidity index. These premorbid conditions were considered as a comprehensive set of agnostic negative exposures. In our case-control study, we observed statistically significant increases in most of these exposures in PD cases compared with NCs, but not compared with AD and CVD ( online supplemental figure 1 ). To establish whether these increases represented surveillance bias or true associations (although unlikely based on the current literature), we investigated the same exposures for other neurological disorders (AD and CVD) compared with their NCs ( online supplemental figure 3 ). Since the same significant correlations emerged, surveillance bias was likely. Subsequently, we determined the range of ORs that should be expected if they are a result of surveillance bias alone. These ORs ranged between 1.20 and 2.79 in the analysis of PD with NCs. To determine whether existing associations with PD could be replicated with ORs greater than those of negative exposures, we also collected prodromal motor and non-motor symptoms of PD (ie, positive exposures). These resulted in ORs ranging between 2.04 and 7.41 ( online supplemental figure 1 ).

Having established a measure of surveillance bias in the case-control study, we then determined ORs for the putative GI pre/comorbidities of PD. Relative to the upper limit of the negative exposures, GI exposures fell into two categories. First were those for which the ORs overlapped with the ORs expected for surveillance bias (OR 1.20–2.79). For these exposures, including achalasia, GORD, PI, IBD (ie, CD, UC, and MC), appendectomy and vagotomy, we cannot be confident that these were true associations, although within the constraints of our study we cannot categorically state that they were not. Second were those for which the ORs were clearly higher than the ORs expected for surveillance bias (OR >2.79). For these exposures, including dysphagia, GP, FD, intestinal pseudo-obstruction, diarrhoea, constipation, IBS-C, IBS-D, IBS without diarrhoea and FI, we can confidently state to have established significant associations with new onset PD. To determine the specificity of the identified significant exposures for PD in the case-control analyses, we subsequently compared subjects with PD with subjects with AD and CVD. Only dysphagia, GP, constipation, IBS without diarrhoea and IBS-C remained specific for PD compared with both neurological diseases ( table 2 ). Importantly, we cannot exclude the possibility that these factors might still be associated with these diseases, although at a smaller scale. Similarly, while other exposures were not specific for PD (ie, FD, intestinal pseudo-obstruction, diarrhoea, IBS-D and FI), we cannot strictly exclude the possibility that these conditions might still be risk factors for PD.

Summary of Parkinson’s disease specific exposures in the case-control and cohort studies

Finally, to validate these findings both in terms of their significance and specificity and establish one RR estimate of developing PD after the diagnosis of each exposure, we set up a complementary cohort study. Here, five exposures (ie, dysphagia, GP, IBS without diarrhoea, intestinal pseudo-obstruction and constipation) significantly increased the risk of PD and resulted in RRs that were numerically greater than those of AD and CVD ( table 2 ). Four of these provide internal validation for PD specific exposures identified in the case-control analysis. These exposures are thus very unlikely to be a result of selection or surveillance bias and can therefore be considered the most significant findings from our study. Minor discrepancies can be explained by intrinsic differences in the study designs.

The consistent correlation between constipation and PD (RR 2.38, 95% CI 2.24 to 2.54) confirms an abundance of existing literature. Previous reports have stated that constipation can even precede PD by up to 20 years. 28 More surprising is the strong association for dysphagia (RR 2.27, 95% CI 2.10 to 2.45), which has so far mainly been reported after its diagnosis. 1 The prevalence of oesophageal dysmotility in PD has been shown to be as high as 80% when using objective measures, 29 but a delay in oropharyngeal transit has also been found in drug naïve and subjectively asymptomatic phases of the disease. 30 31 While evidence supports that oropharyngeal function might be affected through brainstem and cortical areas, 6 29 post mortem studies by Mu et al showed that pharyngeal muscles, sensory neurons and motor neurons are also often affected by LB pathology in PD. 32 33 Interestingly, the highest RR was observed for GP (RR 2.43, 95% 1.92 to 3.09), a disorder characterised by delayed gastric emptying (GE) in the presence of symptoms such as nausea, vomiting, early satiety, postprandial fullness, belching and bloating, and the absence of mechanical obstruction. 34 As Braak et al hypothesised, 35 the multitude of gastric vagal connections makes GP an especially promising candidate as a biomarker of PD. 6

Furthermore, despite considerable overlap in symptoms and pathology, 36 the lack of PD specificity for FD suggests that an established delay in GE in the presence of symptoms is more strongly associated with PD than symptoms alone, assuming that ICD codes are taken at face value. This indicates that objective changes in enteric physiology may provide a more reliable measure for evaluating enteric involvement in PD. Although the prevalence of delayed GE in PD has been reported to range from 70% to 100%, 37 reports of GP preceding PD remain anecdotal. 37 Because of its relatively low prevalence, it should not be a surprise that our study is the first to provide observational evidence that GP and dysphagia might precede PD. 5 6 More established is the association of IBS with the subsequent development of PD. 38–40 IBS-C and IBS without diarrhoea were both specifically increased in the case-control analysis, but only the latter was replicated in the cohort analysis (RR 1.17, 95% CI 1.05 to 1.3). Importantly, a Swedish study revealed that although the positive predictive value of ICD-10 codes for IBS is generally high (80–95%), their accuracy in indicating specific subtypes was considerably lower (55–67%). 41 Nevertheless, increased intestinal permeability constitutes a core pathophysiological mechanism in a major subset of IBS patients, 42 and has also been found in PD. 38 Routine colonoscopies for clinically suspect IBS could become important to determine the presence of LB pathology in patients at risk for PD, only if intestinal LB pathology becomes an established biomarker, which until now it has not. 39

Although anorectal symptoms are among the most frequent GI symptoms in PD, our data suggest that the presence of FI might not distinguish between the development of PD and other neurodegenerative diseases. 43 Even if not prodromal to AD, our findings support the fact that the progression of cognitive decline in AD is frequently unmasked by FI. 44 While diarrhoea and FD increased the risk of all three diseases, intestinal pseudo-obstruction showed specificity for PD in the cohort analyses (RR 1.84, 95% CI 1.18 to 2.87). This disorder, characterised by impaired peristalsis and presumably caused by a neuropathy or myopathy, has been described in various neurological disorders, including PD. 45

Finally, some exposures in the case-control analyses gave ORs in the range of those expected from surveillance bias. These exposures included achalasia, GORD, PI, IBD (ie, CD, UC, and MC), appendectomy and vagotomy. Other than for GORD, the cohort analyses also did not show any significant associations with PD for these exposures. While IBD has been linked to PD in various observational 46 47 and genetic studies, 48 neither of our study designs supported this link. However, we cannot strictly dismiss the possibility of an association based on this empirical surveillance bias cut-off and a relatively limited follow-up. In addition, we were unable to assess the impact of anti-tumour necrosis factor (anti-TNF) therapy exposure, which has been hypothesised to decrease the risk of PD. 49 Prospective studies are necessary to investigate this association and to establish whether anti-TNF therapy can effectively protect against PD. Notably, concordant with an earlier study that linked reflux oesophagitis to an increased risk of stroke and transient ischaemic attack in patients with atrial fibrillation, 21 the risk of CVD in our study was significantly greater after a diagnosis of GORD, CD and MC. These findings suggest that a better understanding of the link between GI inflammation and cerebrovascular events may lead to improved risk stratification and identification of new preventive strategies. 21

After Grey et al first discovered that α-synuclein was most abundant in the appendiceal mucosa, 9 conflicting evidence has emerged about the impact of an appendectomy on PD risk. While three studies did not find any association, 11 14 50 one abstract reported an increased risk of PD 13 and two large observational studies supported a protective effect. 10 12 Despite our limited follow-up and sample size compared with the aforementioned studies, we observed a relative risk reduction of 52% in our cohort analysis, while the case-control analysis was likely underpowered to detect any consistent association for appendectomy. Multiple studies suggest that the appendix constitutes a prominent source of seeding competent pathologically folded α-synuclein, 10 and houses bacteria capable of releasing inflammatory mediators. 10 The subsequent migration of α-synuclein to the CNS has been substantiated by studies showing a protective effect of a truncal vagotomy on PD development. 15 16 Compared with these reports, our study was underpowered to detect any consistent associations for vagotomy. 15 16

Finally, we attempted to assess the proximity of each diagnosis to the diagnosis of PD in the case-control study ( online supplemental figure 5 ). We found that the OR for dysphagia and constipation decreased considerably as the distance from the diagnosis of PD increased, while the OR for GP and IBS without diarrhoea remained relatively constant. This suggests that differences in lead time exist, but future longitudinal population based studies will be crucial to determine whether these PD specific GI syndromes are part of the early manifestation of PD or truly precede the disease. Importantly, the combination of two complementary study designs reduced the potential for selection bias. The case-control analysis consisted of patients with PD, AD or CVD without the requirement of any previous exposure, while the cohort analyses consisted of patients with newly diagnosed GI exposures without the requirement of a subsequent PD, AD or CVD diagnosis. This study is subject to intrinsic limitations of EHR data, including unknown completeness of records and absent validation of diagnoses. The multicentre character and inclusion of racially and ethnically diverse subjects ensured that these results are generalisable to patients at academic medical centres across the USA.

This study is the first to establish substantial observational evidence that the clinical diagnosis of not only constipation, but also dysphagia, GP and IBS without diarrhoea might specifically predict the development of PD, whereas other exposures were less specific. An appendectomy appeared protective, leading to further speculation about its role in PD pathophysiology. These findings warrant alertness for GI syndromes in patients at higher risk for PD and highlight the need for further investigation of GI precedents in AD and CVD. To establish a stronger body of clinicopathological evidence, we advocate for future studies to assess the sensitivity and specificity of these disorders and their clinicopathological correlates for the early detection of neuropathology.

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Acknowledgments

BK and JV were supported by a fellowship of the Belgian American Educational Foundation (BAEF). Parts of this work have been presented as a conference abstract at Digestive Disease Week 2022, San Diego, California, USA (doi: 10.1016/S0016-5085(22)60469-4).

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• Data supplement 1

Contributors BK: conceptualisation, methodology, formal analysis, data curation, validation, visualisation, writing-original draft, and writing-review and editing. LV: conceptualisation, resources, and investigation. JV: conceptualisation, methodology, and visualisation. GB: conceptualisation, resources, and investigation. RB: conceptualisation, resources, and investigation. MM: conceptualisation. KH: conceptualisation, methodology, and validation. GY: methodology and formal analysis. JT: writing-review and editing. PJP: guarantor, supervision, conceptualisation, methodology, validation, and writing-review and editing.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

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Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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• Published: 30 August 2023

Correction to: Metabolome expression in Eucryphia cordifolia populations: role of seasonality and ecological niche centrality hypothesis

• Camila Fuica-Carrasco   ORCID: orcid.org/0000-0003-4393-7816 1 ,
• Óscar Toro-Núñez   ORCID: orcid.org/0000-0001-6598-0511 2 ,
• Andrés Lira-Noriega   ORCID: orcid.org/0000-0002-3219-0019 3 ,
• Andy J. Pérez   ORCID: orcid.org/0000-0001-6717-2040 4 &
• Víctor Hernández   ORCID: orcid.org/0000-0001-5092-9713 1  

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Fuica-Carrasco, C., Toro-Núñez, Ó., Lira-Noriega, A. et al. Correction to: Metabolome expression in Eucryphia cordifolia populations: role of seasonality and ecological niche centrality hypothesis. J Plant Res (2023). https://doi.org/10.1007/s10265-023-01489-x

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