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• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

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 .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, 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 types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

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 ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize 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.

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

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.

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 .

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

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

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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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.

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.

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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."

Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

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.

On This Page:

A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

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

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

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

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

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

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

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)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist 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 provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

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

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

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization 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.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of 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.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on 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 a Friday afternoon and then measuring their immediate recall of 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.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

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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 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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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.

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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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16 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

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

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

It’s a counter-proposal to be proven as a rejection

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

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

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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.

• Research Process

Systematic Literature Review or Literature Review?

What is a Problem Statement? [with examples]

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How to write a research hypothesis

Last updated

19 January 2023

Reviewed by

Miroslav Damyanov

Start with a broad subject matter that excites you, so your curiosity will motivate your work. Conduct a literature search to determine the range of questions already addressed and spot any holes in the existing research.

Narrow the topics that interest you and determine your research question. Rather than focusing on a hole in the research, you might choose to challenge an existing assumption, a process called problematization. You may also find yourself with a short list of questions or related topics.

Use the FINER method to determine the single problem you'll address with your research. FINER stands for:

I nteresting

You need a feasible research question, meaning that there is a way to address the question. You should find it interesting, but so should a larger audience. Rather than repeating research that others have already conducted, your research hypothesis should test something novel or unique. 

The research must fall into accepted ethical parameters as defined by the government of your country and your university or college if you're an academic. You'll also need to come up with a relevant question since your research should provide a contribution to the existing research area.

This process typically narrows your shortlist down to a single problem you'd like to study and the variable you want to test. You're ready to write your hypothesis statements.

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• Types of research hypotheses

It is important to narrow your topic down to one idea before trying to write your research hypothesis. You'll only test one problem at a time. To do this, you'll write two hypotheses – a null hypothesis (H0) and an alternative hypothesis (Ha).

You'll come across many terms related to developing a research hypothesis or referring to a specific type of hypothesis. Let's take a quick look at these terms.

Null hypothesis

The term null hypothesis refers to a research hypothesis type that assumes no statistically significant relationship exists within a set of observations or data. It represents a claim that assumes that any observed relationship is due to chance. Represented as H0, the null represents the conjecture of the research.

Alternative hypothesis

The alternative hypothesis accompanies the null hypothesis. It states that the situation presented in the null hypothesis is false or untrue, and claims an observed effect in your test. This is typically denoted by Ha or H(n), where “n” stands for the number of alternative hypotheses. You can have more than one alternative hypothesis. 

Simple hypothesis

The term simple hypothesis refers to a hypothesis or theory that predicts the relationship between two variables - the independent (predictor) and the dependent (predicted). 

Complex hypothesis

The term complex hypothesis refers to a model – either quantitative (mathematical) or qualitative . A complex hypothesis states the surmised relationship between two or more potentially related variables.

Directional hypothesis

When creating a statistical hypothesis, the directional hypothesis (the null hypothesis) states an assumption regarding one parameter of a population. Some academics call this the “one-sided” hypothesis. The alternative hypothesis indicates whether the researcher tests for a positive or negative effect by including either the greater than (">") or less than ("<") sign.

Non-directional hypothesis

We refer to the alternative hypothesis in a statistical research question as a non-directional hypothesis. It includes the not equal ("≠") sign to show that the research tests whether or not an effect exists without specifying the effect's direction (positive or negative).

Associative hypothesis

The term associative hypothesis assumes a link between two variables but stops short of stating that one variable impacts the other. Academic statistical literature asserts in this sense that correlation does not imply causation. So, although the hypothesis notes the correlation between two variables – the independent and dependent - it does not predict how the two interact.

Logical hypothesis

Typically used in philosophy rather than science, researchers can't test a logical hypothesis because the technology or data set doesn't yet exist. A logical hypothesis uses logic as the basis of its assumptions. 

In some cases, a logical hypothesis can become an empirical hypothesis once technology provides an opportunity for testing. Until that time, the question remains too expensive or complex to address. Note that a logical hypothesis is not a statistical hypothesis.

Empirical hypothesis

When we consider the opposite of a logical hypothesis, we call this an empirical or working hypothesis. This type of hypothesis considers a scientifically measurable question. A researcher can consider and test an empirical hypothesis through replicable tests, observations, and measurements.

Statistical hypothesis

The term statistical hypothesis refers to a test of a theory that uses representative statistical models to test relationships between variables to draw conclusions regarding a large population. This requires an existing large data set, commonly referred to as big data, or implementing a survey to obtain original statistical information to form a data set for the study. 

Testing this type of hypothesis requires the use of random samples. Note that the null and alternative hypotheses are used in statistical hypothesis testing.

Causal hypothesis

The term causal hypothesis refers to a research hypothesis that tests a cause-and-effect relationship. A causal hypothesis is utilized when conducting experimental or quasi-experimental research.

Descriptive hypothesis

The term descriptive hypothesis refers to a research hypothesis used in non-experimental research, specifying an influence in the relationship between two variables.

• What makes an effective research hypothesis?

An effective research hypothesis offers a clearly defined, specific statement, using simple wording that contains no assumptions or generalizations, and that you can test. A well-written hypothesis should predict the tested relationship and its outcome. It contains zero ambiguity and offers results you can observe and test. 

The research hypothesis should address a question relevant to a research area. Overall, your research hypothesis needs the following essentials:

Hypothesis Essential #1: Specificity & Clarity

Hypothesis Essential #2: Testability (Provability)

• How to develop a good research hypothesis

In developing your hypothesis statements, you must pre-plan some of your statistical analysis. Once you decide on your problem to examine, determine three aspects:

the parameter you'll test

the test's direction (left-tailed, right-tailed, or non-directional)

the hypothesized parameter value

Any quantitative research includes a hypothesized parameter value of a mean, a proportion, or the difference between two proportions. Here's how to note each parameter:

Single mean (μ)

Paired means (μd)

Single proportion (p)

Difference between two independent means (μ1−μ2)

Difference between two proportions (p1−p2)

Simple linear regression slope (β)

Correlation (ρ)

Defining these parameters and determining whether you want to test the mean, proportion, or differences helps you determine the statistical tests you'll conduct to analyze your data. When writing your hypothesis, you only need to decide which parameter to test and in what overarching way.

The null research hypothesis must include everyday language, in a single sentence, stating the problem you want to solve. Write it as an if-then statement with defined variables. Write an alternative research hypothesis that states the opposite.

• What is the correct format for writing a hypothesis?

The following example shows the proper format and textual content of a hypothesis. It follows commonly accepted academic standards.

Null hypothesis (H0): High school students who participate in varsity sports as opposed to those who do not, fail to score higher on leadership tests than students who do not participate.

Alternative hypothesis (H1): High school students who play a varsity sport as opposed to those who do not participate in team athletics will score higher on leadership tests than students who do not participate in athletics.

The research question tests the correlation between varsity sports participation and leadership qualities expressed as a score on leadership tests. It compares the population of athletes to non-athletes.

• What are the five steps of a hypothesis?

Once you decide on the specific problem or question you want to address, you can write your research hypothesis. Use this five-step system to hone your null hypothesis and generate your alternative hypothesis.

Step 1 : Create your research question. This topic should interest and excite you; answering it provides relevant information to an industry or academic area.

Step 2 : Conduct a literature review to gather essential existing research.

Step 3 : Write a clear, strong, simply worded sentence that explains your test parameter, test direction, and hypothesized parameter.

Step 4 : Read it a few times. Have others read it and ask them what they think it means. Refine your statement accordingly until it becomes understandable to everyone. While not everyone can or will comprehend every research study conducted, any person from the general population should be able to read your hypothesis and alternative hypothesis and understand the essential question you want to answer.

Step 5 : Re-write your null hypothesis until it reads simply and understandably. Write your alternative hypothesis.

What is the Red Queen hypothesis?

Some hypotheses are well-known, such as the Red Queen hypothesis. Choose your wording carefully, since you could become like the famed scientist Dr. Leigh Van Valen. In 1973, Dr. Van Valen proposed the Red Queen hypothesis to describe coevolutionary activity, specifically reciprocal evolutionary effects between species to explain extinction rates in the fossil record. 

Essentially, Van Valen theorized that to survive, each species remains in a constant state of adaptation, evolution, and proliferation, and constantly competes for survival alongside other species doing the same. Only by doing this can a species avoid extinction. Van Valen took the hypothesis title from the Lewis Carroll book, "Through the Looking Glass," which contains a key character named the Red Queen who explains to Alice that for all of her running, she's merely running in place.

• Getting started with your research

In conclusion, once you write your null hypothesis (H0) and an alternative hypothesis (Ha), you’ve essentially authored the elevator pitch of your research. These two one-sentence statements describe your topic in simple, understandable terms that both professionals and laymen can understand. They provide the starting point of your research project.

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Sat / act prep online guides and tips, what is a hypothesis and how do i write one.

General Education

Think about something strange and unexplainable in your life. Maybe you get a headache right before it rains, or maybe you think your favorite sports team wins when you wear a certain color. If you wanted to see whether these are just coincidences or scientific fact, you would form a hypothesis, then create an experiment to see whether that hypothesis is true or not.

But what is a hypothesis, anyway? If you’re not sure about what a hypothesis is--or how to test for one!--you’re in the right place. This article will teach you everything you need to know about hypotheses, including: 

• Defining the term “hypothesis” 
• Providing hypothesis examples 
• Giving you tips for how to write your own hypothesis

So let’s get started!

What Is a Hypothesis?

Merriam Webster defines a hypothesis as “an assumption or concession made for the sake of argument.” In other words, a hypothesis is an educated guess . Scientists make a reasonable assumption--or a hypothesis--then design an experiment to test whether it’s true or not. Keep in mind that in science, a hypothesis should be testable. You have to be able to design an experiment that tests your hypothesis in order for it to be valid. 

As you could assume from that statement, it’s easy to make a bad hypothesis. But when you’re holding an experiment, it’s even more important that your guesses be good...after all, you’re spending time (and maybe money!) to figure out more about your observation. That’s why we refer to a hypothesis as an educated guess--good hypotheses are based on existing data and research to make them as sound as possible.

Hypotheses are one part of what’s called the scientific method .  Every (good) experiment or study is based in the scientific method. The scientific method gives order and structure to experiments and ensures that interference from scientists or outside influences does not skew the results. It’s important that you understand the concepts of the scientific method before holding your own experiment. Though it may vary among scientists, the scientific method is generally made up of six steps (in order):

• Observation
• Asking questions
• Forming a hypothesis
• Analyze the data
• Communicate your results

You’ll notice that the hypothesis comes pretty early on when conducting an experiment. That’s because experiments work best when they’re trying to answer one specific question. And you can’t conduct an experiment until you know what you’re trying to prove!

Independent and Dependent Variables 

After doing your research, you’re ready for another important step in forming your hypothesis: identifying variables. Variables are basically any factor that could influence the outcome of your experiment . Variables have to be measurable and related to the topic being studied.

There are two types of variables:  independent variables and dependent variables. I ndependent variables remain constant . For example, age is an independent variable; it will stay the same, and researchers can look at different ages to see if it has an effect on the dependent variable. 

Speaking of dependent variables... dependent variables are subject to the influence of the independent variable , meaning that they are not constant. Let’s say you want to test whether a person’s age affects how much sleep they need. In that case, the independent variable is age (like we mentioned above), and the dependent variable is how much sleep a person gets. 

Variables will be crucial in writing your hypothesis. You need to be able to identify which variable is which, as both the independent and dependent variables will be written into your hypothesis. For instance, in a study about exercise, the independent variable might be the speed at which the respondents walk for thirty minutes, and the dependent variable would be their heart rate. In your study and in your hypothesis, you’re trying to understand the relationship between the two variables.

Elements of a Good Hypothesis

The best hypotheses start by asking the right questions . For instance, if you’ve observed that the grass is greener when it rains twice a week, you could ask what kind of grass it is, what elevation it’s at, and if the grass across the street responds to rain in the same way. Any of these questions could become the backbone of experiments to test why the grass gets greener when it rains fairly frequently.

As you’re asking more questions about your first observation, make sure you’re also making more observations . If it doesn’t rain for two weeks and the grass still looks green, that’s an important observation that could influence your hypothesis. You'll continue observing all throughout your experiment, but until the hypothesis is finalized, every observation should be noted.

Finally, you should consult secondary research before writing your hypothesis . Secondary research is comprised of results found and published by other people. You can usually find this information online or at your library. Additionally, m ake sure the research you find is credible and related to your topic. If you’re studying the correlation between rain and grass growth, it would help you to research rain patterns over the past twenty years for your county, published by a local agricultural association. You should also research the types of grass common in your area, the type of grass in your lawn, and whether anyone else has conducted experiments about your hypothesis. Also be sure you’re checking the quality of your research . Research done by a middle school student about what minerals can be found in rainwater would be less useful than an article published by a local university.

Writing Your Hypothesis

Once you’ve considered all of the factors above, you’re ready to start writing your hypothesis. Hypotheses usually take a certain form when they’re written out in a research report.

When you boil down your hypothesis statement, you are writing down your best guess and not the question at hand . This means that your statement should be written as if it is fact already, even though you are simply testing it.

The reason for this is that, after you have completed your study, you'll either accept or reject your if-then or your null hypothesis. All hypothesis testing examples should be measurable and able to be confirmed or denied. You cannot confirm a question, only a statement! 

In fact, you come up with hypothesis examples all the time! For instance, when you guess on the outcome of a basketball game, you don’t say, “Will the Miami Heat beat the Boston Celtics?” but instead, “I think the Miami Heat will beat the Boston Celtics.” You state it as if it is already true, even if it turns out you’re wrong. You do the same thing when writing your hypothesis.

Additionally, keep in mind that hypotheses can range from very specific to very broad.  These hypotheses can be specific, but if your hypothesis testing examples involve a broad range of causes and effects, your hypothesis can also be broad.  

The Two Types of Hypotheses

Now that you understand what goes into a hypothesis, it’s time to look more closely at the two most common types of hypothesis: the if-then hypothesis and the null hypothesis.

#1: If-Then Hypotheses

First of all, if-then hypotheses typically follow this formula:

If ____ happens, then ____ will happen.

The goal of this type of hypothesis is to test the causal relationship between the independent and dependent variable. It’s fairly simple, and each hypothesis can vary in how detailed it can be. We create if-then hypotheses all the time with our daily predictions. Here are some examples of hypotheses that use an if-then structure from daily life: 

• If I get enough sleep, I’ll be able to get more work done tomorrow.
• If the bus is on time, I can make it to my friend’s birthday party. 
• If I study every night this week, I’ll get a better grade on my exam. 

In each of these situations, you’re making a guess on how an independent variable (sleep, time, or studying) will affect a dependent variable (the amount of work you can do, making it to a party on time, or getting better grades). 

You may still be asking, “What is an example of a hypothesis used in scientific research?” Take one of the hypothesis examples from a real-world study on whether using technology before bed affects children’s sleep patterns. The hypothesis read s:

“We hypothesized that increased hours of tablet- and phone-based screen time at bedtime would be inversely correlated with sleep quality and child attention.”

It might not look like it, but this is an if-then statement. The researchers basically said, “If children have more screen usage at bedtime, then their quality of sleep and attention will be worse.” The sleep quality and attention are the dependent variables and the screen usage is the independent variable. (Usually, the independent variable comes after the “if” and the dependent variable comes after the “then,” as it is the independent variable that affects the dependent variable.) This is an excellent example of how flexible hypothesis statements can be, as long as the general idea of “if-then” and the independent and dependent variables are present.

#2: Null Hypotheses

Your if-then hypothesis is not the only one needed to complete a successful experiment, however. You also need a null hypothesis to test it against. In its most basic form, the null hypothesis is the opposite of your if-then hypothesis . When you write your null hypothesis, you are writing a hypothesis that suggests that your guess is not true, and that the independent and dependent variables have no relationship .

One null hypothesis for the cell phone and sleep study from the last section might say: 

“If children have more screen usage at bedtime, their quality of sleep and attention will not be worse.” 

In this case, this is a null hypothesis because it’s asking the opposite of the original thesis! 

Conversely, if your if-then hypothesis suggests that your two variables have no relationship, then your null hypothesis would suggest that there is one. So, pretend that there is a study that is asking the question, “Does the amount of followers on Instagram influence how long people spend on the app?” The independent variable is the amount of followers, and the dependent variable is the time spent. But if you, as the researcher, don’t think there is a relationship between the number of followers and time spent, you might write an if-then hypothesis that reads:

“If people have many followers on Instagram, they will not spend more time on the app than people who have less.”

In this case, the if-then suggests there isn’t a relationship between the variables. In that case, one of the null hypothesis examples might say:

“If people have many followers on Instagram, they will spend more time on the app than people who have less.”

You then test both the if-then and the null hypothesis to gauge if there is a relationship between the variables, and if so, how much of a relationship. 

4 Tips to Write the Best Hypothesis

If you’re going to take the time to hold an experiment, whether in school or by yourself, you’re also going to want to take the time to make sure your hypothesis is a good one. The best hypotheses have four major elements in common: plausibility, defined concepts, observability, and general explanation.

#1: Plausibility

At first glance, this quality of a hypothesis might seem obvious. When your hypothesis is plausible, that means it’s possible given what we know about science and general common sense. However, improbable hypotheses are more common than you might think. 

Imagine you’re studying weight gain and television watching habits. If you hypothesize that people who watch more than  twenty hours of television a week will gain two hundred pounds or more over the course of a year, this might be improbable (though it’s potentially possible). Consequently, c ommon sense can tell us the results of the study before the study even begins.

Improbable hypotheses generally go against  science, as well. Take this hypothesis example: 

“If a person smokes one cigarette a day, then they will have lungs just as healthy as the average person’s.” 

This hypothesis is obviously untrue, as studies have shown again and again that cigarettes negatively affect lung health. You must be careful that your hypotheses do not reflect your own personal opinion more than they do scientifically-supported findings. This plausibility points to the necessity of research before the hypothesis is written to make sure that your hypothesis has not already been disproven.

#2: Defined Concepts

The more advanced you are in your studies, the more likely that the terms you’re using in your hypothesis are specific to a limited set of knowledge. One of the hypothesis testing examples might include the readability of printed text in newspapers, where you might use words like “kerning” and “x-height.” Unless your readers have a background in graphic design, it’s likely that they won’t know what you mean by these terms. Thus, it’s important to either write what they mean in the hypothesis itself or in the report before the hypothesis.

Here’s what we mean. Which of the following sentences makes more sense to the common person?

If the kerning is greater than average, more words will be read per minute.

If the space between letters is greater than average, more words will be read per minute.

For people reading your report that are not experts in typography, simply adding a few more words will be helpful in clarifying exactly what the experiment is all about. It’s always a good idea to make your research and findings as accessible as possible. 

Good hypotheses ensure that you can observe the results. 

#3: Observability

In order to measure the truth or falsity of your hypothesis, you must be able to see your variables and the way they interact. For instance, if your hypothesis is that the flight patterns of satellites affect the strength of certain television signals, yet you don’t have a telescope to view the satellites or a television to monitor the signal strength, you cannot properly observe your hypothesis and thus cannot continue your study.

Some variables may seem easy to observe, but if you do not have a system of measurement in place, you cannot observe your hypothesis properly. Here’s an example: if you’re experimenting on the effect of healthy food on overall happiness, but you don’t have a way to monitor and measure what “overall happiness” means, your results will not reflect the truth. Monitoring how often someone smiles for a whole day is not reasonably observable, but having the participants state how happy they feel on a scale of one to ten is more observable. 

In writing your hypothesis, always keep in mind how you'll execute the experiment.

#4: Generalizability 

Perhaps you’d like to study what color your best friend wears the most often by observing and documenting the colors she wears each day of the week. This might be fun information for her and you to know, but beyond you two, there aren’t many people who could benefit from this experiment. When you start an experiment, you should note how generalizable your findings may be if they are confirmed. Generalizability is basically how common a particular phenomenon is to other people’s everyday life.

Let’s say you’re asking a question about the health benefits of eating an apple for one day only, you need to realize that the experiment may be too specific to be helpful. It does not help to explain a phenomenon that many people experience. If you find yourself with too specific of a hypothesis, go back to asking the big question: what is it that you want to know, and what do you think will happen between your two variables?

Hypothesis Testing Examples

We know it can be hard to write a good hypothesis unless you’ve seen some good hypothesis examples. We’ve included four hypothesis examples based on some made-up experiments. Use these as templates or launch pads for coming up with your own hypotheses.

Experiment #1: Students Studying Outside (Writing a Hypothesis)

You are a student at PrepScholar University. When you walk around campus, you notice that, when the temperature is above 60 degrees, more students study in the quad. You want to know when your fellow students are more likely to study outside. With this information, how do you make the best hypothesis possible?

You must remember to make additional observations and do secondary research before writing your hypothesis. In doing so, you notice that no one studies outside when it’s 75 degrees and raining, so this should be included in your experiment. Also, studies done on the topic beforehand suggested that students are more likely to study in temperatures less than 85 degrees. With this in mind, you feel confident that you can identify your variables and write your hypotheses:

If-then: “If the temperature in Fahrenheit is less than 60 degrees, significantly fewer students will study outside.”

Null: “If the temperature in Fahrenheit is less than 60 degrees, the same number of students will study outside as when it is more than 60 degrees.”

These hypotheses are plausible, as the temperatures are reasonably within the bounds of what is possible. The number of people in the quad is also easily observable. It is also not a phenomenon specific to only one person or at one time, but instead can explain a phenomenon for a broader group of people.

To complete this experiment, you pick the month of October to observe the quad. Every day (except on the days where it’s raining)from 3 to 4 PM, when most classes have released for the day, you observe how many people are on the quad. You measure how many people come  and how many leave. You also write down the temperature on the hour. 

After writing down all of your observations and putting them on a graph, you find that the most students study on the quad when it is 70 degrees outside, and that the number of students drops a lot once the temperature reaches 60 degrees or below. In this case, your research report would state that you accept or “failed to reject” your first hypothesis with your findings.

Experiment #2: The Cupcake Store (Forming a Simple Experiment)

Let’s say that you work at a bakery. You specialize in cupcakes, and you make only two colors of frosting: yellow and purple. You want to know what kind of customers are more likely to buy what kind of cupcake, so you set up an experiment. Your independent variable is the customer’s gender, and the dependent variable is the color of the frosting. What is an example of a hypothesis that might answer the question of this study?

Here’s what your hypotheses might look like: 

If-then: “If customers’ gender is female, then they will buy more yellow cupcakes than purple cupcakes.”

Null: “If customers’ gender is female, then they will be just as likely to buy purple cupcakes as yellow cupcakes.”

This is a pretty simple experiment! It passes the test of plausibility (there could easily be a difference), defined concepts (there’s nothing complicated about cupcakes!), observability (both color and gender can be easily observed), and general explanation ( this would potentially help you make better business decisions ).

Experiment #3: Backyard Bird Feeders (Integrating Multiple Variables and Rejecting the If-Then Hypothesis)

While watching your backyard bird feeder, you realized that different birds come on the days when you change the types of seeds. You decide that you want to see more cardinals in your backyard, so you decide to see what type of food they like the best and set up an experiment. 

However, one morning, you notice that, while some cardinals are present, blue jays are eating out of your backyard feeder filled with millet. You decide that, of all of the other birds, you would like to see the blue jays the least. This means you'll have more than one variable in your hypothesis. Your new hypotheses might look like this: 

If-then: “If sunflower seeds are placed in the bird feeders, then more cardinals will come than blue jays. If millet is placed in the bird feeders, then more blue jays will come than cardinals.”

Null: “If either sunflower seeds or millet are placed in the bird, equal numbers of cardinals and blue jays will come.”

Through simple observation, you actually find that cardinals come as often as blue jays when sunflower seeds or millet is in the bird feeder. In this case, you would reject your “if-then” hypothesis and “fail to reject” your null hypothesis . You cannot accept your first hypothesis, because it’s clearly not true. Instead you found that there was actually no relation between your different variables. Consequently, you would need to run more experiments with different variables to see if the new variables impact the results.

Experiment #4: In-Class Survey (Including an Alternative Hypothesis)

You’re about to give a speech in one of your classes about the importance of paying attention. You want to take this opportunity to test a hypothesis you’ve had for a while: 

If-then: If students sit in the first two rows of the classroom, then they will listen better than students who do not.

Null: If students sit in the first two rows of the classroom, then they will not listen better or worse than students who do not.

You give your speech and then ask your teacher if you can hand out a short survey to the class. On the survey, you’ve included questions about some of the topics you talked about. When you get back the results, you’re surprised to see that not only do the students in the first two rows not pay better attention, but they also scored worse than students in other parts of the classroom! Here, both your if-then and your null hypotheses are not representative of your findings. What do you do?

This is when you reject both your if-then and null hypotheses and instead create an alternative hypothesis . This type of hypothesis is used in the rare circumstance that neither of your hypotheses is able to capture your findings . Now you can use what you’ve learned to draft new hypotheses and test again! 

Key Takeaways: Hypothesis Writing

The more comfortable you become with writing hypotheses, the better they will become. The structure of hypotheses is flexible and may need to be changed depending on what topic you are studying. The most important thing to remember is the purpose of your hypothesis and the difference between the if-then and the null . From there, in forming your hypothesis, you should constantly be asking questions, making observations, doing secondary research, and considering your variables. After you have written your hypothesis, be sure to edit it so that it is plausible, clearly defined, observable, and helpful in explaining a general phenomenon.

Writing a hypothesis is something that everyone, from elementary school children competing in a science fair to professional scientists in a lab, needs to know how to do. Hypotheses are vital in experiments and in properly executing the scientific method . When done correctly, hypotheses will set up your studies for success and help you to understand the world a little better, one experiment at a time.

What’s Next?

If you’re studying for the science portion of the ACT, there’s definitely a lot you need to know. We’ve got the tools to help, though! Start by checking out our ultimate study guide for the ACT Science subject test. Once you read through that, be sure to download our recommended ACT Science practice tests , since they’re one of the most foolproof ways to improve your score. (And don’t forget to check out our expert guide book , too.)

If you love science and want to major in a scientific field, you should start preparing in high school . Here are the science classes you should take to set yourself up for success.

If you’re trying to think of science experiments you can do for class (or for a science fair!), here’s a list of 37 awesome science experiments you can do at home

Ashley Sufflé Robinson has a Ph.D. in 19th Century English Literature. As a content writer for PrepScholar, Ashley is passionate about giving college-bound students the in-depth information they need to get into the school of their dreams.

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Research Questions & Hypotheses

Generally, in quantitative studies, reviewers expect hypotheses rather than research questions. However, both research questions and hypotheses serve different purposes and can be beneficial when used together.

Research Questions

Clarify the research’s aim (farrugia et al., 2010).

• Research often begins with an interest in a topic, but a deep understanding of the subject is crucial to formulate an appropriate research question.
• Descriptive: “What factors most influence the academic achievement of senior high school students?”
• Comparative: “What is the performance difference between teaching methods A and B?”
• Relationship-based: “What is the relationship between self-efficacy and academic achievement?”
• Increasing knowledge about a subject can be achieved through systematic literature reviews, in-depth interviews with patients (and proxies), focus groups, and consultations with field experts.
• Some funding bodies, like the Canadian Institute for Health Research, recommend conducting a systematic review or a pilot study before seeking grants for full trials.
• The presence of multiple research questions in a study can complicate the design, statistical analysis, and feasibility.
• It’s advisable to focus on a single primary research question for the study.
• The primary question, clearly stated at the end of a grant proposal’s introduction, usually specifies the study population, intervention, and other relevant factors.
• The FINER criteria underscore aspects that can enhance the chances of a successful research project, including specifying the population of interest, aligning with scientific and public interest, clinical relevance, and contribution to the field, while complying with ethical and national research standards.
• The P ICOT approach is crucial in developing the study’s framework and protocol, influencing inclusion and exclusion criteria and identifying patient groups for inclusion.
• Defining the specific population, intervention, comparator, and outcome helps in selecting the right outcome measurement tool.
• The more precise the population definition and stricter the inclusion and exclusion criteria, the more significant the impact on the interpretation, applicability, and generalizability of the research findings.
• A restricted study population enhances internal validity but may limit the study’s external validity and generalizability to clinical practice.
• A broadly defined study population may better reflect clinical practice but could increase bias and reduce internal validity.
• An inadequately formulated research question can negatively impact study design, potentially leading to ineffective outcomes and affecting publication prospects.

Checklist: Good research questions for social science projects (Panke, 2018)

Research Hypotheses

Present the researcher’s predictions based on specific statements.

• These statements define the research problem or issue and indicate the direction of the researcher’s predictions.
• Formulating the research question and hypothesis from existing data (e.g., a database) can lead to multiple statistical comparisons and potentially spurious findings due to chance.
• The research or clinical hypothesis, derived from the research question, shapes the study’s key elements: sampling strategy, intervention, comparison, and outcome variables.
• Hypotheses can express a single outcome or multiple outcomes.
• After statistical testing, the null hypothesis is either rejected or not rejected based on whether the study’s findings are statistically significant.
• Hypothesis testing helps determine if observed findings are due to true differences and not chance.
• Hypotheses can be 1-sided (specific direction of difference) or 2-sided (presence of a difference without specifying direction).
• 2-sided hypotheses are generally preferred unless there’s a strong justification for a 1-sided hypothesis.
• A solid research hypothesis, informed by a good research question, influences the research design and paves the way for defining clear research objectives.

Types of Research Hypothesis

• In a Y-centered research design, the focus is on the dependent variable (DV) which is specified in the research question. Theories are then used to identify independent variables (IV) and explain their causal relationship with the DV.
• Example: “An increase in teacher-led instructional time (IV) is likely to improve student reading comprehension scores (DV), because extensive guided practice under expert supervision enhances learning retention and skill mastery.”
• Hypothesis Explanation: The dependent variable (student reading comprehension scores) is the focus, and the hypothesis explores how changes in the independent variable (teacher-led instructional time) affect it.
• In X-centered research designs, the independent variable is specified in the research question. Theories are used to determine potential dependent variables and the causal mechanisms at play.
• Example: “Implementing technology-based learning tools (IV) is likely to enhance student engagement in the classroom (DV), because interactive and multimedia content increases student interest and participation.”
• Hypothesis Explanation: The independent variable (technology-based learning tools) is the focus, with the hypothesis exploring its impact on a potential dependent variable (student engagement).
• Probabilistic hypotheses suggest that changes in the independent variable are likely to lead to changes in the dependent variable in a predictable manner, but not with absolute certainty.
• Example: “The more teachers engage in professional development programs (IV), the more their teaching effectiveness (DV) is likely to improve, because continuous training updates pedagogical skills and knowledge.”
• Hypothesis Explanation: This hypothesis implies a probable relationship between the extent of professional development (IV) and teaching effectiveness (DV).
• Deterministic hypotheses state that a specific change in the independent variable will lead to a specific change in the dependent variable, implying a more direct and certain relationship.
• Example: “If the school curriculum changes from traditional lecture-based methods to project-based learning (IV), then student collaboration skills (DV) are expected to improve because project-based learning inherently requires teamwork and peer interaction.”
• Hypothesis Explanation: This hypothesis presumes a direct and definite outcome (improvement in collaboration skills) resulting from a specific change in the teaching method.
• Example : “Students who identify as visual learners will score higher on tests that are presented in a visually rich format compared to tests presented in a text-only format.”
• Explanation : This hypothesis aims to describe the potential difference in test scores between visual learners taking visually rich tests and text-only tests, without implying a direct cause-and-effect relationship.
• Example : “Teaching method A will improve student performance more than method B.”
• Explanation : This hypothesis compares the effectiveness of two different teaching methods, suggesting that one will lead to better student performance than the other. It implies a direct comparison but does not necessarily establish a causal mechanism.
• Example : “Students with higher self-efficacy will show higher levels of academic achievement.”
• Explanation : This hypothesis predicts a relationship between the variable of self-efficacy and academic achievement. Unlike a causal hypothesis, it does not necessarily suggest that one variable causes changes in the other, but rather that they are related in some way.

Tips for developing research questions and hypotheses for research studies

• Perform a systematic literature review (if one has not been done) to increase knowledge and familiarity with the topic and to assist with research development.
• Learn about current trends and technological advances on the topic.
• Seek careful input from experts, mentors, colleagues, and collaborators to refine your research question as this will aid in developing the research question and guide the research study.
• Use the FINER criteria in the development of the research question.
• Ensure that the research question follows PICOT format.
• Develop a research hypothesis from the research question.
• Ensure that the research question and objectives are answerable, feasible, and clinically relevant.

If your research hypotheses are derived from your research questions, particularly when multiple hypotheses address a single question, it’s recommended to use both research questions and hypotheses. However, if this isn’t the case, using hypotheses over research questions is advised. It’s important to note these are general guidelines, not strict rules. If you opt not to use hypotheses, consult with your supervisor for the best approach.

Farrugia, P., Petrisor, B. A., Farrokhyar, F., & Bhandari, M. (2010). Practical tips for surgical research: Research questions, hypotheses and objectives.  Canadian journal of surgery. Journal canadien de chirurgie ,  53 (4), 278–281.

Hulley, S. B., Cummings, S. R., Browner, W. S., Grady, D., & Newman, T. B. (2007). Designing clinical research. Philadelphia.

Panke, D. (2018). Research design & method selection: Making good choices in the social sciences.  Research Design & Method Selection , 1-368.

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An hypothesis is a specific statement of prediction. It describes in concrete (rather than theoretical) terms what you expect will happen in your study. Not all studies have hypotheses. Sometimes a study is designed to be exploratory (see inductive research ). There is no formal hypothesis, and perhaps the purpose of the study is to explore some area more thoroughly in order to develop some specific hypothesis or prediction that can be tested in future research. A single study may have one or many hypotheses.

Actually, whenever I talk about an hypothesis, I am really thinking simultaneously about two hypotheses. Let’s say that you predict that there will be a relationship between two variables in your study. The way we would formally set up the hypothesis test is to formulate two hypothesis statements, one that describes your prediction and one that describes all the other possible outcomes with respect to the hypothesized relationship. Your prediction is that variable A and variable B will be related (you don’t care whether it’s a positive or negative relationship). Then the only other possible outcome would be that variable A and variable B are not related. Usually, we call the hypothesis that you support (your prediction) the alternative hypothesis, and we call the hypothesis that describes the remaining possible outcomes the null hypothesis. Sometimes we use a notation like HA or H1 to represent the alternative hypothesis or your prediction, and HO or H0 to represent the null case. You have to be careful here, though. In some studies, your prediction might very well be that there will be no difference or change. In this case, you are essentially trying to find support for the null hypothesis and you are opposed to the alternative.

If your prediction specifies a direction, and the null therefore is the no difference prediction and the prediction of the opposite direction, we call this a one-tailed hypothesis . For instance, let’s imagine that you are investigating the effects of a new employee training program and that you believe one of the outcomes will be that there will be less employee absenteeism. Your two hypotheses might be stated something like this:

The null hypothesis for this study is:

HO: As a result of the XYZ company employee training program, there will either be no significant difference in employee absenteeism or there will be a significant increase .

which is tested against the alternative hypothesis:

HA: As a result of the XYZ company employee training program, there will be a significant decrease in employee absenteeism.

In the figure on the left, we see this situation illustrated graphically. The alternative hypothesis – your prediction that the program will decrease absenteeism – is shown there. The null must account for the other two possible conditions: no difference, or an increase in absenteeism. The figure shows a hypothetical distribution of absenteeism differences. We can see that the term “one-tailed” refers to the tail of the distribution on the outcome variable.

When your prediction does not specify a direction, we say you have a two-tailed hypothesis . For instance, let’s assume you are studying a new drug treatment for depression. The drug has gone through some initial animal trials, but has not yet been tested on humans. You believe (based on theory and the previous research) that the drug will have an effect, but you are not confident enough to hypothesize a direction and say the drug will reduce depression (after all, you’ve seen more than enough promising drug treatments come along that eventually were shown to have severe side effects that actually worsened symptoms). In this case, you might state the two hypotheses like this:

HO: As a result of 300mg./day of the ABC drug, there will be no significant difference in depression.

HA: As a result of 300mg./day of the ABC drug, there will be a significant difference in depression.

The figure on the right illustrates this two-tailed prediction for this case. Again, notice that the term “two-tailed” refers to the tails of the distribution for your outcome variable.

The important thing to remember about stating hypotheses is that you formulate your prediction (directional or not), and then you formulate a second hypothesis that is mutually exclusive of the first and incorporates all possible alternative outcomes for that case. When your study analysis is completed, the idea is that you will have to choose between the two hypotheses. If your prediction was correct, then you would (usually) reject the null hypothesis and accept the alternative. If your original prediction was not supported in the data, then you will accept the null hypothesis and reject the alternative. The logic of hypothesis testing is based on these two basic principles:

• the formulation of two mutually exclusive hypothesis statements that, together, exhaust all possible outcomes
• the testing of these so that one is necessarily accepted and the other rejected

OK, I know it’s a convoluted, awkward and formalistic way to ask research questions. But it encompasses a long tradition in statistics called the hypothetical-deductive model , and sometimes we just have to do things because they’re traditions. And anyway, if all of this hypothesis testing was easy enough so anybody could understand it, how do you think statisticians would stay employed?

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5.2 - writing hypotheses.

The first step in conducting a hypothesis test is to write the hypothesis statements that are going to be tested. For each test you will have a null hypothesis (\(H_0\)) and an alternative hypothesis (\(H_a\)).

When writing hypotheses there are three things that we need to know: (1) the parameter that we are testing (2) the direction of the test (non-directional, right-tailed or left-tailed), and (3) the value of the hypothesized parameter.

• At this point we can write hypotheses for a single mean (\(\mu\)), paired means(\(\mu_d\)), a single proportion (\(p\)), the difference between two independent means (\(\mu_1-\mu_2\)), the difference between two proportions (\(p_1-p_2\)), a simple linear regression slope (\(\beta\)), and a correlation (\(\rho\)). 
• The research question will give us the information necessary to determine if the test is two-tailed (e.g., "different from," "not equal to"), right-tailed (e.g., "greater than," "more than"), or left-tailed (e.g., "less than," "fewer than").
• The research question will also give us the hypothesized parameter value. This is the number that goes in the hypothesis statements (i.e., \(\mu_0\) and \(p_0\)). For the difference between two groups, regression, and correlation, this value is typically 0.

Hypotheses are always written in terms of population parameters (e.g., \(p\) and \(\mu\)).  The tables below display all of the possible hypotheses for the parameters that we have learned thus far. Note that the null hypothesis always includes the equality (i.e., =).

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• Published: 01 April 2024

Complexity of avian evolution revealed by family-level genomes

• Josefin Stiller   ORCID: orcid.org/0000-0001-6009-9581 1 ,
• Shaohong Feng   ORCID: orcid.org/0000-0002-2462-7348 2 , 3 , 4 , 5 ,
• Al-Aabid Chowdhury 6 ,
• Iker Rivas-González   ORCID: orcid.org/0000-0002-0515-0628 7 ,
• David A. Duchêne   ORCID: orcid.org/0000-0002-5479-1974 8 ,
• Qi Fang   ORCID: orcid.org/0000-0002-9181-8689 9 ,
• Yuan Deng 9 ,
• Alexey Kozlov   ORCID: orcid.org/0000-0001-7394-2718 10 ,
• Alexandros Stamatakis   ORCID: orcid.org/0000-0003-0353-0691 10 , 11 , 12 ,
• Santiago Claramunt   ORCID: orcid.org/0000-0002-8926-5974 13 , 14 ,
• Jacqueline M. T. Nguyen   ORCID: orcid.org/0000-0002-3076-0006 15 , 16 ,
• Simon Y. W. Ho   ORCID: orcid.org/0000-0002-0361-2307 6 ,
• Brant C. Faircloth   ORCID: orcid.org/0000-0002-1943-0217 17 ,
• Julia Haag   ORCID: orcid.org/0000-0002-7493-3917 10 ,
• Peter Houde   ORCID: orcid.org/0000-0003-4541-5974 18 ,
• Joel Cracraft   ORCID: orcid.org/0000-0001-7587-8342 19 ,
• Metin Balaban 20 ,
• Uyen Mai 21 ,
• Guangji Chen   ORCID: orcid.org/0000-0002-9441-1155 9 , 22 ,
• Rongsheng Gao 9 , 22 ,
• Chengran Zhou   ORCID: orcid.org/0000-0002-9468-5973 9 ,
• Yulong Xie 2 ,
• Zijian Huang 2 ,
• Zhen Cao 23 ,
• Zhi Yan   ORCID: orcid.org/0000-0003-2433-5553 23 ,
• Huw A. Ogilvie   ORCID: orcid.org/0000-0003-1589-6885 23 ,
• Luay Nakhleh   ORCID: orcid.org/0000-0003-3288-6769 23 ,
• Bent Lindow   ORCID: orcid.org/0000-0002-1864-4221 24 ,
• Benoit Morel 10 , 11 ,
• Jon Fjeldså   ORCID: orcid.org/0000-0003-0790-3600 24 ,
• Peter A. Hosner   ORCID: orcid.org/0000-0001-7499-6224 24 , 25 ,
• Rute R. da Fonseca   ORCID: orcid.org/0000-0002-2805-4698 25 ,
• Bent Petersen   ORCID: orcid.org/0000-0002-2472-8317 8 , 26 ,
• Joseph A. Tobias   ORCID: orcid.org/0000-0003-2429-6179 27 ,
• Tamás Székely   ORCID: orcid.org/0000-0003-2093-0056 28 , 29 ,
• Jonathan David Kennedy 30 ,
• Andrew Hart Reeve   ORCID: orcid.org/0000-0001-5233-6030 24 ,
• Andras Liker 31 , 32 ,
• Martin Stervander   ORCID: orcid.org/0000-0002-6139-7828 33 ,
• Agostinho Antunes   ORCID: orcid.org/0000-0002-1328-1732 34 , 35 ,
• Dieter Thomas Tietze   ORCID: orcid.org/0000-0001-6868-227X 36 ,
• Mads Bertelsen 37 ,
• Fumin Lei   ORCID: orcid.org/0000-0001-9920-8167 38 , 39 ,
• Carsten Rahbek   ORCID: orcid.org/0000-0003-4585-0300 25 , 30 , 40 , 41 ,
• Gary R. Graves   ORCID: orcid.org/0000-0003-1406-5246 30 , 42 ,
• Mikkel H. Schierup   ORCID: orcid.org/0000-0002-5028-1790 7 ,
• Tandy Warnow 43 ,
• Edward L. Braun   ORCID: orcid.org/0000-0003-1643-5212 44 ,
• M. Thomas P. Gilbert   ORCID: orcid.org/0000-0002-5805-7195 8 , 45 ,
• Erich D. Jarvis 46 , 47 ,
• Siavash Mirarab   ORCID: orcid.org/0000-0001-5410-1518 48 &
• Guojie Zhang   ORCID: orcid.org/0000-0001-6860-1521 2 , 3 , 5 , 49  

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• Evolutionary biology
• Genome evolution
• Molecular evolution
• Phylogenetics

Despite tremendous efforts in the past decades, relationships among main avian lineages remain heavily debated without a clear resolution. Discrepancies have been attributed to diversity of species sampled, phylogenetic method, and the choice of genomic regions 1–3 . Here, we address these issues by analyzing genomes of 363 bird species 4 (218 taxonomic families, 92% of total). Using intergenic regions and coalescent methods, we present a well-supported tree but also a remarkable degree of discordance. The tree confirms that Neoaves experienced rapid radiation at or near the Cretaceous–Paleogene (K–Pg) boundary. Sufficient loci rather than extensive taxon sampling were more effective in resolving difficult nodes. Remaining recalcitrant nodes involve species that challenge modeling due to extreme GC content, variable substitution rates, incomplete lineage sorting, or complex evolutionary events such as ancient hybridization. Assessment of the impacts of different genomic partitions showed high heterogeneity across the genome. We discovered sharp increases in effective population size, substitution rates, and relative brain size following the K–Pg extinction event, supporting the hypothesis that emerging ecological opportunities catalyzed the diversification of modern birds. The resulting phylogenetic estimate offers novel insights into the rapid radiation of modern birds and provides a taxon-rich backbone tree for future comparative studies.

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Section for Ecology and Evolution, Department of Biology, University of Copenhagen, Copenhagen, Denmark

Josefin Stiller

Center for Evolutionary & Organismal Biology, & Women’s Hospital, Zhejiang University School of Medicine, Hangzhou, China

Shaohong Feng, Yulong Xie, Zijian Huang & Guojie Zhang

Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China

Shaohong Feng & Guojie Zhang

Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China

Shaohong Feng

Innovation Center of Yangtze River Delta, Zhejiang University, Jiashan, China

School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia

Al-Aabid Chowdhury & Simon Y. W. Ho

Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark

Iker Rivas-González & Mikkel H. Schierup

Center for Evolutionary Hologenomics, The Globe Institute, University of Copenhagen, Copenhagen, Denmark

David A. Duchêne, Bent Petersen & M. Thomas P. Gilbert

BGI-Shenzhen, Beishan Industrial Zone, Shenzhen, China

Qi Fang, Yuan Deng, Guangji Chen, Rongsheng Gao & Chengran Zhou

Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany

Alexey Kozlov, Alexandros Stamatakis, Julia Haag & Benoit Morel

Institute of Computer Science, Foundation for Research and Technology Hellas, Heraklion, Greece

Alexandros Stamatakis & Benoit Morel

Institute for Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany

Alexandros Stamatakis

Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada

Santiago Claramunt

Department of Natural History, Royal Ontario Museum, Toronto, Ontario, Canada

College of Science and Engineering, Flinders University, Bedford Park, South Australia, Australia

Jacqueline M. T. Nguyen

Research Institute, Australian Museum, Sydney, New South Wales, Australia

Department of Biological Sciences and Museum of Natural Science, Louisiana State University, Baton Rouge, LA, USA

Brant C. Faircloth

Department of Biology, New Mexico State University, Las Cruces, NM, USA

Peter Houde

Department of Ornithology, American Museum of Natural History, New York, NY, USA

Joel Cracraft

Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA, USA

Metin Balaban

Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA

College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China

Guangji Chen & Rongsheng Gao

Department of Computer Science, Rice University, Houston, TX, USA

Zhen Cao, Zhi Yan, Huw A. Ogilvie & Luay Nakhleh

Natural History Museum Denmark, University of Copenhagen, Copenhagen, Denmark

Bent Lindow, Jon Fjeldså, Peter A. Hosner & Andrew Hart Reeve

Center for Global Mountain Biodiversity, Globe Institute, University of Copenhagen, Copenhagen, Denmark

Peter A. Hosner, Rute R. da Fonseca & Carsten Rahbek

Centre of Excellence for Omics-Driven Computational Biodiscovery (COMBio), Faculty of Applied Sciences, AIMST University, Bedong, Kedah, Malaysia

Bent Petersen

Department of Life Sciences, Imperial College London, Silwood Park, Ascot, UK

Joseph A. Tobias

Milner Centre for Evolution, University of Bath, Bath, UK

Tamás Székely

ELKH-DE Reproductive Strategies Research Group, University of Debrecen, Debrecen, Hungary

Center for Macroecology, Evolution, and Climate, The Globe Institute, University of Copenhagen, Copenhagen, Denmark

Jonathan David Kennedy, Carsten Rahbek & Gary R. Graves

HUN-REN-PE Evolutionary Ecology Research Group, University of Pannonia, Veszprém, Hungary

Andras Liker

Behavioural Ecology Research Group, Center for Natural Sciences, University of Pannonia, Veszprém, Hungary

Bird Group, Natural History Museum, Akeman St, Tring, Hertfordshire, United Kingdom

Martin Stervander

CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Porto, Portugal

Agostinho Antunes

Department of Biology, Faculty of Sciences, University of Porto, Porto, Portugal

NABU, Berlin, Germany

Dieter Thomas Tietze

Centre for Zoo and Wild Animal Health, Copenhagen Zoo, Frederiksberg, Denmark

Mads Bertelsen

Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China

College of Life Science, University of Chinese Academy of Sciences, Beijing, China

Institute of Ecology, Peking University, Beijing, China

Carsten Rahbek

Danish Institute for Advanced Study, University of Southern Denmark, Odense, Denmark

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

Gary R. Graves

University of Illinois Urbana-Champaign, Champaign, IL, USA

Tandy Warnow

Department of Biology, University of Florida, Gainesville, FL, USA

Edward L. Braun

University Museum, NTNU, Trondheim, Norway

M. Thomas P. Gilbert

Vertebrate Genome Lab, The Rockefeller University, New York, NY, USA

Erich D. Jarvis

Howard Hughes Medical Institute, Durham, NC, USA

University of California, San Diego, San Diego, CA, USA

Siavash Mirarab

Villum Center for Biodiversity Genomics, Department of Biology, University of Copenhagen, Copenhagen, Denmark

Guojie Zhang

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Correspondence to Josefin Stiller , Siavash Mirarab or Guojie Zhang .

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Table of all sequenced species with taxonomic grouping according to Howard & Moore. 4th Edition and accession numbers of the used genome assemblies. Given as a separate tab-delimited text file.

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Stiller, J., Feng, S., Chowdhury, AA. et al. Complexity of avian evolution revealed by family-level genomes. Nature (2024). https://doi.org/10.1038/s41586-024-07323-1

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Why do women go through menopause? Scientists find fascinating clues in a study of whales.

The existence of menopause in humans has long been a biological conundrum, but scientists are getting a better understanding from a surprising source: whales.

Findings of a new study suggest menopause gives an evolutionary advantage to grandmother whales’ grandchildren. It's a unique insight because very few groups of animals experience menopause.

A paper published Wednesday in the journal Nature looked at a total of 32 whale species, five of which undergo menopause. The findings could offer clues about why humans, the only land-based animals that also goes through menopause, evolved the trait.

“They’ve done a great job of compiling all the evidence,” said Michael Gurven, a professor of anthropology at the University of California, Santa Barbara who studies human evolution and societies. “This paper quite elegantly gets at these very difficult issues.”

Whales might seem very distant from humans, but they have important similarities. Both are mammals, both are long-lived, and both live in family and social groups that help each other.

How long does menopause last? Menopause questions and concerns, answered.

Studying these toothed whale species offers a way to think about human evolution, said Gurven, who was not involved in the study.

In five species of toothed whales – killer whales, beluga whales, narwhals, short-finned pilot whales and false killer whales – the researchers’ findings suggest menopause evolved so grandmothers could help their daughters' offspring, without competing with them for mates.

Only daughters' offspring are aided because in these whales, while the males stay with their family group, they mate with females in other groups. But mothers do tend to give more support to their male offspring than to their female offspring.

Post-reproductive-age females help their family group in many ways. Off the coast of Washington state and British Columbia in Canada, grandmother killer whales catch salmon and "break the fish in half and share that catch with their families. So they're actively feeding their families,” said Darren Croft, a professor of behavioral ecology at the University of Exeter in the United Kingdom and senior author on the paper.

The whale grandmothers also store ecological knowledge about when and where to find food in times of hardship by using the experience they have gained over the lifetime of their environments.

“We see just the same patterns in (human) hunter-gatherer societies,” Croft said. “In times of a drought or in during times of social conflict, the people would turn to the elders of that community. They would have the knowledge.”

The 'grandmother hypothesis'

The researchers’ findings support what’s known as “the grandmother hypothesis .” It states that menopause is evolutionarily useful because while older women are no longer able to have children, they can instead focus their efforts on supporting their children and grandchildren. This means their family lines are more likely to survive, which has the same effect as having more children.

“What we showed is that species with menopause have a much longer time spent to live with their grand offspring, giving them many more opportunities for intergenerational health due to their long life,” said Samuel Ellis, an expert in human social behavior at the University of Exeter and the paper’s first author.

The difference in humans, Gurven said, is that both grandmothers and grandfathers contribute to the well-being of their children and grandchildren.

“In the human story, I think it’s multigenerational cooperation on steroids,” he said.

Though the study doesn’t prove once and for all that the grandmother hypothesis is the reason for menopause in women, it does lay out the evidence, he said. “It’s part of the story, but no one would say it tells the whole story,” Gruven said.

Does menopause lead to a longer life in humans?

There are two proposed pathways for how menopause evolved in humans: the live-long hypothesis and the stop-early hypothesis.

The live-long hypothesis suggests menopause increased total life span, but not how long a woman could have children. That leads to a prediction that species with menopause would live longer but have the same reproductive life span as species without menopause.

In the stop-early hypothesis, the theory is that menopause evolved by shortening the reproductive life span while the total life span remained unchanged. For this to be true, it would be likely that similar species without menopause would have the same life span as those that have menopause, but a shorter reproductive life span.

In looking at species of toothed whales that don’t have menopause and five that do, the researchers' findings make the long-life hypothesis seem most likely.

“This comparative work we’ve been able to do shows that females minimize this competition over reproduction by not also lengthening their reproductive period. Instead, they've evolved a longer lifespan while keeping a shorter reproductive life span,” Croft said.

This appears to be exactly what humans did.

“One of the striking features of this work is the fact that we find this really incredible and rare life-history strategy that we see human societies and in the ocean, but not elsewhere in mammal societies,” he said.

Whale study doesn't reflect men's life spans

The similarities with humans are not across the board, which is good news for men.

No one knows why in humans only females undergo menopause even though both sexes live to be approximately the same ages.

That’s not the case in some of these whales species, where male life spans are typically much shorter than those of females.

“In the killer whale population, for example, females regularly live into their 60s and 70s," Croft said. "The males are all dead by 40.”

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Research News

Scientists study brains to understand the joy that's felt when caring for siblings.

Michaeleen Doucleff

For our series The Science of Siblings, we hear how researchers have found out that caring for siblings can make people happier.

STEVE INSKEEP, HOST:

Every Saturday evening inside a blue house in Odessa, Texas, you hear this.

(CROSSTALK)

UNIDENTIFIED PERSON #1: This is the fresas with crema.

UNIDENTIFIED PERSON #2: That's perfect.

UNIDENTIFIED PERSON #3: That's good.

INSKEEP: The Almance family gets together, cooks together, including the family's special taco recipe, and they laugh together.

CINDY ALMANCE: Tell daddy and you, and he was like, ma (laughter).

INSKEEP: This is much more than a good time because layered into that social gathering is a way to teach brothers and sisters to stop fighting and care for each other. For our series, The Science of Siblings, Michaeleen Doucleff traveled to the family's home in West Texas.

MICHAELEEN DOUCLEFF, BYLINE: Several months ago, Caitlynn Almance graduated from college. She's 22 years old. And as she thinks back to high school and college, she's had the same best friend the entire time.

CAITLYNN ALMANCE: My sister is my best friend in the entire world, and I tell people that all the time. I don't think I could, like, make it, like, day-to-day basis without her. Like, she's the person that I depend on for everything.

DOUCLEFF: Caitlynn grew up in Odessa, but she went to college in another town two hours away. Sometimes her sister, who's still in high school, will call up Caitlynn and ask her to borrow her clothes or a pair of boots. And Caitlynn would stop what she's doing and drive four hours.

CAITLYNN ALMANCE: There was one time that I had, like, just like a four-hour break. That's all I had. And I drove all the way down, like, dropped it off in the front door and, like, took all the way back off to, like, Alpine. It's 'cause she needed it. She needed it. And, you know, in that moment, she really does need it.

DOUCLEFF: Caitlynn's love and caring doesn't end with her sister. She feels the same about her little brother. A few years ago, she seriously considered moving with him to Los Angeles to help support him through school.

CAITLYNN ALMANCE: And I was like, look, Eddie, if that's what you really want to do, like, I'll, like, get up, and I'll pick up my life. And I'll move over there, and I'd work.

DOUCLEFF: To help him pay for schooling?

CAITLYNN ALMANCE: Yeah. Yeah. And so he wouldn't have to struggle either. 'Cause I know, like, how hard it was for me to, like, work and go to school at the same time and, like, juggle. And, like, if he doesn't have to do that, then I wouldn't want him to do that.

DOUCLEFF: For the past few decades, scientists have been studying how parents all around the world teach their children to build deep, fulfilling relationships with their siblings. Belinda Campos is a psychologist at the University of California, Irvine. She says that in Latino families like Caitlynn's, parents are often doing something very specific, and scientists can even see what it is inside the brains of young adults. In one study, researchers brought college students into the laboratory from two cultural groups.

BELINDA CAMPOS: They had European American and Latino participants come in, and they had them play a resource game.

DOUCLEFF: During the game, the students had to decide whether or not to help a family member who needed money.

CAMPOS: Both groups assisted their family members when there was a perceived need.

DOUCLEFF: But here's the key. While the participants gave their family money, the researchers studied their brains with an MRI scanner. What happened inside the brains of the Latino participants was quite different than inside European Americans' brains. While giving their family money...

CAMPOS: The brains of the Latino participants - the reward centers of the brain lit up when they were doing so.

DOUCLEFF: In fact, their reward centers lit up more when they gave money than when they received money themselves. But that didn't happen with European American students.

CAMPOS: It makes such a compelling argument that some of us find the act of giving - or because we're socialized to - to be, like, really rewarding.

DOUCLEFF: Now, the reward center of our brains helps us feel pleasure and joy. Mariano Rojas is a behavioral economist at Mexico's Technology Institute. He says many studies, including his own, show that Latino parents often teach their children not so much that they have to help their siblings, but to actually want to help their siblings. And they do that by teaching them that helping brings you joy.

MARIANO ROJAS: It's about enjoyment and relations that provide happiness.

DOUCLEFF: In other words, helping your sister - bringing her a pair of boots - isn't so much a huge burden or obligation, but a major source of joy in your life. It gives children...

ROJAS: What I call relational wealth.

DOUCLEFF: This relational wealth has massive repercussions for Latin American communities. Rojas' research finds it's the major reason why people in these communities tend to score very high on surveys about happiness.

ROJAS: Latin Americans perform outstandingly well. Their happiness is very high.

DOUCLEFF: That's across all economic levels.

CINDY ALMANCE: The funniest story of my life.

DOUCLEFF: Back in Odessa, the sun has just set over the dry West Texas prairie. It's around dinnertime, and the Almance family is doing what they do almost every day. They're gathered around the kitchen island. There's Caitlynn's grandmother, her parents, an aunt and an uncle. They're telling stories about trips they've taken all together and reliving all the fun.

CINDY ALMANCE: Welcome to the family. I mean...

DOUCLEFF: That's Caitlynn's mom, Cindy. I asked her, how do you teach children to find joy in helping their siblings?

CINDY ALMANCE: It's the modeling, right?

DOUCLEFF: Modeling. And what Cindy models is the joy she gets from the relationships she has with her own brothers and sisters.

CINDY ALMANCE: I guess it's a close, close bond.

DOUCLEFF: Even after some of her siblings left Odessa, she still loves to talk to them on the phone.

CINDY ALMANCE: You know, 6:30 in the morning on my way to work, I'm talking to them already, but it's every single day.

DOUCLEFF: And her daughter Caitlynn not only notices these calls - she even joins them.

CAITLYNN ALMANCE: My mom's older sister, they're in a different time zone than we are, so they have to wake up an hour earlier than their day would start, just so that we could, like, have that, like, daily, like, dose before we get to work.

DOUCLEFF: Perhaps most importantly, Caitlynn says, is the family enjoys being together - multiple generations of brothers and sisters all having fun together.

CAITLYNN ALMANCE: This is, like, your everyday people. And they have such, like, an everyday impact on my life rather than, like, us, like, searching each other up on Facebook. Like, that doesn't exist. Like, we all know each other.

DOUCLEFF: And now Caitlynn is going to add the next generation of siblings. She's expecting her first child this summer, and she's already talking about having a second baby.

CAITLYNN ALMANCE: This one isn't even out yet, but I'm like, let's hurry up and make the other one.

DOUCLEFF: Why? Because she wants her baby to have a sibling right away.

CAITLYNN ALMANCE: Just, like, imagining my kid growing up without that - there's probably nothing worse than that. That would be, like, the biggest sin I could ever imagine.

DOUCLEFF: For NPR News, I'm Michaeleen Doucleff.

(SOUNDBITE OF MUSIC)

INSKEEP: Hey, join us tomorrow for a story of identical twins. Both are autistic and have different experiences.

Copyright © 2024 NPR. All rights reserved. Visit our website terms of use and permissions pages at www.npr.org for further information.

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• Published: 03 April 2024

Heavy metal association with chronic kidney disease of unknown cause in central India-results from a case-control study

• Mahendra Atlani 1   na1 ,
• Ashok Kumar 2   na1 ,
• Rajesh Ahirwar 3 ,
• M. N. Meenu 1 ,
• Sudhir K. Goel 2 ,
• Ravita Kumari 2 ,
• Athira Anirudhan 1 ,
• Saikrishna Vallamshetla 4 &
• G. Sai Tharun Reddy 4  

BMC Nephrology volume  25 , Article number:  120 ( 2024 ) Cite this article

Metrics details

Chronic Kidney Disease of unknown cause (CKDu) a disease of exclusion, and remains unexplained in various parts of the world, including India. Previous studies have reported mixed findings about the role of heavy metals or agrochemicals in CKDu. These studies compared CKDu with healthy controls but lacked subjects with CKD as controls. The purpose of this study was to test the hypothesis whether heavy metals, i.e. Arsenic (As), Cadmium (Cd), Lead (Pb), and Chromium (Cr) are associated with CKDu, in central India.

The study was conducted in a case-control manner at a tertiary care hospital. CKDu cases ( n  = 60) were compared with CKD ( n  = 62) and healthy subjects ( n  = 54). Blood and urine levels of As, Cd, Pb, and Cr were measured by Inductively Coupled Plasma- Optical Emission Spectrometry. Pesticide use, painkillers, smoking, and alcohol addiction were also evaluated. The median blood and urine metal levels were compared among the groups by the Kruskal-Wallis rank sum test.

CKDu had significantly higher pesticide and surface water usage as a source of drinking water. Blood As levels (median, IQR) were significantly higher in CKDu 91.97 (1.3–132.7) µg/L compared to CKD 4.5 (0.0–58.8) µg/L and healthy subjects 39.01 (4.8–67.4) µg/L ( p  < 0.001) On multinominal regression age and sex adjusted blood As was independently associated with CKDu[ OR 1.013 (95%CI 1.003–1.024) P  < .05].Blood and urinary Cd, Pb, and Cr were higher in CKD compared to CKDu ( p  > .05). Urinary Cd, Pb and Cr were undetectable in healthy subjects and were significantly higher in CKDu and CKD compared to healthy subjects ( P  = < 0.001). There was a significant correlation of Cd, Pb and Cr in blood and urine with each other in CKDu and CKD subjects as compared to healthy subjects. Surface water use also associated with CKDu [OR 3.178 (95%CI 1.029–9.818) p  < .05).

The study showed an independent association of age and sex adjusted blood As with CKDu in this Indian cohort. Subjects with renal dysfunction (CKDu and CKD) were found to have significantly higher metal burden of Pb, Cd, As, and Cr as compared to healthy controls. CKDu subjects had significantly higher pesticide and surface water usage, which may be the source of differential As exposure in these subjects.

Peer Review reports

Chronic kidney disease of unknown cause (CKDu) has been reported in various parts of the world (i.e., Nicaragua, El Salvador, Sri Lanka), including India, as an endemic disease. The disease is a diagnosis of exclusion, made when a patient fulfils the Kidney Disease Improving Global Outcomes (KDIGO) CKD criteria but without the evidence of a recognized cause such as diabetes, hypertension, or glomerulonephritis [ 1 ]. No uniform and definite cause has yet been identified, though various environmental factors have been associated with and suggested to play a role in the pathogenesis. For example, heat stress, strenuous exercise, agrochemicals, and heavy metals have been held responsible for Mesoamerican nephropathy [ 2 ]. Mixed evidence has been reported for association with agrochemicals, heavy metals, and genetic variability for CKDu in Sri Lanka [ 3 , 4 , 5 ]. In India, one small study reported an association of heavy metals with CKD [ 6 ]. A study done on groundwater samples from the Uddanam region of Andhra Pradesh (India), which has a high prevalence of CKDu reported water as acidic (pH < 6.5) and to contain higher silica and lead in wet and dry seasons, respectively. Phthalates were also detected in the groundwater [ 7 ]. Previous studies have attempted to find a correlation between heavy metals and CKDu by comparing cases and endemic and nonendemic controls [ 4 ]. No study has yet tried to find the association of heavy metals comparing CKDu with CKD. Furthermore, previous studies have used urinary metal levels as a biomarker of metal exposure. There is an inherent issue of reverse causality i.e., reduced excretion of metals in urine with a reduction in glomerular filtration rate (GFR) [ 8 ]. Measurement of metals in blood has also been reported to be a promising biomarker of metal exposure [ 9 , 10 ]. Some studies have employed urine to blood ratio for deciding whether urine or blood levels should be used for a particular metal. For metals with urine/blood ratio more than one blood metal levels, whereas for metals with urine/blood ratio less than one, urine metal levels were used in estimation analysis [ 11 ]. The purpose of this study was to test hypothesis whether heavy metals i.e. Arsenic (As), Cadmium (Cd), Lead (Pb) and Chromium (Cr) are associated with CKDu, in central India using blood and urine levels as biomarker of metal exposure.

Materials and methods

Study setting and population.

Study was conducted in a tertiary care hospital setting in the Department of Nephrology in India in a case-control design between December 2019 to June 2022. Participants were enrolled between December 2019 -December 2021. The data collection was done simultaneously. The sample analysis was carried out between January to June-2022. The study was performed according to the guidelines of the Declaration of Helsinki. The study objective was to compare CKDu cases with CKD and healthy controls with regard to biomarkers of exposure of heavy metals [blood and urine levels of cadmium (Cd), lead (Pb), arsenic (As) and chromium (Cr)]. The study included adults aged 18–70 years with CKDu and two groups of the control population, one with CKD and another group of healthy controls without evidence of CKD.

The CKDu and CKD cases were inducted among the patients visiting the nephrology outpatient department and based on pre-defined criteria. At the same time, healthy controls were inducted among the healthy relatives accompanying the patients visiting other departments of the institute for treatment. Written informed consent was obtained from all the participants.

The case definition of CKDu was based on criteria proposed by the Indian Society of Nephrology for the diagnosis of CKDu [ 12 ]. The inclusion criteria included- eGFR < 60 mL/min/1.73m2 (CKD-EPI) [ 13 ] and albumin-to-creatinine ratio (ACR) > 30 mg/g for more than 3 months with:

Urine protein creatinine ratio(PCR) less than 2g/g.

No history of glomerulonephritis, pyelonephritis, renal calculi, polycystic kidneys or obstruction on renal ultrasound.

Not on treatment for diabetes and HbA1c less than 6.5%.

Blood pressure less than 140/90 if CKD stage 1 and 2; and less than 160/100 if CKD stage 3,4, and 5 and on a single drug for blood pressure control.

Case definition of CKD was based on: eGFR < 60 mL/min/1.73m2 (CKD-EPI) and albumin-to-creatinine ratio > 30 mg/g for more than 3 months. Patients were included in the CKD group only if PCR > 2g/g. Hypertension with BP > 140/90 in stages 1–2 and > 160/100 in stages 3–5 or on two or more drugs for BP control.

CKD staging was based on the KDIGO-2008 classification [ 1 ]. The same stages were applied to categorize the renal functions of subjects with CKDu.

Inclusion criteria for healthy controls included: Absence of CKD as evidenced by eGFR more than 90 ml/min/1.73m2, ACR < 30mg/g and lack of anatomical renal disease, obstruction or stone on renal ultrasound, no history of diabetes, HbA1C less than 6.5 and BP less than 140/90.

Biases were kept a minimum by adhering to the case definition described above, and study exposures are mainly objectively assessed with very less dependency on recall i.e. for pesticide or painkiller use. The urine metal levels were adjusted for urine dilution by estimating metals per gram of creatinine in urine.

Sample size

Assuming a difference of moderate effect size (0.25), between three groups (CKDu cases, CKD Controls, Normal Controls) with a confidence level of 95% and power of 80%, the calculated sample size was 159. The final sample size estimated, including a 10% non-response rate, was 180 (60 per group).

Specimen collection and analysis

For the analysis of heavy metals, venous blood (2 ml) was collected in trace element free Trace Element K2-EDTA Vacutainer (Cat# BD 368381). Whole blood was stored at -40 °C until analysis. Ten millilitres (10 ml) of first-morning urine was collected in 50 ml polypropylene tubes. Urine was stored at -40 °C in aliquots until analysis. Serum and urine creatinine was measured using a modified kinetic Jaffe’s method using a Random Access Fully Automated Chemistry Analyzer (Beckman Coulter). Urinary protein and urine albumin were estimated using a colorimetric and immune-turbidimetric methods, respectively, using a Random Access Fully Automated Chemistry Analyzer (Beckman Coulter). HbA1c was analyzed by ion-exchange high pressure liquid chromatography method using a D10 Haemoglobin testing system (BioRad Laboratories). eGFR was calculated from serum creatinine and CKD -EPI equation (Ref). A kidney ultrasound was performed in standard B Mode grey scale in 3.5–5 MHz, the longitudinal length was measured along with the width and thickness of the kidney, renal stones, and any other anatomical abnormality.

Estimation of heavy metals in blood and urine

Levels of Cd, Pb, Cr and As were measured in whole blood and urine. Urinary spot sample results of metal analysis were adjusted for dilution by urine creatinine. Metal analysis was carried out at NIREH, Bhopal (India).

Levels of various heavy metals, viz. Cd, Pb, Cr, As in the collected blood and urine samples were analyzed through inductively coupled plasma optical emission spectroscopy (iCAP® 7400 Duo ICP-OES, ThermoFisher Scientific® Pvt. Ltd). Blood and urine samples were acid-digested in a microwave oven prior to metal detection on ICP-OES. For blood digestion, 1 mL of whole blood sample was mixed with 6 ml of a freshly prepared mixture of concentrated trace metal grade nitric acid (HNO3) and hydrogen peroxide (H2O2) in a ratio 2:1 (v/v) in high-purity polytetrafluoroethylene (PTFE-TFM) vessels. For urine digestion, 5 mL of urine sample was mixed with 6 ml of a freshly prepared mixture of HNO3 and H2O2 in a ratio of 2:1. After gentle mixing of these reactants with blood, the PTFE-TFM vessels were arranged in the rotor (24HVT80, Anton PAAR) and digestion was carried out in the Anton Paar, multi microwave PRO Reaction System at 200 C for 15 min. Digested samples were cooled to 40°C and diluted to 30 ml with distilled water. Blank was prepared for each cycle of digestion using distilled water, nitric acid, and hydrogen peroxide mixture. All the chemicals were trace-element free.

Before the analysis of metal ions in processed blood and urine samples, calibration standards for each element were prepared from multi-element stock solutions (1000 mg L − 1) in triple distilled water. Detection of Cd, Pb, and Cr was performed using a standard sample introduction setup, whereas for As, the hydride generation sample introduction system was utilized. Online hydride generation for As was achieved with an Enhanced Vapor System sample introduction kit using 0.5% m/v sodium tetrahydroborate (NaBH4) stabilized in 0.5% m/v NaOH and 50% v/v HCl solution. Emission data acquisition was performed using the Qtegra ISDS Software at interference-free wavelengths.

Statistical analysis

Statistical analyses were performed with R version 4.2 (R Foundation for Statistical Computing, Vienna, Austria) and IBM SPSS 26 version. The distribution of data in groups was evaluated with Shapiro-Wilk, kurtosis, skewness, and histograms. Skewed data for three groups was compared with the Kruskal-Wallis test. Subgroup analysis in three groups was performed with pairwise comparisons by Dunn test. Parameters with homogeneous distribution were compared with the chi-square test. Data are presented as %, for categorical variables or as median (Q1-Q3) for continuous variables.

Detection rates for blood and urinary metal levels were calculated. For urine metal levels, all statistical analyses were performed with creatinine-adjusted metal concentrations.

Urine to blood ratio was calculated for all metal levels. Spearman correlation coefficient was used to find the association between blood and urine metal levels of individual metals as well as for the association between different metals both in blood and urine. Correlation of blood and urine As with GFR was also performed.

We performed multinominal regression analysis for significantly different metal level in CKDu cases with respect to CKD and healthy controls. We included age and gender (confounding factors) in the model to see the y independence of association and effect estimate of the factor associated with CKDu. Regression model matrices and goodness-of-fit were also determined by the pseudo R 2 coefficient and Hosmer-Lemeshow goodness-of-fit test.

For all analyses, we have considered a p -value less than 0.05 as statistically significant.

A total of 568 patients who visited Nephrology OPD during the study period were screened for inclusion in the study. Out of these, 66 CKDu and 70 CKD cases were found eligible to enroll in the study. Eight patients withdrew consent in the CKD group, whereas four patients in the CKDu group had uncontrolled blood pressure with a single drug, and two withdrew consent. Finally, 60 CKDu and 62 CKD cases were included in the study for outcome analysis. We have approached 120 relatives of patients attending other OPDs and screened them for eligibility criteria of the healthy control group. Out of these, 60 were eligible, and 54 provided consent for participation in the study.

Demography and lab parameters

The CKD and CKDu subjects were similar in demographics for age and sex. However, healthy subjects were younger (Table  1 ). There was no significant difference between CKDu and CKD with reference to stage V (32 vs. 44, P-0.107).There were 05 diabetic kidney disease 04 CKD due to secondary glomerular disease patients (3-lupus nephritis, 1-FSGS), 12 hypertension-associated renal disease, 01 ADPKD, 36 Chronic glomerulonephritis patients, and 04 Chronic pyelonephritis patients in the CKD group. Use of smoking, Alcohol, and painkillers was similar across the three groups (Table  1 ). A significant difference was found between the three study groups with respect to the source of drinking water (ground or surface water). A significantly higher number of CKDu subjects used surface water as a source of drinking water (Table  1 and Table-S 1 and Fig-S 1 ) and a higher number of CKDu subjects reported pesticide usage. As shown in Table  1 , blood pressures were significantly higher in CKD subjects compared to CKDu and healthy subjects and reflect the inclusion criteria with appropriate patient inclusion in three groups. Both ACR and PCR were also significantly different between CKD and CKDu. The eGFR was calculated based on the CKD-EPI formula and was not significantly different between the CKD and CKDu subjects, however, CKD subjects had lower median eGFR compared to CKDu subjects. The healthy subjects had significantly higher eGFR compared to both groups. HbA1c, were similar across the three groups (Table  1 ).

Analytical results

The urinary and blood levels of As, Cd, Pb, and Cr (Table  2 ) were measured in ppb (micrograms per litre), and median with interquartile ranges were reported. Urinary metal levels were also measured in ppb (micrograms per liter) and then adjusted for urinary dilution by urine creatinine value and were finally expressed as micrograms/grams of urine creatinine (Table  2 ).

Detection limits

The lowest detectable concentrations of various heavy metals analyzed on ICP OES with a signal-to-noise ratio of 1 were as follows: As (193.759 nm) - 0.191 ppb; Cd (214.438 nm) - 0 ppb; Pb (220.353 nm) - 0.822 ppb; Cr (283.563 nm) - 3.156 ppb (Table  2 , Figs-S 2 -S 5 ).

Detection percentage

The number of subjects with blood and urine metal levels above the respective detection limits in each study group is reported in Table  2 .

Urine to blood ratio

A urine/blood ratio for each metal in all study groups was calculated for patients with metal levels above the detection limit. The distribution of urine/blood ratios for all metals is presented in Table  2 . Ratios were different between healthy and subjects with deranged kidney functions i.e. low GFR (CKD and CKDu). Median urine/blood Ratio for As was > 1 in healthy subjects and < 1 in CKD and CKDu, reflecting higher urinary levels compared to blood in healthy and reverse in CKD and CKDu subjects. For Pb, it was < 1 in healthy subjects and > 1 in subjects with CKD and CKDu, reflecting higher blood levels compared to urine in healthy and reverse in CKD and CKDu subjects. For Cd and Cr the ratio were < 1 across all three groups suggesting higher urine levels compared to blood levels.

Correlation

A spearman correlation (ρ) was also performed to see the association between each urine and blood metal and among the metals with each other as well. In CKDu, UAs were negatively associated with BAs (ρ-0.260, p -0.11) and in CKD positively (0.138, p -0.37). There was a positive association between urine and blood levels of As,Pb, and Cr and negative association of urine and blood Cd in CKD. In CKDu, a positive association was found in blood and urine Cd,Pb and Cr. In addition, there was a strong correlation of blood Cd, Pb, and Cr ( p  < 0.01) [ρ = 0.68 (BCd and BPb), 0.88 (BCd and BCr), 0.71 (BPb and BCr) in CKDu and [ρ = 0.55 (BCd and BPb), 0.82 (BCd and BCr), 0.65 (BPb and BCr) in CKD. The Urine Cd, Pb, and Cr also had strong correlations [ρ = 0.33 (UCd and UPb), and 0.48(UPb and UCr)] in CKD and [ρ = 0.19(UCd and UPb), 0.67 (UCd and UCr), and 0.69 (UPb and UCr)] in CKDu < 0.05 (Table-S 2 -S 4 and Fig-S 6 ). Association of Blood and urine As with GFR was also evaluated, and BAs were found to be negatively associated with GFR (ρ = -0.097, p  = 0.56), whereas UAs were positively associated (ρ = 0.14, p  = 0.25) with GFR (Table-S 5 ). Metal levels: Blood As: was significantly higher in CKDu ( n  = 37) subjects compared to CKD ( n  = 41) and healthy ( n  = 53) subjects (Table  2 ). On the other hand, the urinary As (UAs) was significantly low in CKD ( n  = 50) and CKDu ( n  = 48) subjects compared to healthy subjects ( n  = 38) and was non significantly higher in CKD subjects compared to CKDu subjects (Fig.  1 , Table  2 ).The blood and urine As values were below detection limits in 21.6%, 35.7%, and 18.8% and in 6%, 16.6%, and 0% of subjects in CKDu, CKD, and healthy groups, respectively.

Box plot for distribution of blood and urine arsenic according to diagnosis categories. Median; microgram/Lt (blood); microgram/gm(urine); UAs- Urine arsenic;CKDu-Chronic kidney disease of unknown cause; CKD-Chronic kidney disease

Blood Cd also was significantly higher in CKD and CKDu subjects compared to healthy subjects. Urinary Cd (UCd) levels were significantly higher in CKD and CKDu subjects compared to healthy subjects,. There was a weak association of ( p  = 0.06) UCd with CKD subjects compared to CKDu subjects.UCd was higher in CKD subjects compared to CKDu (Fig.  2 , Table  2 ). The blood and urine Cd values were below detection limits in 8.3%, 8.1%, and 0% and 37.2%, 19.3%, and 75.9% of subjects in CKDu, CKD, and healthy groups, respectively.

Box plot for distribution of blood and urine cadmium according to diagnosis categories. Median, microgram/Lt (blood); micrograms/gm (urine); UCd- Urine cadmium;CKDu-Chronic kidney disease of unknown cause; CKD-Chronic kidney disease

Pb levels in the blood of CKD and CKDu as well as in urine of CKD and CKDu subjects were significantly higher compared to healthy subjects. The Pb levels were higher in CKD subjects compared to CKDu subjects, but it was not statistically significant (Fig.  3 , Table  2 ). The blood and urine Pb values were below detection limits in 15%, 20%, and 25.9% and 23.3%, 19.4% and 70.4% of subjects in CKDu, CKD, and healthy groups, respectively.

Box plot for distribution of blood and urine lead according to diagnosis categories. microgram/Lt (blood); microgram/gm(urine); UPb- Urine lead;CKDu-Chronic kidney disease of unknown cause; CKD-Chronic kidney disease

As shown in Table  2 and Fig.  4 , urinary and blood Cr was significantly higher in CKD, and CKDu patients than healthy subjects. The blood and urine Cr values were below detection limits in 13%, 0%, and 0% and 13.3%, 14.5% and 85.2% of subjects in CKDu, CKD, and healthy groups, respectively.

Box plot for distribution of blood and urine chromium according to diagnosis categories. Median; microgram/Lt(blood); microgram/gm(urine); UCr-urine chromium, CKDu-Chronic kidney disease of unknown cause; CKD-Chronic kidney disease

Multinominal regression

Though age, gender, were not significantly different between CKDu and CKD, on univariate analysis, we included these In the multinominal regression analysis between CKDu and CKD in reference to healthy subjects in addition to factors found significantly different ( p  < 0.01 on univariate analysis) i.e. blood As and source of drinking water.After the final model, gender had no association with CKDu. Blood As, surface water as drinking water source and age were independently associated with CKDu. Age was associated independently with CKD also (Table  3 ).

To the best of our knowledge, this is the first study wherein an attempt has been made to analyze the association of heavy metals with CKDu in central India, using blood and urine levels as biomarkers of metal exposure. In addition, CKD and healthy subjects have been used as control groups.

The current study showed that blood and urine creatinine-adjusted urinary levels of heavy metals Cd, Pb and Cr were significantly higher in patients with CKD and CKDu as compared to healthy subjects. The urinary levels of the above metals were undetectable in healthy subjects. The study also showed a weak association of ( p  = < 0.06) higher urinary Cd in CKD subjects compared to CKDu subjects of this Indian cohort.

The study also showed that Blood As was significantly higher in CKDu subjects compared to CKD and healthy subjects. On multinominalregression, blood As was independently ( p  < 0.05) associated with CKDu after age adjustment.

In our study, median GFR was rather high in CKDu subjects [14.5 (7.0, 34.2)] compared to GFR in CKD subjects [9.0 (6.0, 17.0)ml/min/1.73m 2 )] and it was non significantly different between the two groups. On correlation analysis, there was a negative correlation between Blood As and GFR and a positive correlation of urine As with GFR. Based on this, the higher blood As in CKDu with higher GFR appears to be truly elevated.

Previously a study from Sri Lanka has also reported an association of CKDu with chronic As toxicity. In that study, 48% of CKDu patients and 17.4% of the control subjects fulfilled the criteria to be diagnosed with chronic arsenical toxicity(CAT), indicating the potential link between CAT and CKDu and suggesting agrochemicals could be the possible source [ 14 ]. Later, it was reported that glyphosate was the most widely used pesticide in Sri Lanka, which contains an average of 1.9 mg/kg arsenic. Findings suggest that agrochemicals, especially phosphate fertilizers, are a major source of inorganic arsenic in CKDu endemic areas [ 15 ]. However, another study from Sri Lanka did not find any difference in UAs levels in patients of CKDu in endemic areas and controls from endemic and nonendemic areas [ 4 ].

Some other studies have reported associations of As with CKD. A study from Taiwan found total UAs to be associated with a four-fold risk of CKD [ 6 ]. Another study reported an association of MMA V (mono methyl arsenate pentavalent) and DMA V (dimethyl arsenate pentavalent) in urine with prevalence of CKD [ 16 ]. However, in both studies, the type of CKD was not reported.

The higher blood As in CKDu compared to CKD may be associated with exposures in our study; a significantly higher number of subjects in CKDu group reported use of pesticides, surface water as a source of drinking water in CKDu subjects.On regression analysis also, surface water was independently associated with CKDu.

A study from north India reported increased levels of OCPs, namely α-HCH, aldrin, and β-endosulfan, in CKDu patients as compared to healthy control and CKD patients of known etiology [ 17 ] and it is also known that arsenic is an important component of pesticides [ 18 ]. The contamination of surface water with various pollutants i.e. pesticides, is common [ 19 ]. Arsenic is a known nephrotoxin, and one of the case reports where kidney histopathology was evaluated reported As causes tubulointerstitial disease (TID) [ 20 ]. The difference in methylation processes of As has also been found responsible for various diseases associated with As i.e. for example, high proportions of urinary MMAs (%U-MMAs) have been associated with a higher risk of cancers and skin lesions [ 21 ]. In contrast, high %U-DMAs has been associated with diabetes risk [ 22 ]. We have measured only iAs in our study. Whether methylation resulting in various metabolite species has different associations with CKDu or CKD should be explored further. We recently found a significant association of single nucleotide polymorphism in a gene coding for sodium-dependent dicarboxylate transporter (SLC13A3) with the susceptibility to CKDu [ 23 ].

In the current study, the UAs results suggest that As levels of 97 µg/gm of creatinine in healthy subjects were not associated with decreased GFR or proteinuria. Similar results were reported by a study from China where researchers found a lower confidence limit on the benchmark dose (LBMD) of 102 and 0.88 µg/gm creatinine for As and Cd, respectively, in order to prevent renal damage in the general population co-exposed to arsenic and cadmium [ 24 ]. The UAs in healthy subjects in our study were nearly similar to the LBMD reference and, not surprisingly, not to be associated with CKD or proteinuria.

Some studies have reported lead to be associated with CKDu. An Indian study reported high levels of lead and silicon concentrations in Indian groundwater in the endemic Uddanam area [ 7 ]. Jaysuman et al. also reported higher levels of Pb (26.5 µg/gm) in the urine of patients with Sri Lankan agricultural nephropathy compared to endemic and nonendemic control [ 25 ].

In the current study, although the median level of blood Pb was almost double in CKD patients compared to CKDu, the result was not statistically significant.

Our study showed that Cd was significantly associated with renal disease. Blood Cd and urine Cd (UCd) levels were significantly higher in patients with renal disease (CKD and CKDu) as compared to healthy subjects. The findings of UCd also showed a weak association (p-0.06) of Cd with CKD compared to CKDu among patients with renal diseases. There are some concerns that UCd may not be truly reflective of metal burden in patients with advanced CKD [ 26 ], because initially, in the course of Cd toxicity with early tubular damage, the normal reabsorption of cadmium-metallothionein decreases, and the UCd concentration increases. However, in the long run, cadmium-induced kidney damage gives rise to low Cd concentrations in both the kidney and urine, while the tubular damage remains [ 27 ]. The U/B ratio of < 1 for Cd in our study supports the above findings.

The mean eGFR in our CKD cohort was lower compared to CKDu; despite this, higher UCd values in patients with CKD compared to CKDu in our study indicate a potential association of Cd with CKD.

Studies have reported variable association of Cd with CKDu when compared to healthy subjects. Nanayakkara et al. [ 28 ] did not find an association of UCd with CKDu in stages 1–4 compared to healthy controls. Whereas another Sri Lankan [ 4 ] study found significantly high UCd in patients with CKDu against the endemic and nonendemic controls. We also observed significantly higher UCd in CKDu vs. healthy controls.

In the current study, urinary Cr (UCr) was not detected in healthy subjects, whereas it was significantly higher in patients with CKD and CKDu as compared to healthy subjects. UCr levels were higher in CKD compared to CKDu. Epidemiologically, Cr exposure has been reported to be associated with kidney damage in occupational populations [ 26 ]. Recently, a study from Taiwan reported that a significant and independent association between Cr exposure and decreased renal function in the general population, and co-exposure to Cr with Pb and Cd is potentially associated with an additional decline in the GFR in Taiwanese adults [ 27 ]. A study from Bangladesh reported outcomes similar to our study; however, the study included only CKD ( n  = 30) patients and compared them with healthy subjects ( n  = 20). In that study, compared to the controls, CKD patients exhibited significantly higher levels of Pb, Cd, and Cr levels in their urine samples. This signifies a potential association between heavy metal co-exposure and CKD [ 29 ]. In the current study a significant correlation between blood Cd, Pb, and Cr and urine Cd, Pb, and Cr were found in CKDu and CKD subjects compared to healthy subjects. The levels of UCd, UPb, and UCr in CKD and CKDu patients were significantly higher compared to healthy controls; The possibility of the combined effect of Cd, Pb, and Cr in the causation of renal diseases could be evaluated further in future studies. As CKDu is an endemic disease, the results of our study suggest an association of arsenic with CKDu in the Indian population, and so the generalizability of the result should be used with caution.

Strengths and limitations

This is the first study which has included two controls (CKD and healthy) and compared metal levels in patients with CKDu. In addition, the comparison of metals in both blood and urine is another advantage, as falling GFR levels and urine levels of several metals do not reflect true metal burden in patients. Inclusion of CKDu patients, as per the suggested definition by the Indian society of Nephrology, is another strength of our study.

The small sample size of our study may be a limitation of our study though it was calculated scientifically. The study involved Indian patients and controls only so the generalization of the results should be with caution. Healthy controls were of younger age is also a limitation of the study.

Also the study included patients from central India, comparatively a larger area and does not points out endemicity.

The study finds an association of environmental toxins with CKDu and CKD. The age and sex-adjusted As were observed to have an independent association with CKDu. A weak association of Cd with CKD was also observed in this Indian cohort. Subjects with renal dysfunction (CKDu and CKD) were observed to have a significantly higher metal burden of Pb, Cd, As, and Cr as compared to healthy controls. CKDu patients may have higher exposure to As via pesticides, surface water usage, or both.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Chronic Kidney Disease of unknown cause

Chronic Kidney Disease

Kidney Disease Improving Global Outcomes

Glomerular filteration rate

Institutional Human Ethics committee

Concentrated trace metal grade nitric acid

Hydrogen peroxide

High-purity polytetrafluoroethylene

Inorganic As

Tubulo-interstitial disease

Pentavalent monomethylarsonic acid

Pentavalent dimethylarsinic acid

Methylarsenous acid

Sodium-dependent dicarboxylate transporter

Limit on the benchmark dose

Arsenobetaine

Urinary MMAs

Urinary DMAs

Blood lead levels

End-stage kidney disease

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Acknowledgements

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The Study was funded by Indian council of Medical Research (ICMR),New Delhi, India. Sanction no.:5/4/7-14/2019-NCD-II.

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Mahendra Atlani and Ashok Kumar contributed equally to this work.

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Department of Nephrology, All India Institute of Medical Sciences (AIIMS), Room No-3022, Academic Block, 3rd Floor, Saket Nagar, Bhopal, Madhya Pradesh, 462020, India

Mahendra Atlani, M. N. Meenu & Athira Anirudhan

Department of Biochemistry, All India Institute of Medical Sciences (AIIMS), Saket Nagar, Bhopal, Madhya Pradesh, 462020, India

Ashok Kumar, Sudhir K. Goel & Ravita Kumari

Department of Environmental Biochemistry, ICMR-National Institute for Research in Environmental Health (NIREH), Bhopal, Madhya Pradesh, India

Rajesh Ahirwar

All India Institute of Medical Sciences (AIIMS), Bhopal, Madhya Pradesh, India

Saikrishna Vallamshetla & G. Sai Tharun Reddy

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MA and AK were equally involved in conceptualising the study, winning the grant, analyzing the results, monitoring the progress of study. MA prepared the manuscript. AK and SKG did the editing. RA supervised analysis of metal levels, sample collection done by MMN, RK. Metal analysis done by AA. Data entry and file preparation for results done by MMN, AA, SKV and STR.

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Atlani, M., Kumar, A., Ahirwar, R. et al. Heavy metal association with chronic kidney disease of unknown cause in central India-results from a case-control study. BMC Nephrol 25 , 120 (2024). https://doi.org/10.1186/s12882-024-03564-4

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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.

Local News | Nighttime gunshots may harm health and sleep of…

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Local News | Nighttime gunshots may harm health and sleep of millions, Mass General study finds

Boston had the highest rate of gunshots at night of six of the most populous u.s. cities.

Nighttime gunshots and the sirens wailing right after may be a larger factor in the health and well-being of millions of people than previously considered, Mass General Brigham researchers report .

The study looked at 72,236 gunshot reports from big cities — including Boston.

“A nighttime gunshot likely disrupts the sleep of nearby community residents due to the sheer sound of the shot, which is then followed by a cacophony of sirens from police vehicles and ambulances,” said Rebecca Robbins, a researcher at Brigham and Women’s Hospital and one author of the study. “The findings from our study shed light on this potentially significant and underexplored social determinant of sleep and population health.”

The report, titled “Estimating Community Disruption from Nighttime Gunshots in 6 U.S. Cities, 2015 to 2021,” explores how many people hear gunshots during the nighttime, how many nighttime shots are heard and the demographics of the most impacted communities.

Of the most populated U.S. city, researchers were only able to access enough gunshot data for Boston; Philadelphia; Washington D.C.; Baltimore; Portland, Oregon; and New York. In total, the team looked at 72,236 gunshot reports from 2015 to 2021.

The work builds on research looking into the “potential for an exponentially broader community impact” of gun violence than the “staggering” number of direct casualties in the U.S., a release from MGH states.

Overall, the study concluded that gunshots were far more prevalent at night — 72% of the shots across the cities occurred during nighttime — and there may be an estimated 12.5 million individual instances of a person hearing a gunshot in just the six cities annually.

As median household income went up, the study concluded, rates of nearby nighttime gunshots went down.

“Nighttime gunshots may be an additional environmental hindrance to sleep, health, and well-being, particularly in economically vulnerable neighborhoods,” the study concludes. “A greater understanding of the ubiquity of nighttime gun violence in underserved communities, presented here, may inspire future research and practical efforts to forge inter-disciplinary care teams to support communities impacted by these events.”

Boston was excluded from the geographic and income analysis of people impacted because the city does not release specific enough gunshot data.

Boston ran in the middle of the cities measured, with about 919 nighttime gunshots a year and between 1.6 million and 10.1 million individual instances of a person being in range to hear a nighttime gunshot. The city also had the highest rate of shots at night, with 80% of gunshots happening during the nighttime over the time period.

The Boston neighborhood most impacted by nighttime gunshots was Roxbury, the study noted, and the least was Charlestown.

The team plans to continue the research and “study sleep disturbances in response to nighttime gunshots as they work to design community-based sleep interventions to support individuals in communities with high incidences,” the release said.

“The traumatic ripple effects from gunshots can extend across families and entire communities,” co-author Chana Sacks, a researcher from Massachusetts General Hospital. “Our work helps to broaden how we think about who is impacted by these events.”

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City Politics | Virginia Beach parks need millions of dollars…

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City politics | virginia beach parks need millions of dollars in improvements, study says.

VIRGINIA BEACH — A recent study recommended the city consider several funding strategies to bring its public parks up to par with similar cities, including a possible tax increase.

Virginia Beach faces a backlog in park maintenance, and several districts don’t have an adequate number of public parks. The city also is lacking park amenities that can be found in peer cities, including a nature center, splash pad and archery range, according to the park need assessment.

An estimated $115 million would required for short-term fixes over the next five years, the study found .

“We understand that resources are limited, so where are you going to be focusing first to make the biggest impact,” said Tristan Cleveland, project manager for Virginia Beach consulting firm Kimley-Horn, at a meeting Tuesday.

The city owns 7,425 acres of park land, of which 5,772 is developed. Addressing safety hazards, improving trail conditions and providing accessible amenities in the developed parks are among the highest priorities recommended in the report.

“You got to take care of what you have,” said Cleveland.

Virginia Beach Parks & Recreation has several projects in the works, including a $1.6 million renovation of the Kids Cove playground area at Mount Trashmore.

In a city survey last year, the majority of respondents who ranked park maintenance as a priority also supported a tax increase to pay for it. The results of the survey were included in the study, which recommended the city consider a bond referendum question on the November ballot to gauge the public’s interest.

Councilwoman Barbara Henley suggested, for starters, that an existing open space fund could be expanded to develop new parks.

A portion of Virginia Beach’s 5.5% meal tax is funneled into the open space fund to help the city buy land for future public parks. Currently, less than half of 1% of the meal tax goes to open space purchases. The total meal tax revenue is expected to grow more than 5% in the new fiscal year to $97.3 million, according to the city’s proposed budget.

The City Council could decide to redirect some funds earmarked for buying park land to improving existing parks. But to fully accomplish the study’s recommendations, a tax increase to pay for a general obligation bond could be necessary.

“It would make a big difference in what can be done,” Henley said.

Councilwoman Sabrina Wooten first advocated for a bond referendum to build new parks last year, but it was tabled for further public input through the survey.

“If we do want to address these disparities, it’s a good way to do it,” she said.

Stacy Parker, 757-222-5125, [email protected]

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Mars Lake Hypothesis on Ice After Study Offers Different Explanation

This image taken by UArizona's HiRISE camera on NASA's Mars Reconnaissance Orbiter shows ice sheets at Mars' south pole. The spacecraft detected clays near this ice; scientists have proposed that such clays are the source of radar reflections previously interpreted as liquid water.

NASA/JPL-Caltech/University of Arizona/JHU

For years, scientists have been debating what might lay under the Martian planet's south polar cap after bright radar reflections were discovered and initially attributed to water.

Now, a new  study involving a researcher from the University of Arizona puts that theory to rest and demonstrates that another material is most likely the answer.

The research, published in Geophysical Research Letters and led by York University planetary scientists in Canada, uses multiple lines of evidence to show that smectites, a common type of clay, can explain all of the observations – putting the Mars lake theory on ice.

"Since being first reported as bodies of water, the scientific community has shown skepticism about the lake hypothesis," said principal investigator Isaac Smith, Canada Research Chair and assistant professor of earth and space science at York University. "And recent publications questioned if it was even possible to have liquid water."

Papers in 2018 and 2021 demonstrated that the amount of salt and heat required to thaw ice at the bottom of the polar cap was many times more than Mars provides, and recent evidence showing these radar detections are much more widespread – to places even harder to thaw ice – put the idea further into question.

Stefano Nerozzi

The research team – which included Stefano Nerozzi , a postdoctoral fellow in the University of Arizona Lunar and Planetary Laboratory and Department of Geosciences , as well as researchers from Cornell, Purdue and Tulane universities – used experimental and modelling work to demonstrate that smectites can better explain the radar observations made by the MARSIS instrument aboard the European Space Agency's Mars Express orbiter. Further, they found spectral evidence that smectites are present at the edges of the south polar cap.

"Smectites are very abundant on Mars, covering about half the planet, especially in the Southern Hemisphere," said Smith. "That knowledge, along with the radar properties of smectites at cryogenic temperatures, points to them being the most likely explanation to the riddle."

"Based on observations, the first reason the bright reflectors cannot be water is because some of them continue from underground onto the surface. If that is the case, then we should see springs, which we don't," Nerozzi said. "Not only that, but multiple reflectors are stacked on top of each other, and some are even found right in the middle of the polar cap. If this were water, this would be physically impossible."

Experiments also measured the radar characteristics of hydrated smectites at room temperature and cryogenic temperatures. The radar characteristics in question are two numbers that represent the real and imaginary parts of the permittivity, which tells you about the material's electrical properties and response to the radio waves employed by radars. Both numbers are important for fully characterizing a material, but the 2018 study used modeling that included only the real part of the dielectric value, leaving out certain classes of materials from being considered – namely clays.

Once the experimental measurements were completed, data was evaluated using code. It was in these simulations that researchers found that unlike the original liquid water hypothesis, frozen clays have numbers just right to explain all the reflections.

Smectites are a class of clay that is formed when basalt – the volcanic rock that comprises most of Mars' surface – breaks down chemically in the presence of liquid water. 

"Detecting possible clay minerals in and below the south polar ice cap is important because it tells us that the ice includes sediments that have interacted with water sometime in the past, either in the ice cap or before the ice was there," said Briony Horgan, co-author and associate professor of earth, atmospheric and planetary sciences at Purdue University. "So, while our work shows that there may not be liquid water and an associated habitable environment for life under the cap today, it does tell us about water that existed in this area in the past."

To support the new hypothesis, Smith conducted experiments in his lab with equipment designed for measuring dielectric values. To simulate the conditions beneath Mars' south polar cap as best as possible, his team froze the clays to minus 50 degrees Celsius and measured them again, something that had never been done before. Smith adds that the infrared absorptions attributable to these minerals are present in south polar orbital visible-near infrared reflectance spectra. Because these minerals are both present at the south pole and can cause the reflections, the team believes this to be a more viable scenario than the presence of liquid water. No salt or heat is required.

"We used our lab measurements of clay minerals as the input for a radar reflection model and found that the results of the model matched very well with the real, observed data," said Dan Lalich, postdoctoral researcher at the Cornell Center for Astrophysics and Planetary Science at Cornell University and second author on the study. "While it's disappointing that liquid water might not actually be present below the ice today, this is still a cool observation that might help us learn more about conditions on ancient Mars."

"We analyzed the MARSIS radar data and identified observations with high-power values at the base of the south polar layered deposits, both in the proposed lake region and elsewhere," said Jenny Whitten, co-author and planetary scientist in the Department of Earth and Environmental Sciences at Tulane University.

Putting the results in perspective Smith says the answer is clear.

"Now, we have the trifecta. One, we measured dielectric properties of materials that are known to exist over 50% of Mars' surface and found them to have very high values. Two, we modelled how those numbers would respond in Mars' south-polar conditions and found them to match the radar observations well. Three, we demonstrated that these minerals are at the south pole. Because the liquid water theory required incredible amounts of heat, which is six to eight times more than Mars provides, and more salt than Mars has, it was already implausible. Now, the clays can explain the observations with absolutely no qualifiers or asterisks."

Resources for the Media

Mikayla Mace Kelley Science Writer, University Communications [email protected] 520-621-1878

Stefano Nerozzi [email protected] 520-621-6963

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