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How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

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

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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4.14: Experiments and Hypotheses

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Now we’ll focus on the methods of scientific inquiry. Science often involves making observations and developing hypotheses. Experiments and further observations are often used to test the hypotheses.

A scientific experiment is a carefully organized procedure in which the scientist intervenes in a system to change something, then observes the result of the change. Scientific inquiry often involves doing experiments, though not always. For example, a scientist studying the mating behaviors of ladybugs might begin with detailed observations of ladybugs mating in their natural habitats. While this research may not be experimental, it is scientific: it involves careful and verifiable observation of the natural world. The same scientist might then treat some of the ladybugs with a hormone hypothesized to trigger mating and observe whether these ladybugs mated sooner or more often than untreated ones. This would qualify as an experiment because the scientist is now making a change in the system and observing the effects.

Forming a Hypothesis

When conducting scientific experiments, researchers develop hypotheses to guide experimental design. A hypothesis is a suggested explanation that is both testable and falsifiable. You must be able to test your hypothesis, and it must be possible to prove your hypothesis true or false.

For example, Michael observes that maple trees lose their leaves in the fall. He might then propose a possible explanation for this observation: “cold weather causes maple trees to lose their leaves in the fall.” This statement is testable. He could grow maple trees in a warm enclosed environment such as a greenhouse and see if their leaves still dropped in the fall. The hypothesis is also falsifiable. If the leaves still dropped in the warm environment, then clearly temperature was not the main factor in causing maple leaves to drop in autumn.

In the Try It below, you can practice recognizing scientific hypotheses. As you consider each statement, try to think as a scientist would: can I test this hypothesis with observations or experiments? Is the statement falsifiable? If the answer to either of these questions is “no,” the statement is not a valid scientific hypothesis.

Practice Questions

Determine whether each following statement is a scientific hypothesis.

No. This statement is not testable or falsifiable.
No. This statement is not testable.
No. This statement is not falsifiable.
Yes. This statement is testable and falsifiable.

[reveal-answer q=”429550″] Show Answers [/reveal-answer] [hidden-answer a=”429550″]

d: Yes. This statement is testable and falsifiable. This could be tested with a number of different kinds of observations and experiments, and it is possible to gather evidence that indicates that air pollution is not linked with asthma.
a: No. This statement is not testable or falsifiable. “Bad thoughts and behaviors” are excessively vague and subjective variables that would be impossible to measure or agree upon in a reliable way. The statement might be “falsifiable” if you came up with a counterexample: a “wicked” place that was not punished by a natural disaster. But some would question whether the people in that place were really wicked, and others would continue to predict that a natural disaster was bound to strike that place at some point. There is no reason to suspect that people’s immoral behavior affects the weather unless you bring up the intervention of a supernatural being, making this idea even harder to test.

[/hidden-answer]

Testing a Vaccine

Let’s examine the scientific process by discussing an actual scientific experiment conducted by researchers at the University of Washington. These researchers investigated whether a vaccine may reduce the incidence of the human papillomavirus (HPV). The experimental process and results were published in an article titled, “ A controlled trial of a human papillomavirus type 16 vaccine .”

Preliminary observations made by the researchers who conducted the HPV experiment are listed below:

Human papillomavirus (HPV) is the most common sexually transmitted virus in the United States.
There are about 40 different types of HPV. A significant number of people that have HPV are unaware of it because many of these viruses cause no symptoms.
Some types of HPV can cause cervical cancer.
About 4,000 women a year die of cervical cancer in the United States.

Practice Question

Researchers have developed a potential vaccine against HPV and want to test it. What is the first testable hypothesis that the researchers should study?

HPV causes cervical cancer.
People should not have unprotected sex with many partners.
People who get the vaccine will not get HPV.
The HPV vaccine will protect people against cancer.

[reveal-answer q=”20917″] Show Answer [/reveal-answer] [hidden-answer a=”20917″]Hypothesis A is not the best choice because this information is already known from previous studies. Hypothesis B is not testable because scientific hypotheses are not value statements; they do not include judgments like “should,” “better than,” etc. Scientific evidence certainly might support this value judgment, but a hypothesis would take a different form: “Having unprotected sex with many partners increases a person’s risk for cervical cancer.” Before the researchers can test if the vaccine protects against cancer (hypothesis D), they want to test if it protects against the virus. This statement will make an excellent hypothesis for the next study. The researchers should first test hypothesis C—whether or not the new vaccine can prevent HPV.[/hidden-answer]

Experimental Design

You’ve successfully identified a hypothesis for the University of Washington’s study on HPV: People who get the HPV vaccine will not get HPV.

The next step is to design an experiment that will test this hypothesis. There are several important factors to consider when designing a scientific experiment. First, scientific experiments must have an experimental group. This is the group that receives the experimental treatment necessary to address the hypothesis.

The experimental group receives the vaccine, but how can we know if the vaccine made a difference? Many things may change HPV infection rates in a group of people over time. To clearly show that the vaccine was effective in helping the experimental group, we need to include in our study an otherwise similar control group that does not get the treatment. We can then compare the two groups and determine if the vaccine made a difference. The control group shows us what happens in the absence of the factor under study.

However, the control group cannot get “nothing.” Instead, the control group often receives a placebo. A placebo is a procedure that has no expected therapeutic effect—such as giving a person a sugar pill or a shot containing only plain saline solution with no drug. Scientific studies have shown that the “placebo effect” can alter experimental results because when individuals are told that they are or are not being treated, this knowledge can alter their actions or their emotions, which can then alter the results of the experiment.

Moreover, if the doctor knows which group a patient is in, this can also influence the results of the experiment. Without saying so directly, the doctor may show—through body language or other subtle cues—his or her views about whether the patient is likely to get well. These errors can then alter the patient’s experience and change the results of the experiment. Therefore, many clinical studies are “double blind.” In these studies, neither the doctor nor the patient knows which group the patient is in until all experimental results have been collected.

Both placebo treatments and double-blind procedures are designed to prevent bias. Bias is any systematic error that makes a particular experimental outcome more or less likely. Errors can happen in any experiment: people make mistakes in measurement, instruments fail, computer glitches can alter data. But most such errors are random and don’t favor one outcome over another. Patients’ belief in a treatment can make it more likely to appear to “work.” Placebos and double-blind procedures are used to level the playing field so that both groups of study subjects are treated equally and share similar beliefs about their treatment.

The scientists who are researching the effectiveness of the HPV vaccine will test their hypothesis by separating 2,392 young women into two groups: the control group and the experimental group. Answer the following questions about these two groups.

This group is given a placebo.
This group is deliberately infected with HPV.
This group is given nothing.
This group is given the HPV vaccine.

[reveal-answer q=”918962″] Show Answers [/reveal-answer] [hidden-answer a=”918962″]

a: This group is given a placebo. A placebo will be a shot, just like the HPV vaccine, but it will have no active ingredient. It may change peoples’ thinking or behavior to have such a shot given to them, but it will not stimulate the immune systems of the subjects in the same way as predicted for the vaccine itself.
d: This group is given the HPV vaccine. The experimental group will receive the HPV vaccine and researchers will then be able to see if it works, when compared to the control group.

Experimental Variables

A variable is a characteristic of a subject (in this case, of a person in the study) that can vary over time or among individuals. Sometimes a variable takes the form of a category, such as male or female; often a variable can be measured precisely, such as body height. Ideally, only one variable is different between the control group and the experimental group in a scientific experiment. Otherwise, the researchers will not be able to determine which variable caused any differences seen in the results. For example, imagine that the people in the control group were, on average, much more sexually active than the people in the experimental group. If, at the end of the experiment, the control group had a higher rate of HPV infection, could you confidently determine why? Maybe the experimental subjects were protected by the vaccine, but maybe they were protected by their low level of sexual contact.

To avoid this situation, experimenters make sure that their subject groups are as similar as possible in all variables except for the variable that is being tested in the experiment. This variable, or factor, will be deliberately changed in the experimental group. The one variable that is different between the two groups is called the independent variable. An independent variable is known or hypothesized to cause some outcome. Imagine an educational researcher investigating the effectiveness of a new teaching strategy in a classroom. The experimental group receives the new teaching strategy, while the control group receives the traditional strategy. It is the teaching strategy that is the independent variable in this scenario. In an experiment, the independent variable is the variable that the scientist deliberately changes or imposes on the subjects.

Dependent variables are known or hypothesized consequences; they are the effects that result from changes or differences in an independent variable. In an experiment, the dependent variables are those that the scientist measures before, during, and particularly at the end of the experiment to see if they have changed as expected. The dependent variable must be stated so that it is clear how it will be observed or measured. Rather than comparing “learning” among students (which is a vague and difficult to measure concept), an educational researcher might choose to compare test scores, which are very specific and easy to measure.

In any real-world example, many, many variables MIGHT affect the outcome of an experiment, yet only one or a few independent variables can be tested. Other variables must be kept as similar as possible between the study groups and are called control variables . For our educational research example, if the control group consisted only of people between the ages of 18 and 20 and the experimental group contained people between the ages of 30 and 35, we would not know if it was the teaching strategy or the students’ ages that played a larger role in the results. To avoid this problem, a good study will be set up so that each group contains students with a similar age profile. In a well-designed educational research study, student age will be a controlled variable, along with other possibly important factors like gender, past educational achievement, and pre-existing knowledge of the subject area.

What is the independent variable in this experiment?

Sex (all of the subjects will be female)
Presence or absence of the HPV vaccine
Presence or absence of HPV (the virus)

[reveal-answer q=”68680″]Show Answer[/reveal-answer] [hidden-answer a=”68680″]Answer b. Presence or absence of the HPV vaccine. This is the variable that is different between the control and the experimental groups. All the subjects in this study are female, so this variable is the same in all groups. In a well-designed study, the two groups will be of similar age. The presence or absence of the virus is what the researchers will measure at the end of the experiment. Ideally the two groups will both be HPV-free at the start of the experiment.

List three control variables other than age.

[practice-area rows=”3″][/practice-area] [reveal-answer q=”903121″]Show Answer[/reveal-answer] [hidden-answer a=”903121″]Some possible control variables would be: general health of the women, sexual activity, lifestyle, diet, socioeconomic status, etc.

What is the dependent variable in this experiment?

Sex (male or female)
Rates of HPV infection
Age (years)

[reveal-answer q=”907103″]Show Answer[/reveal-answer] [hidden-answer a=”907103″]Answer b. Rates of HPV infection. The researchers will measure how many individuals got infected with HPV after a given period of time.[/hidden-answer]

Contributors and Attributions

Revision and adaptation. Authored by : Shelli Carter and Lumen Learning. Provided by : Lumen Learning. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
Scientific Inquiry. Provided by : Open Learning Initiative. Located at : https://oli.cmu.edu/jcourse/workbook/activity/page?context=434a5c2680020ca6017c03488572e0f8 . Project : Introduction to Biology (Open + Free). License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

Module 1: Introduction to Biology

Experiments and hypotheses, learning outcomes.

Form a hypothesis and use it to design a scientific experiment

Forming a Hypothesis

When conducting scientific experiments, researchers develop hypotheses to guide experimental design. A hypothesis is a suggested explanation that is both testable and falsifiable. You must be able to test your hypothesis through observations and research, and it must be possible to prove your hypothesis false.

Practice Questions

Determine whether each following statement is a scientific hypothesis.

Air pollution from automobile exhaust can trigger symptoms in people with asthma.

No. This statement is not testable or falsifiable.
No. This statement is not testable.
No. This statement is not falsifiable.
Yes. This statement is testable and falsifiable.

Natural disasters, such as tornadoes, are punishments for bad thoughts and behaviors.

a: No. This statement is not testable or falsifiable. “Bad thoughts and behaviors” are excessively vague and subjective variables that would be impossible to measure or agree upon in a reliable way. The statement might be “falsifiable” if you came up with a counterexample: a “wicked” place that was not punished by a natural disaster. But some would question whether the people in that place were really wicked, and others would continue to predict that a natural disaster was bound to strike that place at some point. There is no reason to suspect that people’s immoral behavior affects the weather unless you bring up the intervention of a supernatural being, making this idea even harder to test.

Testing a Vaccine

Preliminary observations made by the researchers who conducted the HPV experiment are listed below:

Human papillomavirus (HPV) is the most common sexually transmitted virus in the United States.
There are about 40 different types of HPV. A significant number of people that have HPV are unaware of it because many of these viruses cause no symptoms.
Some types of HPV can cause cervical cancer.
About 4,000 women a year die of cervical cancer in the United States.

Practice Question

Researchers have developed a potential vaccine against HPV and want to test it. What is the first testable hypothesis that the researchers should study?

HPV causes cervical cancer.
People should not have unprotected sex with many partners.
People who get the vaccine will not get HPV.
The HPV vaccine will protect people against cancer.

Experimental Design

You’ve successfully identified a hypothesis for the University of Washington’s study on HPV: People who get the HPV vaccine will not get HPV.

Moreover, if the doctor knows which group a patient is in, this can also influence the results of the experiment. Without saying so directly, the doctor may show—through body language or other subtle cues—their views about whether the patient is likely to get well. These errors can then alter the patient’s experience and change the results of the experiment. Therefore, many clinical studies are “double blind.” In these studies, neither the doctor nor the patient knows which group the patient is in until all experimental results have been collected.

This group is given a placebo.
This group is deliberately infected with HPV.
This group is given nothing.
This group is given the HPV vaccine.
a: This group is given a placebo. A placebo will be a shot, just like the HPV vaccine, but it will have no active ingredient. It may change peoples’ thinking or behavior to have such a shot given to them, but it will not stimulate the immune systems of the subjects in the same way as predicted for the vaccine itself.
d: This group is given the HPV vaccine. The experimental group will receive the HPV vaccine and researchers will then be able to see if it works, when compared to the control group.

Experimental Variables

What is the independent variable in this experiment?

Sex (all of the subjects will be female)
Presence or absence of the HPV vaccine
Presence or absence of HPV (the virus)

List three control variables other than age.

What is the dependent variable in this experiment?

Sex (male or female)
Rates of HPV infection
Age (years)
Revision and adaptation. Authored by : Shelli Carter and Lumen Learning. Provided by : Lumen Learning. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
Scientific Inquiry. Provided by : Open Learning Initiative. Located at : https://oli.cmu.edu/jcourse/workbook/activity/page?context=434a5c2680020ca6017c03488572e0f8 . Project : Introduction to Biology (Open + Free). License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

What Are Examples of a Hypothesis?

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Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
B.A., Physics and Mathematics, Hastings College

A hypothesis is an explanation for a set of observations. Here are examples of a scientific hypothesis.

Although you could state a scientific hypothesis in various ways, most hypotheses are either "If, then" statements or forms of the null hypothesis . The null hypothesis is sometimes called the "no difference" hypothesis. The null hypothesis is good for experimentation because it's simple to disprove. If you disprove a null hypothesis, that is evidence for a relationship between the variables you are examining.

Examples of Null Hypotheses

Hyperactivity is unrelated to eating sugar.
All daisies have the same number of petals.
The number of pets in a household is unrelated to the number of people living in it.
A person's preference for a shirt is unrelated to its color.

Examples of If, Then Hypotheses

If you get at least 6 hours of sleep, you will do better on tests than if you get less sleep.
If you drop a ball, it will fall toward the ground.
If you drink coffee before going to bed, then it will take longer to fall asleep.
If you cover a wound with a bandage, then it will heal with less scarring.

Improving a Hypothesis to Make It Testable

You may wish to revise your first hypothesis in order to make it easier to design an experiment to test. For example, let's say you have a bad breakout the morning after eating a lot of greasy food. You may wonder if there is a correlation between eating greasy food and getting pimples. You propose the hypothesis:

Eating greasy food causes pimples.

Next, you need to design an experiment to test this hypothesis. Let's say you decide to eat greasy food every day for a week and record the effect on your face. Then, as a control, you'll avoid greasy food for the next week and see what happens. Now, this is not a good experiment because it does not take into account other factors such as hormone levels, stress, sun exposure, exercise, or any number of other variables that might conceivably affect your skin.

The problem is that you cannot assign cause to your effect . If you eat french fries for a week and suffer a breakout, can you definitely say it was the grease in the food that caused it? Maybe it was the salt. Maybe it was the potato. Maybe it was unrelated to diet. You can't prove your hypothesis. It's much easier to disprove a hypothesis.

So, let's restate the hypothesis to make it easier to evaluate the data:

Getting pimples is unaffected by eating greasy food.

So, if you eat fatty food every day for a week and suffer breakouts and then don't break out the week that you avoid greasy food, you can be pretty sure something is up. Can you disprove the hypothesis? Probably not, since it is so hard to assign cause and effect. However, you can make a strong case that there is some relationship between diet and acne.

If your skin stays clear for the entire test, you may decide to accept your hypothesis . Again, you didn't prove or disprove anything, which is fine

Null Hypothesis Examples
Null Hypothesis Definition and Examples
What Is a Hypothesis? (Science)
Difference Between Independent and Dependent Variables
What Are the Elements of a Good Hypothesis?
Understanding Simple vs Controlled Experiments
What Is a Testable Hypothesis?
What 'Fail to Reject' Means in a Hypothesis Test
Scientific Hypothesis Examples
Scientific Method Vocabulary Terms
How To Design a Science Fair Experiment
An Example of a Hypothesis Test
Definition of a Hypothesis
Six Steps of the Scientific Method
Null Hypothesis and Alternative Hypothesis

What is a scientific hypothesis?

It's the initial building block in the scientific method.

A girl looks at plants in a test tube for a science experiment. What's her scientific hypothesis?

Hypothesis basics

What makes a hypothesis testable.

Types of hypotheses
Hypothesis versus theory

Additional resources

Bibliography.

A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method . Many describe it as an "educated guess" based on prior knowledge and observation. While this is true, a hypothesis is more informed than a guess. While an "educated guess" suggests a random prediction based on a person's expertise, developing a hypothesis requires active observation and background research.

The basic idea of a hypothesis is that there is no predetermined outcome. For a solution to be termed a scientific hypothesis, it has to be an idea that can be supported or refuted through carefully crafted experimentation or observation. This concept, called falsifiability and testability, was advanced in the mid-20th century by Austrian-British philosopher Karl Popper in his famous book "The Logic of Scientific Discovery" (Routledge, 1959).

A key function of a hypothesis is to derive predictions about the results of future experiments and then perform those experiments to see whether they support the predictions.

A hypothesis is usually written in the form of an if-then statement, which gives a possibility (if) and explains what may happen because of the possibility (then). The statement could also include "may," according to California State University, Bakersfield .

Here are some examples of hypothesis statements:

If garlic repels fleas, then a dog that is given garlic every day will not get fleas.
If sugar causes cavities, then people who eat a lot of candy may be more prone to cavities.
If ultraviolet light can damage the eyes, then maybe this light can cause blindness.

A useful hypothesis should be testable and falsifiable. That means that it should be possible to prove it wrong. A theory that can't be proved wrong is nonscientific, according to Karl Popper's 1963 book " Conjectures and Refutations ."

An example of an untestable statement is, "Dogs are better than cats." That's because the definition of "better" is vague and subjective. However, an untestable statement can be reworded to make it testable. For example, the previous statement could be changed to this: "Owning a dog is associated with higher levels of physical fitness than owning a cat." With this statement, the researcher can take measures of physical fitness from dog and cat owners and compare the two.

Types of scientific hypotheses

Elementary-age students study alternative energy using homemade windmills during public school science class.

In an experiment, researchers generally state their hypotheses in two ways. The null hypothesis predicts that there will be no relationship between the variables tested, or no difference between the experimental groups. The alternative hypothesis predicts the opposite: that there will be a difference between the experimental groups. This is usually the hypothesis scientists are most interested in, according to the University of Miami .

For example, a null hypothesis might state, "There will be no difference in the rate of muscle growth between people who take a protein supplement and people who don't." The alternative hypothesis would state, "There will be a difference in the rate of muscle growth between people who take a protein supplement and people who don't."

If the results of the experiment show a relationship between the variables, then the null hypothesis has been rejected in favor of the alternative hypothesis, according to the book " Research Methods in Psychology " (BCcampus, 2015).

There are other ways to describe an alternative hypothesis. The alternative hypothesis above does not specify a direction of the effect, only that there will be a difference between the two groups. That type of prediction is called a two-tailed hypothesis. If a hypothesis specifies a certain direction — for example, that people who take a protein supplement will gain more muscle than people who don't — it is called a one-tailed hypothesis, according to William M. K. Trochim , a professor of Policy Analysis and Management at Cornell University.

Sometimes, errors take place during an experiment. These errors can happen in one of two ways. A type I error is when the null hypothesis is rejected when it is true. This is also known as a false positive. A type II error occurs when the null hypothesis is not rejected when it is false. This is also known as a false negative, according to the University of California, Berkeley .

A hypothesis can be rejected or modified, but it can never be proved correct 100% of the time. For example, a scientist can form a hypothesis stating that if a certain type of tomato has a gene for red pigment, that type of tomato will be red. During research, the scientist then finds that each tomato of this type is red. Though the findings confirm the hypothesis, there may be a tomato of that type somewhere in the world that isn't red. Thus, the hypothesis is true, but it may not be true 100% of the time.

Scientific theory vs. scientific hypothesis

The best hypotheses are simple. They deal with a relatively narrow set of phenomena. But theories are broader; they generally combine multiple hypotheses into a general explanation for a wide range of phenomena, according to the University of California, Berkeley . For example, a hypothesis might state, "If animals adapt to suit their environments, then birds that live on islands with lots of seeds to eat will have differently shaped beaks than birds that live on islands with lots of insects to eat." After testing many hypotheses like these, Charles Darwin formulated an overarching theory: the theory of evolution by natural selection.

"Theories are the ways that we make sense of what we observe in the natural world," Tanner said. "Theories are structures of ideas that explain and interpret facts."

Read more about writing a hypothesis, from the American Medical Writers Association.
Find out why a hypothesis isn't always necessary in science, from The American Biology Teacher.
Learn about null and alternative hypotheses, from Prof. Essa on YouTube .

Encyclopedia Britannica. Scientific Hypothesis. Jan. 13, 2022. https://www.britannica.com/science/scientific-hypothesis

Karl Popper, "The Logic of Scientific Discovery," Routledge, 1959.

California State University, Bakersfield, "Formatting a testable hypothesis." https://www.csub.edu/~ddodenhoff/Bio100/Bio100sp04/formattingahypothesis.htm

Karl Popper, "Conjectures and Refutations," Routledge, 1963.

Price, P., Jhangiani, R., & Chiang, I., "Research Methods of Psychology — 2nd Canadian Edition," BCcampus, 2015.‌

University of Miami, "The Scientific Method" http://www.bio.miami.edu/dana/161/evolution/161app1_scimethod.pdf

William M.K. Trochim, "Research Methods Knowledge Base," https://conjointly.com/kb/hypotheses-explained/

University of California, Berkeley, "Multiple Hypothesis Testing and False Discovery Rate" https://www.stat.berkeley.edu/~hhuang/STAT141/Lecture-FDR.pdf

University of California, Berkeley, "Science at multiple levels" https://undsci.berkeley.edu/article/0_0_0/howscienceworks_19

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How to Write a Great Hypothesis

Hypothesis Definition, 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.

Operationalization

Hypothesis Types

Hypotheses examples.

Collecting Data

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. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "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."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. 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. At this point, researchers then 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 numerous 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 adage 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.

How to Formulate a Good Hypothesis

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.

The Importance of Operational Definitions

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.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

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 various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. 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. For example, 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.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

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 there is a relationship between one independent variable and one dependent variable.
Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
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 population sample 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."
"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."
"Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

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:

"There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
"There is no difference in scores on a memory recall task between children and adults."
"There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

"People who take St. John's wort supplements will have less anxiety than those who do not."
"Adults will perform better on a memory task than children."
"Children who play first-person shooter games will show higher levels of aggression than children who do not."

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 conducting an experiment is difficult or impossible. 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 examine how the variables are related. This 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.

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.

Thompson WH, Skau S. On the scope of scientific hypotheses . R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:]. Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ? PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies . Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

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."

Writing an Introduction for a Scientific Paper

Dr. michelle harris, dr. janet batzli, biocore.

This section provides guidelines on how to construct a solid introduction to a scientific paper including background information, study question , biological rationale, hypothesis , and general approach . If the Introduction is done well, there should be no question in the reader’s mind why and on what basis you have posed a specific hypothesis.

Broad Question : based on an initial observation (e.g., “I see a lot of guppies close to the shore. Do guppies like living in shallow water?”). This observation of the natural world may inspire you to investigate background literature or your observation could be based on previous research by others or your own pilot study. Broad questions are not always included in your written text, but are essential for establishing the direction of your research.

Background Information : key issues, concepts, terminology, and definitions needed to understand the biological rationale for the experiment. It often includes a summary of findings from previous, relevant studies. Remember to cite references, be concise, and only include relevant information given your audience and your experimental design. Concisely summarized background information leads to the identification of specific scientific knowledge gaps that still exist. (e.g., “No studies to date have examined whether guppies do indeed spend more time in shallow water.”)

Testable Question : these questions are much more focused than the initial broad question, are specific to the knowledge gap identified, and can be addressed with data. (e.g., “Do guppies spend different amounts of time in water <1 meter deep as compared to their time in water that is >1 meter deep?”)

Biological Rationale : describes the purpose of your experiment distilling what is known and what is not known that defines the knowledge gap that you are addressing. The “BR” provides the logic for your hypothesis and experimental approach, describing the biological mechanism and assumptions that explain why your hypothesis should be true.

The biological rationale is based on your interpretation of the scientific literature, your personal observations, and the underlying assumptions you are making about how you think the system works. If you have written your biological rationale, your reader should see your hypothesis in your introduction section and say to themselves, “Of course, this hypothesis seems very logical based on the rationale presented.”

A thorough rationale defines your assumptions about the system that have not been revealed in scientific literature or from previous systematic observation. These assumptions drive the direction of your specific hypothesis or general predictions.
Defining the rationale is probably the most critical task for a writer, as it tells your reader why your research is biologically meaningful. It may help to think about the rationale as an answer to the questions— how is this investigation related to what we know, what assumptions am I making about what we don’t yet know, AND how will this experiment add to our knowledge? *There may or may not be broader implications for your study; be careful not to overstate these (see note on social justifications below).
Expect to spend time and mental effort on this. You may have to do considerable digging into the scientific literature to define how your experiment fits into what is already known and why it is relevant to pursue.
Be open to the possibility that as you work with and think about your data, you may develop a deeper, more accurate understanding of the experimental system. You may find the original rationale needs to be revised to reflect your new, more sophisticated understanding.
As you progress through Biocore and upper level biology courses, your rationale should become more focused and matched with the level of study e ., cellular, biochemical, or physiological mechanisms that underlie the rationale. Achieving this type of understanding takes effort, but it will lead to better communication of your science.

***Special note on avoiding social justifications: You should not overemphasize the relevance of your experiment and the possible connections to large-scale processes. Be realistic and logical —do not overgeneralize or state grand implications that are not sensible given the structure of your experimental system. Not all science is easily applied to improving the human condition. Performing an investigation just for the sake of adding to our scientific knowledge (“pure or basic science”) is just as important as applied science. In fact, basic science often provides the foundation for applied studies.

Hypothesis / Predictions : specific prediction(s) that you will test during your experiment. For manipulative experiments, the hypothesis should include the independent variable (what you manipulate), the dependent variable(s) (what you measure), the organism or system , the direction of your results, and comparison to be made.

If you are doing a systematic observation , your hypothesis presents a variable or set of variables that you predict are important for helping you characterize the system as a whole, or predict differences between components/areas of the system that help you explain how the system functions or changes over time.

Experimental Approach : Briefly gives the reader a general sense of the experiment, the type of data it will yield, and the kind of conclusions you expect to obtain from the data. Do not confuse the experimental approach with the experimental protocol . The experimental protocol consists of the detailed step-by-step procedures and techniques used during the experiment that are to be reported in the Methods and Materials section.

Some Final Tips on Writing an Introduction

As you progress through the Biocore sequence, for instance, from organismal level of Biocore 301/302 to the cellular level in Biocore 303/304, we expect the contents of your “Introduction” paragraphs to reflect the level of your coursework and previous writing experience. For example, in Biocore 304 (Cell Biology Lab) biological rationale should draw upon assumptions we are making about cellular and biochemical processes.
Be Concise yet Specific: Remember to be concise and only include relevant information given your audience and your experimental design. As you write, keep asking, “Is this necessary information or is this irrelevant detail?” For example, if you are writing a paper claiming that a certain compound is a competitive inhibitor to the enzyme alkaline phosphatase and acts by binding to the active site, you need to explain (briefly) Michaelis-Menton kinetics and the meaning and significance of Km and Vmax. This explanation is not necessary if you are reporting the dependence of enzyme activity on pH because you do not need to measure Km and Vmax to get an estimate of enzyme activity.
Another example: if you are writing a paper reporting an increase in Daphnia magna heart rate upon exposure to caffeine you need not describe the reproductive cycle of magna unless it is germane to your results and discussion. Be specific and concrete, especially when making introductory or summary statements.

Where Do You Discuss Pilot Studies? Many times it is important to do pilot studies to help you get familiar with your experimental system or to improve your experimental design. If your pilot study influences your biological rationale or hypothesis, you need to describe it in your Introduction. If your pilot study simply informs the logistics or techniques, but does not influence your rationale, then the description of your pilot study belongs in the Materials and Methods section.

How will introductions be evaluated? The following is part of the rubric we will be using to evaluate your papers.

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Education and Communications
Science Writing

How to Write a Good Lab Conclusion in Science

Last Updated: May 31, 2024 Fact Checked

This article was co-authored by Bess Ruff, MA . Bess Ruff is a Geography PhD student at Florida State University. She received her MA in Environmental Science and Management from the University of California, Santa Barbara in 2016. She has conducted survey work for marine spatial planning projects in the Caribbean and provided research support as a graduate fellow for the Sustainable Fisheries Group. There are 11 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 1,764,504 times.

A lab report describes an entire experiment from start to finish, outlining the procedures, reporting results, and analyzing data. The report is used to demonstrate what has been learned, and it will provide a way for other people to see your process for the experiment and understand how you arrived at your conclusions. The conclusion is an integral part of the report; this is the section that reiterates the experiment’s main findings and gives the reader an overview of the lab trial. Writing a solid conclusion to your lab report will demonstrate that you’ve effectively learned the objectives of your assignment.

Outlining Your Conclusion

Restate : Restate the lab experiment by describing the assignment.
Explain : Explain the purpose of the lab experiment. What were you trying to figure out or discover? Talk briefly about the procedure you followed to complete the lab.
Results : Explain your results. Confirm whether or not your hypothesis was supported by the results.
Uncertainties : Account for uncertainties and errors. Explain, for example, if there were other circumstances beyond your control that might have impacted the experiment’s results.
New : Discuss new questions or discoveries that emerged from the experiment.

Your assignment may also have specific questions that need to be answered. Make sure you answer these fully and coherently in your conclusion.

Discussing the Experiment and Hypothesis

Step 1 Introduce the experiment in your conclusion.

If you tried the experiment more than once, describe the reasons for doing so. Discuss changes that you made in your procedures.
Brainstorm ways to explain your results in more depth. Go back through your lab notes, paying particular attention to the results you observed. [5] X Trustworthy Source University of North Carolina Writing Center UNC's on-campus and online instructional service that provides assistance to students, faculty, and others during the writing process Go to source

Step 3 Describe what you discovered briefly.

Start this section with wording such as, “The results showed that…”
You don’t need to give the raw data here. Just summarize the main points, calculate averages, or give a range of data to give an overall picture to the reader.
Make sure to explain whether or not any statistical analyses were significant, and to what degree, such as 1%, 5%, or 10%.

Step 4 Comment on whether or not your hypothesis is supported.

Use simple language such as, “The results supported the hypothesis,” or “The results did not support the hypothesis.”

Step 5 Link your results to your hypothesis.

Demonstrating What You Have Learned

Step 1 Describe what you learned in the lab.

If it’s not clear in your conclusion what you learned from the lab, start off by writing, “In this lab, I learned…” This will give the reader a heads up that you will be describing exactly what you learned.
Add details about what you learned and how you learned it. Adding dimension to your learning outcomes will convince your reader that you did, in fact, learn from the lab. Give specifics about how you learned that molecules will act in a particular environment, for example.
Describe how what you learned in the lab could be applied to a future experiment.

Step 2 Answer specific questions given in the assignment.

On a new line, write the question in italics. On the next line, write the answer to the question in regular text.

Step 3 Explain whether you achieved the experiment’s objectives.

If your experiment did not achieve the objectives, explain or speculate why not.

Wrapping Up Your Conclusion

Step 1 Describe possible errors that may have occurred.

If your experiment raised questions that your collected data can’t answer, discuss this here.

Describe what is new or innovative about your research.
This can often set you apart from your classmates, many of whom will just write up the barest of discussion and conclusion.

Finalizing Your Lab Report

Community Q&A

Ensure the language used is straightforward with specific details. Try not to drift off topic. Thanks Helpful 1 Not Helpful 0
Once again, avoid using personal pronouns (I, myself, we, our group) in a lab report. The first-person point-of-view is often seen as subjective, whereas science is based on objectivity. Thanks Helpful 1 Not Helpful 0
If you include figures or tables in your conclusion, be sure to include a brief caption or label so that the reader knows what the figures refer to. Also, discuss the figures briefly in the text of your report. Thanks Helpful 1 Not Helpful 0

Take care with writing your lab report when working in a team setting. While the lab experiment may be a collaborative effort, your lab report is your own work. If you copy sections from someone else’s report, this will be considered plagiarism. Thanks Helpful 4 Not Helpful 0

↑ https://phoenixcollege.libguides.com/LabReportWriting/introduction
↑ https://www.hcs-k12.org/userfiles/354/Classes/18203/conclusionwriting.pdf
↑ https://www.education.vic.gov.au/school/teachers/teachingresources/discipline/english/literacy/Pages/puttingittogether.aspx
↑ https://writingcenter.unc.edu/tips-and-tools/brainstorming/
↑ https://advice.writing.utoronto.ca/types-of-writing/lab-report/
↑ http://www.socialresearchmethods.net/kb/hypothes.php
↑ https://libguides.usc.edu/writingguide/conclusion
↑ https://libguides.usc.edu/writingguide/introduction/researchproblem
↑ http://writingcenter.unc.edu/handouts/scientific-reports/
↑ https://phoenixcollege.libguides.com/LabReportWriting/labreportstyle
↑ https://writingcenter.unc.edu/tips-and-tools/editing-and-proofreading/

About This Article

To write a good lab conclusion in science, start with restating the lab experiment by describing the assignment. Next, explain what you were trying to discover or figure out by doing the experiment. Then, list your results and explain how they confirmed or did not confirm your hypothesis. Additionally, include any uncertainties, such as circumstances beyond your control that may have impacted the results. Finally, discuss any new questions or discoveries that emerged from the experiment. For more advice, including how to wrap up your lab report with a final statement, keep reading. Did this summary help you? Yes No

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Research Article

How does green credit guidelines affect environmentally friendly enterprises’ ESG? A quasi-natural experiment from China

Roles Conceptualization, Data curation, Methodology, Software, Writing – original draft

Affiliation School of Economics and Management, Chongqing Jiaotong University, Chongqing, China

Roles Data curation, Writing – review & editing

* E-mail: [email protected]

Roles Supervision, Visualization

Shengyu Xu,
Jinqiu Yang,

Published: May 29, 2024
https://doi.org/10.1371/journal.pone.0304384
Reader Comments

Following decades of extensive economic development, promoting the transition to greening and decarbonization in economic development have become inevitable choices for controlling environmental pollution and achieving high-quality development in China. Green Credit Guidelines (NIGCG) is a major policy innovation to promote green credit and further improve sustainable economic development. The influence of these guidelines on environmentally friendly enterprises’ sustainable development capacity, proxied by environmental, social, and corporate governance (ESG), has not yet been discussed. Therefore, this study takes the NIGCG issued in 2012 as a quasi-natural experiment, and adopts a propensity score matching–difference-in-differences (PSM-DID) model to test whether the NIGCG has affected ESG in environmentally friendly enterprises from 2009 to 2022. Our results indicate that the NIGCG significantly boosts environmentally friendly enterprises’ ESG, and this finding remains robust to a series of tests. In addition, a mediating effect analysis reveals that the NIGCG affects enterprises’ ESG through research and development (R&D) investment, verifying the Porter hypothesis in China. Finally, we determine that the role of NIGCG in promoting ESG is significantly reflected in the non-politically connected enterprises and enterprises in the eastern region. The empirical results suggest that the authorities should stimulate enterprises’ R&D investments through supporting policies, such as tax reimbursement and government subsidies, and formulate differentiated policies according to the characteristics of enterprises and their regions, so as to improve the effect of NIGCG.

Citation: Xu S, Yang J, Li R (2024) How does green credit guidelines affect environmentally friendly enterprises’ ESG? A quasi-natural experiment from China. PLoS ONE 19(5): e0304384. https://doi.org/10.1371/journal.pone.0304384

Editor: Pengyu Chen, Inner Mongolia University, CHINA

Received: February 4, 2024; Accepted: May 10, 2024; Published: May 29, 2024

Copyright: © 2024 Xu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files. All files are available from the figshare database ( 10.6084/m9.figshare.25671951 ).

Funding: The paper was funded by the National Social Science Fund of China [grant number 22XJY008]. This funders had no role in this study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

Since the Twelfth Five-Year Plan, China has witnessed enormous and rapid economic development. However, this extensive economic development mode caused increasingly serious problems such as environmental pollution, resource depletion, as well as ecological damage. The Chinese authorities have begun to attach importance to environmental protection as well as transitioning the traditional development mode to a sustainable development strategy in recent years. The development of green credit and provision of more social investments for enterprises’ upgrading and transformation is the key to advancing enterprises sustainable development. Under these circumstances, China issued the Green Credit Guidelines (NIGCG) on February 24, 2012, aiming to achieve sustainable development by establishing a green credit guide for financial institutions and increasing support for the green, low-carbon and circular economy. Studying the relationship between the NIGCG and environmental, social, and corporate governance (ESG) in environmentally friendly enterprises is conducive to an in-depth understanding the policy effect of NIGCG and provides policy references for realizing sustainable economic development.

As a critical indicator, ESG is commonly applied to evaluate enterprises’ sustainable development capacity. Therefore, scholars have dedicated considerable research to explore the potential determinants of ESG by examining the performance of enterprise (Lins et al., 2017; Giuli and Kostovetsky, 2014) [ 1 , 2 ], green finance (Xu et al., 2023; Zhang, 2023a; Xue et al., 2023) [ 3 – 5 ], corporate leadership (Dabbebi et al., 2022; Burke, 2021; Liu and Zhang, 2023; Ritz, 2022) [ 6 – 9 ], ownership (Rees and Rodionova, 2015; Nofsinger et al., 2019; Weber, 2014) [ 10 – 12 ], and environmental regulation (Chen et al., 2022; Yan et al., 2022; He et al., 2023; Shu and Tan, 2023; Lu and Cheng, 2023) [ 13 – 17 ]. Nonetheless, there is a lack of discussion remains regarding the relationship between the NIGCG and environmentally friendly enterprises’ ESG.

Regarding the influence of environmental and green financial policies, there are two controversial viewpoints: a) Porter hypothesis (Porter and Van der Linde, 1995), suggesting that suitable environmental regulations can stimulate enterprises’ innovation activities, enhance production efficiency as well as competitiveness [ 18 ]; b) the restriction hypothesis (Jaffe et al., 1995), arguing that stringent environmental regulations can increase the environmental costs of enterprises, crowding out research and development (R&D) investments, and harm productivity [ 19 ]. Thus, it is meaningful to understand which of the above two hypotheses are dominated in the NIGCG’s effect on environmentally friendly enterprises’ ESG.

To fill this research gap, we use the data of Chinese A-shared listed enterprises during 2009–2022, and employ a propensity score matching–difference-in-differences (PSM-DID) approach to investigate the relationship between the NIGCG and enterprises’ ESG. Since the primary purpose of NIGCG is to strategically develop green credit by promoting banks’ green focus and has a significant influence on improving social comprehensive, coordinated, and sustainable development, this policy may contribute to environmentally friendly enterprises’ ESG. Therefore, this study classifies sample enterprises into environmentally friendly enterprises (treatment group) as well as other enterprises (control group). We conduct a common trend test to determine whether PSM-DID is suitable for this research. Moreover, we expect a certain lag in the policy effect. Our benchmark regression confirms that the NIGCG has significantly improved environmentally friendly enterprises’ ESG, and this finding remains robust to a series of tests.

In addition, the mediating effect analysis demonstrates that the investment of R&D is the intermediary channel for NIGCG to affect enterprises’ ESG, verifying the Porter hypothesis for China. This study also employs two heterogeneity tests to discuss the different effects of NIGCG on enterprises’ ESG, including politically connected (PC) enterprises and non-PC enterprises as well as enterprises located in different regions. The empirical findings indicate that the promoting effect of NIGCG on ESG is mainly reflected in enterprises that the non-politically connected and enterprises in the eastern region.

The theoretical contributions of this study include the following. a) Previous research has studied the relationship between the NIGCG and heavy polluters; however, this policy predominantly aims to promote green industrial development. Therefore, our study explores the influence of the NIGCG on environmentally friendly enterprises’ sustainable development capacity at the micro level, which enriches the research related to the effect of policy’s micro level. b) The mediating effect analysis confirms that the NIGCG can motivate enterprises to increase the investment of R&D and enhance production efficiency, and ultimately boosts enterprises’ ESG, verifying the Porter hypothesis in China.

Moreover, the practical contributions are as follows. a) The mediating effect analysis reveals how the NIGCG affects environmentally friendly enterprises’ ESG, suggesting that the authorities should stimulate enterprises’ R&D investments through supporting policies, such as tax reimbursement and government subsidies. b) Because the NIGCG has different effects on various enterprise types, the government should appropriately adjust this policy to emphasize strategic support for specific enterprises. c) The findings of our research may also help other emerging markets attempting to implement green finance policies that are similar to the NIGCG for promoting sustainable economic development.

The rest of this study is organized as follows: In Section 2, we review related literature and develop hypotheses; In Section 3, we introduce methodology, including data and sample, variables, and summary statistics; In Section 4, we report results of empirical analyses; and we conclude this study in Section 5.

2. Literature review and research hypotheses

2.1 literature review.

To examine how environmental as well as environmental finance policies affect the economy, scholars have conducted a number of research. However, the effect of such policies remains controversial.

Some scholars support Porter hypothesis (Porter and Van der Linde, 1995), which asserts that suitable environmental regulations can motivate enterprises to engage in innovation activities that enhance production efficiency as well as competitiveness [ 18 ], including Jaffe and Palmer (1997) [ 20 ], Ramanathan et al. (2017) [ 21 ], Xue et al. (2023) [ 5 ], Wang (2023) [ 22 ], Yu et al. (2023) [ 23 ], Yan et al. (2022) [ 14 ], Zhang et al. (2021) [ 24 ], Zhang (2023b) [ 25 ], Xu et al. (2023) [ 3 ], Li et al. (2022b) [ 26 ], Chi and Yang (2023) [ 27 ], He et al. (2024) [ 28 ], Zhang and He (2024) [ 29 ] and Wang et al. (2023) [ 30 ]. For instance, Jaffe and Palmer (1997) found environmental compliance expenditure to be positively related to enterprises’ R&D spending [ 20 ]. Ramanathan et al. (2017) determined that enterprises’ private benefits from sustainability activities are generally better obtained through actively focusing on environmental regulations and environmental performance [ 21 ]. Zhang et al. (2021) and Xue et al. (2023) found that local the returns on high ESG portfolios green financial policies significantly increase corporate ESG performance [ 5 , 24 ]. Wang (2023) also showed that green finance policies are positively associated with environmentally friendly industries’ green innovation efficiency [ 22 ]. Yan et al. (2022) noted that green financial reform significantly reduces enterprises’ agency costs as well as increases the scale of R&D investment, boosting investment efficiency [ 14 ]. Furthermore, green financial pilot policies can also significantly improve enterprises’ financing scale, and boost sustainable development capacity (Xu et al., 2023; Yu et al., 2023; Zhang, 2023b) [ 3 , 23 , 25 ]. Li et al. (2022b) demonstrated that green finance reduces the enterprise debt cost by improving the social responsibility of enterprises [ 26 ]. Chi and Yang (2023) claimed that green policies can help achieve green economic transformation using market-oriented governance [ 27 ]. He et al. (2024) and Zhang and He (2024) found that green financial system can promote ESG and total factor productivity of environmentally friendly enterprises [ 28 , 29 ]. Wang et al. (2023) found that low-carbon pilot cities can boost urban carbon emission efficiency by elevating the standard of urban innovation and advanced urban industrial structure [ 30 ].

In contrast, some other scholars support the effect of compliance cost, arguing that stringent environmental regulations can increase the environmental costs of enterprises and harm productivity, including Gray (1987) [ 31 ], Gollop and Roberts (1983) [ 32 ], Testa et al. (2011) [ 33 ], Zhao et al. (2023) [ 34 ], Tang et al. (2020) [ 35 ] as well as Hou et al. (2020) [ 36 ]. For example, Gollop and Roberts (1983) showed that emissions regulation limitations would cause a notable increase in enterprises’ production costs [ 32 ]. Gray (1987) noted that environmental regulation weakens the enterprises’ productivity growth [ 31 ]. Testa et al. (2011) argued that direct regulation can significantly harm enterprises’ competitiveness in terms of innovation, intangibility, and green business performance [ 33 ]. Hou et al. (2020) demonstrated that it is difficult to advance green total factor productivity while improving the environment vias the sulfur dioxide emissions trading scheme [ 36 ]. Tang et al. (2020) found that command-and-control environmental regulation is negatively related to enterprises’ total factor productivity [ 35 ]. Zhao et al. (2023) found that the green finance reform affect enterprises’ total factor productivity not through technological innovation, but through environmental protection investments as well as financing constraints [ 34 ].

Finally, some studies have indicated an uncertain relationship between environmental finance policies, environmental policies, and the economy. For example, Alpay et al. (2002) found that U.S. contamination regulations cannot affect the food manufacturing industry’s profitability or productivity in U.S. [ 37 ]. Becker (2011) demonstrated that counties with higher environmental compliance costs have no statistically significant impact on the average manufacturing plant’s productivity [ 38 ]. Wang and Shen (2016) studied the impact of environmental regulation on China’s environmental productivity and found that the effect of environmental regulation on productivity is different in industries [ 39 ]. Wang et al. (2018) showed that water quality regulation does not significantly affect surviving enterprises’ productivity [ 40 ].

2.2 Research hypotheses

The China Banking Regulatory Commission formulated NIGCG on February 24, 2012 to adjust the credit structure, prevent environmental and social risks, better serve the real economy, and further promote the transformation of economic development mode and economic restructuring. Banking financial institutions should improve green credit, increase support for green economy, low-carbon economy, and circular economy, prevent the threat of uncertain environment and society, and further promote the transformation and diversification of quality levels and development models.

Our review of previous research indicates that the impacts of these environmental as well as environmental finance policies on the economy remains controversial. Notably, although a few scholars have explored the relationship between the NIGCG and the ESG of enterprises (Li et al., 2022a; Gao and Liu, 2023; Lei et al., 2023), these studies primarily examined whether the NIGCG affected heavy polluters’ ESG [ 41 – 43 ]. As noted above, the NIGCG aims to promote green credit development by banking institutions to advance sustainable development; therefore, we contend that the NIGCG will also have an impact on environmentally friendly enterprises’ ESG. Furthermore, this study demonstrates that environmental finance policies can increase enterprises’ investment on R&D, which contributes to enterprises’ sustainable development capacity. Therefore, we assert that the NIGCG may affect environmentally friendly enterprises’ R&D investment, which improves such enterprises’ ESG. To verify our conjecture, this study proposes the following hypothesis:

H1: NIGCG implementation affects environmentally friendly enterprises’ ESG.
H2: NIGCG implementation affects environmentally friendly enterprises’ ESG through R&D investment.

3. Data and methodology

3.1 sample description.

This study examines all A-share listed enterprises on Chinese stock markets from 2009 to 2022 as our initial sample. We choose this sample period because data for several variables, such as ESG, are unavailable prior to 2009, and the relevant data are only available until 2022. Finally, the relevant enterprise data are obtained from the CSMAR Database, and the data of ESG are derived from the Sino-Securities’ ESG rating index.

We further clean sample enterprises based on the following six criteria. (1) The ST, *ST and PT listed enterprises are removed as our research focuses on general cases rather. (2) Enterprises in financial sector are deleted, because of the differences in regulation. (3) To control for the influence of the life cycle effect on enterprise operation, the listed enterprises in the Growth Enterprise Market as well as in the Science and Technology Innovation Board are excluded. (Enterprises listed on the Growth Enterprises Market and the Science and Technology Innovation Board Market are smaller than those enterprises listed on the main board. Moreover, the former are normally in the growth stage, while the latter are in the maturation stage.) (4) Enterprises with missing values are excluded from the analysis. (5) We also exclude enterprises with only a first enterprise-year observation because of the IPO effect on enterprises. (6) Finally, to control for extreme values, we winsorize our sample at a 1% level. Totally, the sample period from 2009 to 2022 includes 2,989 enterprises.

3.2 Variables definition

3.2.1 dependent variable..

As mentioned above, ESG can reflect the sustainable development capacity of enterprises. Our study uses Sino-Securities’ ESG data as the dependent variable. Sino-Securities’ ESG data mainly consists of two forms: a) a rating index, that offers AAA, AA… C ratings for listed enterprises, and b) an overall score, that provides numerical scores for listed enterprises. We adopt both of these data as our dependent variables. We transform the ratings index into numbers for our data analysis. For instance, AAA to 9, AA equals to 8, etc., where a larger number indicates an enterprise’s stronger sustainable development capacity.

3.2.2 Independent variables.

We employ EFTim e as the core independent variable, which assigned a value of 1 if the observation is post-event and belongs to an environmentally friendly enterprise, otherwise 0. As noted previously, the NIGCG was implemented on February 24, 2012; thus, we define 2009–2011 as pre-event and 2012–2022 as post-event. We classify sample enterprises into environmentally friendly enterprises (treatment group) as well as others (control group). This study screen environmentally friendly enterprises as follows: a) Environmentally friendly enterprises listed on Peking University’s China Center for Economic Research, the CSMAR, and RESSET databases, and b) environmentally friendly enterprises based on Environmental Industry Climate Index Report. This study identified 157 environmentally friendly enterprises and 2,832 others in our sample.

Fig 1 presents the considerable differences between the treatment and control groups. To eliminate the endogeneity issue caused by potential sample selectivity bias, our research adopts the PSM-DID model to examine whether and how the NIGCG affected environmentally friendly enterprises’ ESG during the sample period. For PSM, we use logit regression for empirical analysis with the matching caliper set to 0.05 and employ a nearest-neighbor, 1:1, no-release matching. Finally, we obtain 157 environmentally friendly enterprises (1,486 enterprise-year observations) and 668 other enterprises (1,486 enterprise-year observations). Fig 2 reveals that the differences across the treatment and control groups become significantly smaller after PSM screening.

PPT PowerPoint slide
PNG larger image
TIFF original image

This figure shows the kernel density curve before PSM.

https://doi.org/10.1371/journal.pone.0304384.g001

This figure shows the kernel density curve after PSM.

https://doi.org/10.1371/journal.pone.0304384.g002

According to the research of Rees and Rodionova (2015), Weber (2014), Burke (2021) and He et al. (2023) [ 7 , 10 , 12 , 15 ], the enterprise-level attributes may also have significant effects on ESG. Therefore, we select the corporate governance and financial fundamental as control variables. Corporate governance consists of three variables, Board size , First , as well as Sedtenth . Financial fundamentals also contain three variables, Age , Lev , and Cash Flow . Appendix A reports the detail of variable definitions.

3.3 Descriptive statistics

The descriptive statistics corresponding to the study’s variables are presented in Table 1 . It can be found that the mean of ESG is 4.1800, and its maximum and minimum values are 7.0000 and 1.0000. The mean of ESG Score is 73.3925, and its maximum and minimum values are 85.2600 and 55.270. EFTime ’s mean value is 0.4277, which indicate that 42.77% of enterprises are categorized as the post-event treatment group. In addition, the mean of First , Sedtenth , Board Size , Age , Lev , and Cash Flow are 0.3787, 0.2232, 2.2342, 2.3456, 0.3639, and 0.2555, respectively. For the definitions of all variables, see Table 2 .

https://doi.org/10.1371/journal.pone.0304384.t001

https://doi.org/10.1371/journal.pone.0304384.t002

Table 3 demonstrates that the correlation coefficient between variables is small, excepting ESG and ESG Score . The correlation coefficient between ESG and ESG Score is 0.9623; however, these two factors are dependent variables and would not be used in the same regression model. Thus, the multicollinearity issue is unlikely to be a problem in our empirical analysis.

https://doi.org/10.1371/journal.pone.0304384.t003

4. Empirical analyses

4.1 common trend test.

As shown in Table 4 , the coefficients’ confidence intervals prior to NIGCG implementation (2009–2011) are all 0, whereas the coefficients after NIGCG implementation gradually deviate, and the confidence intervals do not include 0 from 2017 forward. These results satisfy the common trend assumption (although a long lag effect is apparent for the NIGCG affecting enterprises’ ESG); therefore, the PSM-DID approach is appropriate for our study. A possible reason for the relatively long lag in the policy effect may be that although NIGCG implementation encourages the provision of financing services for environmentally friendly enterprises, it takes a long time for environmentally friendly enterprises to upgrade production process after receiving funds to improve ESG level. Finally, we present Fig 3 based on the regression results to directly reflect the common trend test’s results.

This figure shows the common trends of ESG and ESG Score, respectively.

https://doi.org/10.1371/journal.pone.0304384.g003

https://doi.org/10.1371/journal.pone.0304384.t004

4.2 Benchmark regression

As reported in Table 5 , the coefficient of EFTime with ESG ( ESG Score ) is 0.1116 (0.6048) and significant at 5%, which means that the NIGCG can significantly boost environmentally friendly enterprises’ ESG (Score). This finding strongly supports Hypothesis 1. Moreover, we find that First , Sedtenth , and Cash Flow ( Lev ) are positively (negatively) related to environmentally friendly enterprises’ sustainable development capacity ( ESG and ESG Score ). The results are similar when we apply ESG and ESG Score as independent variables.

https://doi.org/10.1371/journal.pone.0304384.t005

4.3 Counterfactual test

https://doi.org/10.1371/journal.pone.0304384.t006

Furthermore, we also exclude environmentally friendly enterprises in the sample and randomly select half of the enterprises (334 enterprises) from others as the treatment group. Table 7 reveals that NIGCG implementation has an insignificant effect on enterprises’ ESG after reexamining the NIGCG’s influence with the new control group, indicating that this policy only affects the treatment group, which validates the robustness of the baseline empirical results.

https://doi.org/10.1371/journal.pone.0304384.t007

4.4 Additional tests

4.4.1 mediating effect analysis..

From columns (1)-(6) of Table 8 , we confirm that EFTime is positively related to ESG and R&D. Moreover, columns (3) and (6) of Table 8 indicate that R&D is positively related to ESG. A mediating effect account for 9.93% and 9.26%, respectively. This finding demonstrates that the NIGCG can affect sample enterprises’ ESG through R&D investment, Verifying the Porter hypothesis in China, which supports Hypothesis 2. Furthermore, Wu et al. (2023) discovered that enterprises concentrating on the digital economy are inclined to augment their R&D [ 45 ].

https://doi.org/10.1371/journal.pone.0304384.t008

4.4.2 Heterogeneity tests.

We employ two heterogeneity tests to examine whether NIGCG implementation has various effects on PC enterprises and those located in different regions. In this study, we reference Chen et al. (2011), Francis et al. (2009), and Wu et al. (2012) [ 46 – 48 ] and use the chairman or CEO as a current or former government official to quantify PC, employing Eq ( 2 ) for regression analysis. Table 9 reveals that EFTime has an insignificant influence on PC enterprises’ sustainable development capacity, while it has a significant effect on those without connections. In addition, the impacts of First and Sedtenth on politically connected enterprises’ sustainable development capacity are larger than those of non-PC enterprises. Board Size and Age have increased negative effects on non-PC enterprises, while the impacts on PC enterprises are insignificant.

https://doi.org/10.1371/journal.pone.0304384.t009

Furthermore, we separate our enterprises into three regional sub-samples, including eastern, middle, and western China and use Eq ( 2 ) for regression analysis. The empirical results reported in Table 10 reveals that NIGCG implementation has effectively improved enterprises’ ESG in the eastern region, while this promotional impact is not evident in the middle as well as western regions. First has a significant effect in all three regions, Sedtenth has a relatively large role in promoting ESG in the eastern and middle regions. Board Size only affects the western region, Lev only affects the middle region, and Cash Flow only affects the eastern region.

https://doi.org/10.1371/journal.pone.0304384.t010

5. Conclusions and recommendations

China’s government has recognized that the urgency of environmental protection while continuing to promote high economic growth to advance sustainable development. Under these circumstances, China issued the NIGCG on February 24, 2012, aiming to achieve sustainable social development goals by developing green credit by banking financial institutions and increasing support for the green, low-carbon, and circular economy. Considering the importance and comprehensiveness of this policy, this research constructs a quasi-natural experiment and adopts the PSM-DID model to empirically explore the impact of NIGCG on environmentally friendly enterprises’ ESG during 2009–2022.

The conclusions are as follow: The NIGCG significantly boosts environmentally friendly enterprises’ sustainable development capacity, which is measured by ESG and ESG Score, although a certain lag effect is noted. This finding holds after conducting several robustness tests. Additionally, the mediating effect analysis reveals that the NIGCG can affect enterprises’ ESG through R&D investment, verifying the Porter hypothesis in China. Furthermore, results of heterogeneity tests show that the role of NIGCG in improving enterprises’ ESG is significantly reflected in the non-PC enterprises and enterprises in eastern region.

Based on the empirical results, we suggest the authorities to offer some supporting policies, such as tax reimbursement and government subsidies, to enterprises, so as to stimulate their R&D investments and promote the NIGCG’s effect. Besides, differentiated policies should also be formulated according to the characteristics of enterprises and their regions, which could enhance the NIGCG’s promotional effect nationwide.

Supporting information

https://doi.org/10.1371/journal.pone.0304384.s001

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How to Write a Strong Hypothesis | Guide & Examples

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Variables in hypotheses

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Step 2: Do some preliminary research

Step 3: Formulate your hypothesis

Step 4: Refine your hypothesis

Step 5: Phrase your hypothesis in three ways

Step 6. Write a null hypothesis

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How does green credit guidelines affect environmentally friendly enterprises’ ESG? A quasi-natural experiment from China

1. Introduction

2. Literature review and research hypotheses

2.2 Research hypotheses

3. Data and methodology

3.2 Variables definition

3.2.2 Independent variables.

3.3 Descriptive statistics