what is the scientific method hypothesis

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Six Steps of the Scientific Method

Learn What Makes Each Stage Important

ThoughtCo. / Hugo Lin 

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

The scientific method is a systematic way of learning about the world around us. The key difference between the scientific method and other ways of acquiring knowledge is that, when using the scientific method, we make hypotheses and then test them with an experiment.

Anyone can use the scientific method to acquire knowledge by asking questions and then working to find the answers to those questions. Below are the six steps involved in the scientific method and variables you may encounter when working with this method.

The Six Steps

The number of steps in the scientific method can vary from one description to another (which mainly happens when data and analysis are separated into separate steps), however, below is a fairly standard list of the six steps you'll likely be expected to know for any science class:

• Purpose/Question Ask a question.
• Research Conduct background research. Write down your sources so you can cite your references. In the modern era, you might conduct much of your research online. As you read articles and papers online, ensure you scroll to the bottom of the text to check the author's references. Even if you can't access the full text of a published article, you can usually view the abstract to see the summary of other experiments . Interview experts on a topic. The more you know about a subject, the easier it'll be to conduct your investigation.
• Hypothesis Propose a hypothesis . This is a sort of educated guess about what you expect your research to reveal. A hypothesis is a statement used to predict the outcome of an experiment. Usually, a hypothesis is written in terms of cause and effect. Alternatively, it may describe the relationship between two phenomena. The null hypothesis or the no-difference hypothesis is one type of hypothesis that's easy to test because it assumes changing a variable will not affect the outcome. In reality, you probably expect a change, but rejecting a hypothesis may be more useful than accepting one.
• Experiment Design and experiment to test your hypothesis. An experiment has an independent and dependent variable. You change or control the independent variable and record the effect it has on the dependent variable . It's important to change only one variable for an experiment rather than try to combine the effects of variables in an experiment. For example, if you want to test the effects of light intensity and fertilizer concentration on the growth rate of a plant, you're looking at two separate experiments.
• Data/Analysis Record observations and analyze the meaning of the data. Often, you'll prepare a table or graph of the data. Don't throw out data points you think are bad or that don't support your predictions. Some of the most incredible discoveries in science were made because the data looked wrong! Once you have the data, you may need to perform a mathematical analysis to support or refute your hypothesis.
• Conclusion Conclude whether to accept or reject your hypothesis. There's no right or wrong outcome to an experiment, so either result is fine. Accepting a hypothesis doesn't necessarily mean it's correct! Sometimes repeating an experiment may give a different result. In other cases, a hypothesis may predict an outcome, yet you might draw an incorrect conclusion. Communicate your results. You can compile your results into a lab report or formally submit them as a paper . Whether you accept or reject the hypothesis, you likely learned something about the subject and may wish to revise the original hypothesis or form a new one for a future experiment.

When Are There Seven Steps?

Some teach the scientific method with seven steps instead of six. In the seven-step model, the first step is to make observations. Even if you don't make observations formally, you should think about prior experiences with a subject to ask a question or solve a problem.

Formal observations are a type of brainstorming that can help you find an idea and form a hypothesis. Observe your subject and record everything about it. Include colors, timing, sounds, temperatures, changes, behavior, and anything that strikes you as interesting or significant.

When you design an experiment, you're controlling and measuring variables. There are three types of variables:

• Controlled Variables:  You can have as many  controlled variables  as you like. These are parts of the experiment that you try to keep constant throughout an experiment so they won't interfere with your test. Writing down controlled variables is a good idea because it helps make your experiment  reproducible , which is important in science! If you have trouble duplicating results from one experiment to another, there may be a controlled variable you missed.
• Independent Variable:  This is the variable you control.
• Dependent Variable:  This is the variable you measure. It's called the dependent variable because it  depends  on the independent variable.
• Null Hypothesis Examples
• Scientific Method Flow Chart
• Random Error vs. Systematic Error
• What Is an Experimental Constant?
• Scientific Variable
• What Is a Hypothesis? (Science)
• What Are the Elements of a Good Hypothesis?
• What Are Examples of a Hypothesis?
• What Is a Testable Hypothesis?
• Scientific Hypothesis Examples
• Scientific Method Vocabulary Terms
• Understanding Simple vs Controlled Experiments
• The Role of a Controlled Variable in an Experiment
• What Is the Difference Between a Control Variable and Control Group?
• What Is a Controlled Experiment?
• DRY MIX Experiment Variables Acronym

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What Is the Scientific Method?

The scientific method is a systematic way of conducting experiments or studies so that you can explore the things you observe in the world and answer questions about them. The scientific method, also known as the hypothetico-deductive method, is a series of steps that can help you accurately describe the things you observe or improve your understanding of them.

Ultimately, your goal when you use the scientific method is to:

• Find a cause-and-effect relationship by asking a question about something you observed
• Collect as much evidence as you can about what you observed, as this can help you explore the connection between your evidence and what you observed
• Determine if all your evidence can be combined to answer your question in a way that makes sense

Francis Bacon and René Descartes are usually credited with formalizing the process in the 16th and 17th centuries. The two philosophers argued that research shouldn’t be guided by preset metaphysical ideas of how reality works. They supported the use of inductive reasoning to come up with hypotheses and understand new things about reality.

Scientific Method Steps

The scientific method is a step-by-step problem-solving process. These steps include:

Observe the world around you. This will help you come up with a topic you are interested in and want to learn more about. In many cases, you already have a topic in mind because you have a related question for which you couldn't find an immediate answer.

Either way, you'll start the process by finding out what people before you already know about the topic, as well as any questions that people are still asking about. You may need to look up and read books and articles from academic journals or talk to other people so that you understand as much as you possibly can about your topic. This will help you with your next step.

Ask questions. Asking questions about what you observed and learned from reading and talking to others can help you figure out what the "problem" is. Scientists try to ask questions that are both interesting and specific and can be answered with the help of a fairly easy experiment or series of experiments. Your question should have one part (called a variable) that you can change in your experiment and another variable that you can measure. Your goal is to design an experiment that is a "fair test," which is when all the conditions in the experiment are kept the same except for the one you change (called the experimental or independent variable).

Form a hypothesis and make predictions based on it.  A hypothesis is an educated guess about the relationship between two or more variables in your question. A good hypothesis lets you predict what will happen when you test it in an experiment. Another important feature of a good hypothesis is that, if the hypothesis is wrong, you should be able to show that it's wrong. This is called falsifiability. If your experiment shows that your prediction is true, then your hypothesis is supported by your data.

Test your prediction by doing an experiment or making more observations.  The way you test your prediction depends on what you are studying. The best support comes from an experiment, but in some cases, it's too hard or impossible to change the variables in an experiment. Sometimes, you may need to do descriptive research where you gather more observations instead of doing an experiment. You will carefully gather notes and measurements during your experiments or studies, and you can share them with other people interested in the same question as you. Ideally, you will also repeat your experiment a couple more times because it's possible to get a result by chance, but it's less possible to get the same result more than once by chance.

Draw a conclusion. You will analyze what you already know about your topic from your literature research and the data gathered during your experiment. This will help you decide if the conclusion you draw from your data supports or contradicts your hypothesis. If your results contradict your hypothesis, you can use this observation to form a new hypothesis and make a new prediction. This is why scientific research is ongoing and scientific knowledge is changing all the time. It's very common for scientists to get results that don't support their hypotheses. In fact, you sometimes learn more about the world when your experiments don't support your hypotheses because it leads you to ask more questions. And this time around, you already know that one possible explanation is likely wrong.

Use your results to guide your next steps (iterate). For instance, if your hypothesis is supported, you may do more experiments to confirm it. Or you could come up with a hypothesis about why it works this way and design an experiment to test that. If your hypothesis is not supported, you can come up with another hypothesis and do experiments to test it. You'll rarely get the right hypothesis in one go. Most of the time, you'll have to go back to the hypothesis stage and try again. Every attempt offers you important information that helps you improve your next round of questions, hypotheses, and predictions.

Share your results. Scientific research isn't something you can do on your own; you must work with other people to do it.   You may be able to do an experiment or a series of experiments on your own, but you can't come up with all the ideas or do all the experiments by yourself .

Scientists and researchers usually share information by publishing it in a scientific journal or by presenting it to their colleagues during meetings and scientific conferences. These journals are read and the conferences are attended by other researchers who are interested in the same questions. If there's anything wrong with your hypothesis, prediction, experiment design, or conclusion, other researchers will likely find it and point it out to you.

It can be scary, but it's a critical part of doing scientific research. You must let your research be examined by other researchers who are as interested and knowledgeable about your question as you. This process helps other researchers by pointing out hypotheses that have been proved wrong and why they are wrong. It helps you by identifying flaws in your thinking or experiment design. And if you don't share what you've learned and let other people ask questions about it, it's not helpful to your or anyone else's understanding of what happens in the world.

Scientific Method Example

Here's an everyday example of how you can apply the scientific method to understand more about your world so you can solve your problems in a helpful way.

Let's say you put slices of bread in your toaster and press the button, but nothing happens. Your toaster isn't working, but you can't afford to buy a new one right now. You might be able to rescue it from the trash can if you can figure out what's wrong with it. So, let's figure out what's wrong with your toaster.

Observation. Your toaster isn't working to toast your bread.

Ask a question. In this case, you're asking, "Why isn't my toaster working?" You could even do a bit of preliminary research by looking in the owner's manual for your toaster. The manufacturer has likely tested your toaster model under many conditions, and they may have some ideas for where to start with your hypothesis.

Form a hypothesis and make predictions based on it. Your hypothesis should be a potential explanation or answer to the question that you can test to see if it's correct. One possible explanation that we could test is that the power outlet is broken. Our prediction is that if the outlet is broken, then plugging it into a different outlet should make the toaster work again.

Test your prediction by doing an experiment or making more observations. You plug the toaster into a different outlet and try to toast your bread.

If that works, then your hypothesis is supported by your experimental data. Results that support your hypothesis don't prove it right; they simply suggest that it's a likely explanation. This uncertainty arises because, in the real world, we can't rule out the possibility of mistakes, wrong assumptions, or weird coincidences affecting the results. If the toaster doesn’t work even after plugging it into a different outlet, then your hypothesis is not supported and it's likely the wrong explanation.

Use your results to guide your next steps (iteration). If your toaster worked, you may decide to do further tests to confirm it or revise it. For example, you could plug something else that you know is working into the first outlet to see if that stops working too. That would be further confirmation that your hypothesis is correct.

If your toaster failed to toast when plugged into the second outlet, you need a new hypothesis. For example, your next hypothesis might be that the toaster has a shorted wire. You could test this hypothesis directly if you have the right equipment and training, or you could take it to a repair shop where they could test that hypothesis for you.

Share your results. For this everyday example, you probably wouldn't want to write a paper, but you could share your problem-solving efforts with your housemates or anyone you hire to repair your outlet or help you test if the toaster has a short circuit.

What the Scientific Method Is Used For

The scientific method is useful whenever you need to reason logically about your questions and gather evidence to support your problem-solving efforts. So, you can use it in everyday life to answer many of your questions; however, when most people think of the scientific method, they likely think of using it to:

Describe how nature works . It can be hard to accurately describe how nature works because it's almost impossible to account for every variable that's involved in a natural process. Researchers may not even know about many of the variables that are involved. In some cases, all you can do is make assumptions. But you can use the scientific method to logically disprove wrong assumptions by identifying flaws in the reasoning.

Do scientific research in a laboratory to develop things such as new medicines.

Develop critical thinking skills.  Using the scientific method may help you develop critical thinking in your daily life because you learn to systematically ask questions and gather evidence to find answers. Without logical reasoning, you might be more likely to have a distorted perspective or bias. Bias is the inclination we all have to favor one perspective (usually our own) over another.

The scientific method doesn't perfectly solve the problem of bias, but it does make it harder for an entire field to be biased in the same direction. That's because it's unlikely that all the people working in a field have the same biases. It also helps make the biases of individuals more obvious because if you repeatedly misinterpret information in the same way in multiple experiments or over a period, the other people working on the same question will notice. If you don't correct your bias when others point it out to you, you'll lose your credibility. Other people might then stop believing what you have to say.

Why Is the Scientific Method Important?

When you use the scientific method, your goal is to do research in a fair, unbiased, and repeatable way. The scientific method helps meet these goals because:

It's a systematic approach to problem-solving. It can help you figure out where you're going wrong in your thinking and research if you're not getting helpful answers to your questions. Helpful answers solve problems and keep you moving forward. So, a systematic approach helps you improve your problem-solving abilities if you get stuck.

It can help you solve your problems.  The scientific method helps you isolate problems by focusing on what's important. In addition, it can help you make your solutions better every time you go through the process.

It helps you eliminate (or become aware of) your personal biases.  It can help you limit the influence of your own personal, preconceived notions . A big part of the process is considering what other people already know and think about your question. It also involves sharing what you've learned and letting other people ask about your methods and conclusions. At the end of the process, even if you still think your answer is best, you have considered what other people know and think about the question.

The scientific method is a systematic way of conducting experiments or studies so that you can explore the world around you and answer questions using reason and evidence. It's a step-by-step problem-solving process that involves: (1) observation, (2) asking questions, (3) forming hypotheses and making predictions, (4) testing your hypotheses through experiments or more observations, (5) using what you learned through experiment or observation to guide further investigation, and (6) sharing your results.

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Chapter 3. Psychological Science

The Basic Process of Scientific Research

Amelia Liangzi Shi

Approximate reading time : 28 minutes

Learning Objectives

By the end of this section, you will be able to:

• Describe the principles of the scientific method
• Differentiate hypotheses from predictions
• Describe why operational definitions are important
• Understand why we need peer review before publishing our research findings

Psychologists are not the only people who seek to understand human behaviour and solve social problems. Philosophers, religious leaders, and politicians, among others, also strive to provide explanations for human behaviour. However, psychologists believe that research is the best tool for understanding human beings and their relationships with others. Rather than accepting the claim that people do or do not have free will, a psychologist would collect data to empirically test whether or not people are able to actively control their own behaviour. Rather than accepting an argument that creating or abandoning a new centre for mental health will improve the lives of individuals in the inner city, a psychologist would empirically assess the effects of receiving mental health treatment on quality of life. The statements made by psychologists are empirical , which means they are based on systematic collection and analysis of data.

The Scientific Method

The scientific method can be simplified into a series of steps. Basically, ideas (in the form of theories and hypotheses) are tested against the real world (in the form of empirical observations). Then, those empirical observations lead to more ideas that are tested against the real world, and so on. In this sense, the scientific process is circular. As psychologists learn more about something, that knowledge generates further questions that can be turned into hypotheses. As our knowledge is expanded, we may have to change a theory to account for it.

A theory is a well-developed set of ideas that proposes an explanation for observed phenomena. Theories are repeatedly checked against the world, but they tend to be too complex to be tested all at once; instead, researchers create hypotheses to test specific aspects of a theory. A hypothesis is a testable prediction about how the world will behave, and it is often worded as an if-then statement (e.g., if I study all night, then I will get a passing grade on the test). The hypothesis is extremely important as it bridges the gap between the realm of ideas and the real world. Psychological researchers may form their hypotheses based on both deductive and inductive processes. In the scientific context, deduction refers to testing theories against empirical observations. Imagine you want to deductively test the general idea that “playing video games will make people happy.” You observe your friend Bob, who plays video games every day. You want to see whether he always seems happy during these gaming sessions. In this case, you are applying a general theory specifically to Bob. Induction , on the other hand, refers to using empirical observations to formulate theories. Let’s say several of your friends all mention feeling happy while playing video games. From these specific observations, you may inductively propose a hypothesis like, “People tend to feel happy when playing video games.” In this case, you are forming a theory based on the specific instances you’ve encountered.

In addition to requiring that science be empirical, the scientific method demands that the procedures used be objective , that is to say, free from the personal bias or emotions of the researcher. The scientific method prescribes the process for researchers to collect and analyze data, draw conclusions, and share their findings. By following these rules, other researchers can understand exactly how the data was collected and analyzed. They can draw their own conclusions and not rely solely on the interpretations of the original research. This promotes transparency and allows for diverse perspectives in scientific research. Some new research also aims to replicate previous findings. They can repeat, add to, modify or even falsify earlier findings. In this way, scientific knowledge grows as researchers report their findings, and other researchers add to or modify the procedures through continuous research and sharing of ideas.

The goal of research in psychology is to explain and predict relationships within a certain area of study. A good theory should offer a simple explanation of a phenomenon (that is, be parsimonious) and suggest ideas for future research. Most importantly, a good theory must be falsifiable , which means that the hypotheses generated from this theory are capable of being shown to be incorrect. Recall from the introductory chapter that Sigmund Freud had lots of interesting ideas to explain human behaviours. However, a major criticism of Freud’s work is that many of his ideas were not falsifiable; for example, it is impossible to imagine how we could measure the three elements of personality described in Freud’s theories — the id, the ego, and the superego. How could we verify or falsify their existence?

A good hypothesis is a testable prediction about relationships between clearly defined variables. Suppose that we want to test this idea: sleep is important for memory. Can you think of three different testable hypotheses? For example:

People who get more than seven hours of sleep will get a higher score on the ABC Memory Test than people who get less than seven hours.

There is a positive association between the amount of time students sleep and their grade point average.

People suffering from insomnia show an increased ABC Memory Test score when they are successfully treated for insomnia.

Operational Definitions

As you read through these examples, you may have noticed that we use very specific methods to measure sleep and memory. Psychologists use the term “ operational definition” to describe exactly how a variable is being measured. In contrast to the abstract concepts, the operational definitions are very specific. For example, we could measure sleep in many different ways: self-reported number of hours of sleep according to brain waves measured in a sleep lab, the number of hours of sleep reported by a fitness tracking device worn by participants, and so on. If we were measuring temperature, we would need to define what we mean by temperature: degrees Fahrenheit, degrees Celsius, or simply our best guess. Having clear operational definitions is crucial because. If a variable is not precisely defined, others may misunderstand the data collected, making it hard for future researchers to replicate the study.

Here are some operational definitions (OD) of variables that have been used in psychological research:

• OD1: number of presses of a button that administers shock to another student
• OD2: number of seconds taken to honk the horn at the car ahead after a stoplight turns green
• OD1: number of inches that an individual places his or her chair away from another person
• OD2: number of millimeters of pupil dilation when one person looks at another
• OD1: number of days per month an employee shows up to work on time
• OD2: rating of job satisfaction from 1 (not at all satisfied) to 10 (extremely satisfied)
• OD1 = number of negative words used in a creative story
• OD2 = number of appointments made with a psychotherapist

Peer Review

When psychologists complete a research project, they generally want to share their findings with other scientists. The American Psychological Association (APA, 2020) publishes a manual detailing how to write a paper for submission to peer-reviewed, scientific journals. The Online Writing Lab (OWL) at Purdue University can walk you through the APA writing guidelines.

Peer review is an important part of publishing research findings in many scientific disciplines. A peer-reviewed journal article is read by several other scientists (generally anonymously) with expertise in the subject matter. These peer reviewers provide feedback to both the author and the journal editor regarding the quality of the draft. Peer reviewers look for a strong rationale for the research being described, a clear description of how the research was conducted, and evidence that the research was conducted in an ethical manner. They also look for flaws in the study’s design, methods, and statistical analyses. They check that the conclusions drawn by the authors seem reasonable given the observations made during the research. Peer reviewers also comment on how valuable the research is in advancing the discipline’s knowledge. This helps prevent unnecessary duplication of research findings in the scientific literature and, to some extent, ensures that each research article provides new information. Ultimately, the journal editor will compile all of the peer reviewer feedback and determine whether the article will be published in its current state (a rare occurrence), published with revisions, or not accepted for publication.

Peer review provides some degree of quality control for psychological research. Poorly conceived or executed studies can be weeded out, well-designed research can be improved, and ideally, studies can be described clearly enough to allow other scientists to replicate them, which helps to maintain reliability.

So why would we want to replicate a study? Imagine that our version of the Bobo doll study is done exactly the same as the original, only using a different set of participants and researchers. We use the same operational definitions, manipulations, measurements, and procedures, and our groups are equivalent in terms of their baseline levels of aggression. In our replication however, we receive completely different results and the children do not imitate aggressive behaviours any more than they would at the level of chance. If our experimental manipulation is exactly the same, then the difference in results must be attributable to something else that is different between our study and the original, which might include the researchers, participants, and location. If on the other hand, we were able to replicate the results of the original experiment using different researchers and participants at a different location, then this would provide support for the idea that the results were due to the manipulation and not to any of these other variables. The more we can replicate a result with different samples, the more reliable it is.

In recent years, there has been increasing concern about a “replication crisis” that has affected a number of scientific fields, including psychology. One study found that only about 62% of social science studies reviewed were replicable, and even then their effect sizes were reduced by half (Cramerer et al, 2018). In fact, even a famous Nobel Prize-winning scientist has recently retracted a published paper because she had difficulty replicating her results (BBC, 2020). These kinds of outcomes have prompted some scientists to begin to work together and more openly. One example of this more collaborative approach is the Psychological Science Accelerator , a network of over 500 laboratories, representing 82 countries. This network allows researchers to pre-register their study designs, which minimises any cherry-picking that might happen along the way to boost results. Cherry-picking is a biased approach where researchers selectively report data that supports a researcher’s hypothesis, while ignoring any findings that do not support it. The network also facilitates data collection across multiple labs, allowing for the use of large, diverse samples and more wide-spread sharing of results. Hopefully with a more collaborative approach, we can develop a better process for replicating and checking the quality of research. If you’d like to learn more about the Psychological Sciences Accelerator , you can check out Psychological Science Accelerator’s website .

Watch this video: The Scientific Method (15 minutes)

“The Scientific Method: Crash Course Biology #2” video by CrashCourse is licensed under the Standard YouTube licence.

Image attributions

Figure PS.1. Scientific Method 3 by Whatiguana is used under a CC BY-SA 4.0 license.

Figure PS.2. Original image created for this text and has a CC BY-NC-SA license .

Image Descriptions

Figure PS.1. The Scientific Method as an Ongoing Process image description:

• Make Observations: What do I see in nature? This can be from one’s own experiences, thoughts, or reading.
• Think of Interesting Questions: What does that pattern occur?
• Formulate Hypotheses: What are the general causes of the phenomenon I am wondering about?
• Develop Testable Predictions: If my hypothesis is correct, then I expect a, b, c, …
• Gather Data to Test Predictions: Relevant data can come from the literature, new observations or formal experiments. Thorough testing requires replication to verify results. (Go to Step 6 or Step 7)
• Refine, Alter, Expand or Reject Hypotheses . (Go to Step 4)
• Develop General Theories: General Theories must be consistent with most or all available data and with other current theories. [Return to Figure PS.1]

To calculate this time, we used a reading speed of 150 words per minute and then added extra time to account for images and videos. This is just to give you a rough idea of the length of the chapter section. How long it will take you to engage with this chapter will vary greatly depending on all sorts of things (the complexity of the content, your ability to focus, etc).

The Basic Process of Scientific Research Copyright © 2024 by Amelia Liangzi Shi is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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The Scientific Method

What is the scientific method, research starters, observation, analyze results, draw conclusions.

• Scientific Method Resources

According to Kosso (2011), the scientific method is a specific step-by-step method that aims to answer a question or prove a hypothesis.  It is the process used among all scientific disciplines and is used to conduct both small and large experiments.  It has been used for centuries to solve scientific problems and identify solutions.  While the terminology can be different across disciplines, the scientific method follows these six steps (Larson, 2015):

• Analyze results
• Draw conclusions

Click on each link to learn more about each step in the scientific method, or watch the video below for an introduction to each step.

Research Starters  is a feature available when searching  DragonQuest . You may notice when you enter a generic search term into DragonQuest that a research starter is your first result.

Research Starter  entries are similar to a Wikipedia entry of the topic, but  Research Starters  are pulled from quality sources such as Salem Press, Encyclopedia Britannica, and American National Biography.  Research Starters  can be a great place to begin your research, if you're not yet sure about your topic details.  There are several Research Starters related to the steps of the scientific method:

• Scientific method
• Research methodology
• Research methods

Using Research Starters

To use  Research Starters,  click on the title just as you would for any other  DragonQuest  entry. You will then find a broad overview of the topic. This entry is great for finding

• Subtopics that can narrow your searching
• Background information to support your claims
• Sources you can use and cite in your research

We do not recommend that you use  Research Starters  as a source itself though, because of the difficulties in citation.

Citing Research Starters

Using  Research Starters  as an actual source is not recommended.

Just as we do not recommend using Wikipedia as a source,  Research Starters  is the same. Use  Research Starters  as a starting point to get ideas about how to narrow your search and to use its bibliography to find sources you can cite.

We recommend this because citing  Research Starters  can be tricky as sometimes it will have insufficient bibliographic data to create your reference page.

To begin the scientific method, you have to observe something and identify a problem.  You can observe basically anything, such as a person, place, object, situation, or environment.  Examples of an observation include:

• "My cotton shirt gets more wet in the rain than my friend's silk shirt."
• "I feel more tired after eating a cookie than I do after eating a salad."

Once you have made an observation, it will lead to creating a scientific question (Larson, 2015).  The question focuses on a specific part of your observation:

• Why does a cotton shirt get more wet in the rain than a silk shirt?
• Why do I more tired after eating a cookie than if I ate a salad?

Scientific questions lead to research and crafting a hypothesis, which are the next steps in the scientific method.  Watch the video below for more information on observations.

Once you identify a topic and question from your observations, it is time to conduct some preliminary research.  It is meant to locate a potential answer to your research question or give you ideas on how to draft your hypothesis.  In some cases, it can also help you design an experiment once you determine your hypothesis.  It is a good idea to research your topic or problem using the library and/or the Internet.  It is also recommended to check out different source types for information, such as:

• Academic journals
• News reports
• Audiovisual media (radio, podcasts, etc.)

Background Information

It is important to gather lots of background information on your topic or problem so you understand the topic thoroughly.  It is also critical to find and understand what others have already written about your research question.  This prevents you from experimenting on an issue that already has a definitive answer.

If you need assistance in conducting preliminary research, view our guide on locating background information at the bottom of this box.

If you are unsure where you should start researching, you can view our list of science databases through our  A-Z database list  by selecting "Science" from the subjects dropdown menu.  We also have several research guides that cover topics in the sciences, which can be viewed on our Help page.

Not sure where to begin your research?  Try searching a database in our A-Z list or using one of our  EBSCOhost databases !

• Finding Background Information by Pfeiffer Library Last Updated Jul 10, 2024 78 views this year

When you have gathered enough information on your research question and determined that your question has not already been answered, you can form a hypothesis.  A hypothesis is an educated guess or possible explanation meant to answer your research question.  It often follows the "if, then..." sentence structure because it explains a cause/effect relationship between two variables.  A hypothesis is supposed to form a relationship between the two variables.

• Example hypothesis: "If I soak a penny in lemon juice, then it will look cleaner than if I soak it in soap."

In this example, it is explaining a relationship between a penny and different cleaning agents.  While crafting your hypothesis, it is important to make sure that your "then" statement is something that can be measured, either quantitatively or qualitatively.  In the above example, an experiment for the hypothesis would be measuring the cleanliness of the penny after being exposed to either soap or lemon juice.

For more information on hypotheses, view DragonQuest's Research Starter on hypotheses here .  Alternatively, you can watch the video below for more details on crafting hypotheses.

The fourth step in the scientific method is the experiment stage.  This is where you craft an experiment to test your hypothesis.  The point of an experiment is to find out how changing one thing impacts another (Larson, 2015).  To test a hypothesis, you must implement and change different variables in your experiment.

Anything that you modify in an experiment is considered a variable.  There are two types of variables:

• Independent variable:  The variable that is modified in an experiment so that is has a direct impact on the dependent variable.  It is the variable that you control in the experiment (Larson, 2015).
• Dependent variable:  The variable that is being tested in an experiment, whose measure is directly related to the change of the independent variable (the dependent variable is dependent on the independent variable).  This is what you measure to prove or disprove your hypothesis.

Every experiment must also have a control group , which is a variable that remains unchanged for the duration of the experiment (Larson, 2015).  It is used to compare the results of the dependent variable.  In the case of the sample hypothesis above, a control variable would be a penny that does not receive any cleaning agent.

Research Methods

There are several ways to conduct an experiment.  The approach you take is dependent on your own strengths and weaknesses, the nature of your topic/hypothesis, and the resources you have available to conduct the experiment.  If you are unsure as to what research method you would like to use for your experiment, you can view our research methodologies guide below.  DragonQuest also has a Research Starter on research methods, located  here .

• Research Methodologies by Pfeiffer Library Last Updated Aug 2, 2022 47289 views this year

When designing your experiment:

• Make a list of materials that you will need to conduct your experiment.  If you will need to purchase additional materials, create a budget.
• Consider the best locations for your experiment, especially if outside factors (weather, etc.) may effect the results.
• If you need additional funding for an experiment, it is recommended to consider writing a research proposal for the entity from which you want to receive funding.  You can view our guide on writing research proposals below.

You can also watch the video below to learn more about designing experiments.  Or, you can view DragonQuest's Research Starter on experiments here .

• Writing a Research Proposal by Pfeiffer Library Last Updated May 22, 2023 23606 views this year

When conducting your experiment:

• Record or write down your experimental procedure so that each variable it tested equally.  It is likely that you will conduct your experiment more than once, so it is important that it is conducted exactly the same each time (Larson, 2015).
• Be aware of outside factors that could impact your experiment and results.  Outside factors could include weather patterns, time of day, location, and temperature.
• Wear protective equipment to keep yourself safe during the experiment.
• Record your results on a transferrable platform (Google Spreadsheets, Microsoft Excel, etc.), especially if you plan on running statistical analyses on your data using a computer program.  You should also back your data up electronically so you do not lose it!
• Use a table or chart to record data by hand.  The x-axis (row) of a chart should represent the independent variable, while the y-axis (column) should represent the dependent variable (Riverside Local Schools, n.d.).
• Be prepared for unexpected results.  Some experiments can unexpectedly "go wrong" resulting in different data than planned.  Do not feel defeated if this happens in your experiment!  Once the tests are completed, you can analyze and determine why the experiment went differently.

Before arriving at a conclusion, you must look at all your evidence and analyze it.  Data analysis is "the process of interpreting the meaning of the data we have collected, organized, and displayed in the form of a chart or graph" (Riverside Local Schools, p. 1.).  If you did not create a graph or chart while recording your data, you may choose to create one to analyze your results.  Or, you may choose to create a more elaborate chart from the one you used in the experiment.  Graphs and charts organize data so that you can easily identify trends or patterns.  Patterns are similarities, differences, and relationships that tell you the "big picture" of an experiment (Riverside Local Schools, n.d.).

Questions to Consider

There are several things to consider when analyzing your data:

• What exactly am I trying to discover from this data?
• How does my data relate to my hypothesis?
• Are there any noticeable patterns or trends in the data?  If so, what do these patterns mean?
• Is my data good quality?  Was my data skewed in any way?
• Were there any limitations to retrieving this data during the experiment?

Once you have identified patterns or trends and considered the above questions, you can summarize your findings to draw your final conclusions.

Drawing conclusions is the final step in the scientific method.  It gives you the opportunity to combine your findings and communicate them to your audience.  A conclusion is "a summary of what you have learned from the experiment" (Riverside Local Schools, p. 1).  To draw a conclusion, you will compare your data analysis to your hypothesis and make a statement based on the comparison.  Your conclusion should answer the following questions:

• Was your hypothesis correct?
• Does my data support my hypothesis?
• If your hypothesis was incorrect, what did you learn from the experiment?
• Do you need to change a variable if the experiment is repeated?
• Is your data coherent and easy to understand?
• If the experiment failed, what did you learn?

A strong conclusion should also (American Psychological Association, 2021):

• Be justifiable by the data you collected.
• Provide generalizations that are limited to the sample you studied.
• Relate your preliminary research (background information) to your experiment and state how your conclusion is relevant.
• Be logical and address any potential discrepancies (American Psychological Association, 2021).

Reporting Your Results

Once you have drawn your conclusions, you will communicate your results to others.  This can be in the form of a formal research paper, presentation, or assignment that you submit to an instructor for a grade.  If you are looking to submit an original work to an academic journal, it will require approval and undergo peer-review before being published.  However, it is important to be aware of predatory publishers.  You can view our guide on predatory publishing below.

• Predatory Publishing by Pfeiffer Library Last Updated Aug 2, 2023 640 views this year
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Scientific Method Steps in Psychology Research

Steps, Uses, and Key Terms

Verywell / Theresa Chiechi

How do researchers investigate psychological phenomena? They utilize a process known as the scientific method to study different aspects of how people think and behave.

When conducting research, the scientific method steps to follow are:

• Observe what you want to investigate
• Ask a research question and make predictions
• Test the hypothesis and collect data
• Examine the results and draw conclusions
• Report and share the results 

This process not only allows scientists to investigate and understand different psychological phenomena but also provides researchers and others a way to share and discuss the results of their studies.

Generally, there are five main steps in the scientific method, although some may break down this process into six or seven steps. An additional step in the process can also include developing new research questions based on your findings.

What Is the Scientific Method?

What is the scientific method and how is it used in psychology?

The scientific method consists of five steps. It is essentially a step-by-step process that researchers can follow to determine if there is some type of relationship between two or more variables.

By knowing the steps of the scientific method, you can better understand the process researchers go through to arrive at conclusions about human behavior.

Scientific Method Steps

While research studies can vary, these are the basic steps that psychologists and scientists use when investigating human behavior.

The following are the scientific method steps:

Step 1. Make an Observation

Before a researcher can begin, they must choose a topic to study. Once an area of interest has been chosen, the researchers must then conduct a thorough review of the existing literature on the subject. This review will provide valuable information about what has already been learned about the topic and what questions remain to be answered.

A literature review might involve looking at a considerable amount of written material from both books and academic journals dating back decades.

The relevant information collected by the researcher will be presented in the introduction section of the final published study results. This background material will also help the researcher with the first major step in conducting a psychology study: formulating a hypothesis.

Step 2. Ask a Question

Once a researcher has observed something and gained some background information on the topic, the next step is to ask a question. The researcher will form a hypothesis, which is an educated guess about the relationship between two or more variables

For example, a researcher might ask a question about the relationship between sleep and academic performance: Do students who get more sleep perform better on tests at school?

In order to formulate a good hypothesis, it is important to think about different questions you might have about a particular topic.

You should also consider how you could investigate the causes. Falsifiability is an important part of any valid hypothesis. In other words, if a hypothesis was false, there needs to be a way for scientists to demonstrate that it is false.

Step 3. Test Your Hypothesis and Collect Data

Once you have a solid hypothesis, the next step of the scientific method is to put this hunch to the test by collecting data. The exact methods used to investigate a hypothesis depend on exactly what is being studied. There are two basic forms of research that a psychologist might utilize: descriptive research or experimental research.

Descriptive research is typically used when it would be difficult or even impossible to manipulate the variables in question. Examples of descriptive research include case studies, naturalistic observation , and correlation studies. Phone surveys that are often used by marketers are one example of descriptive research.

Correlational studies are quite common in psychology research. While they do not allow researchers to determine cause-and-effect, they do make it possible to spot relationships between different variables and to measure the strength of those relationships. 

Experimental research is used to explore cause-and-effect relationships between two or more variables. This type of research involves systematically manipulating an independent variable and then measuring the effect that it has on a defined dependent variable .

One of the major advantages of this method is that it allows researchers to actually determine if changes in one variable actually cause changes in another.

While psychology experiments are often quite complex, a simple experiment is fairly basic but does allow researchers to determine cause-and-effect relationships between variables. Most simple experiments use a control group (those who do not receive the treatment) and an experimental group (those who do receive the treatment).

Step 4. Examine the Results and Draw Conclusions

Once a researcher has designed the study and collected the data, it is time to examine this information and draw conclusions about what has been found.  Using statistics , researchers can summarize the data, analyze the results, and draw conclusions based on this evidence.

So how does a researcher decide what the results of a study mean? Not only can statistical analysis support (or refute) the researcher’s hypothesis; it can also be used to determine if the findings are statistically significant.

When results are said to be statistically significant, it means that it is unlikely that these results are due to chance.

Based on these observations, researchers must then determine what the results mean. In some cases, an experiment will support a hypothesis, but in other cases, it will fail to support the hypothesis.

So what happens if the results of a psychology experiment do not support the researcher's hypothesis? Does this mean that the study was worthless?

Just because the findings fail to support the hypothesis does not mean that the research is not useful or informative. In fact, such research plays an important role in helping scientists develop new questions and hypotheses to explore in the future.

After conclusions have been drawn, the next step is to share the results with the rest of the scientific community. This is an important part of the process because it contributes to the overall knowledge base and can help other scientists find new research avenues to explore.

Step 5. Report the Results

The final step in a psychology study is to report the findings. This is often done by writing up a description of the study and publishing the article in an academic or professional journal. The results of psychological studies can be seen in peer-reviewed journals such as  Psychological Bulletin , the  Journal of Social Psychology ,  Developmental Psychology , and many others.

The structure of a journal article follows a specified format that has been outlined by the  American Psychological Association (APA) . In these articles, researchers:

• Provide a brief history and background on previous research
• Present their hypothesis
• Identify who participated in the study and how they were selected
• Provide operational definitions for each variable
• Describe the measures and procedures that were used to collect data
• Explain how the information collected was analyzed
• Discuss what the results mean

Why is such a detailed record of a psychological study so important? By clearly explaining the steps and procedures used throughout the study, other researchers can then replicate the results. The editorial process employed by academic and professional journals ensures that each article that is submitted undergoes a thorough peer review, which helps ensure that the study is scientifically sound.

Once published, the study becomes another piece of the existing puzzle of our knowledge base on that topic.

Before you begin exploring the scientific method steps, here's a review of some key terms and definitions that you should be familiar with:

• Falsifiable : The variables can be measured so that if a hypothesis is false, it can be proven false
• Hypothesis : An educated guess about the possible relationship between two or more variables
• Variable : A factor or element that can change in observable and measurable ways
• Operational definition : A full description of exactly how variables are defined, how they will be manipulated, and how they will be measured

Uses for the Scientific Method

The  goals of psychological studies  are to describe, explain, predict and perhaps influence mental processes or behaviors. In order to do this, psychologists utilize the scientific method to conduct psychological research. The scientific method is a set of principles and procedures that are used by researchers to develop questions, collect data, and reach conclusions.

Goals of Scientific Research in Psychology

Researchers seek not only to describe behaviors and explain why these behaviors occur; they also strive to create research that can be used to predict and even change human behavior.

Psychologists and other social scientists regularly propose explanations for human behavior. On a more informal level, people make judgments about the intentions, motivations , and actions of others on a daily basis.

While the everyday judgments we make about human behavior are subjective and anecdotal, researchers use the scientific method to study psychology in an objective and systematic way. The results of these studies are often reported in popular media, which leads many to wonder just how or why researchers arrived at the conclusions they did.

Examples of the Scientific Method

Now that you're familiar with the scientific method steps, it's useful to see how each step could work with a real-life example.

Say, for instance, that researchers set out to discover what the relationship is between psychotherapy and anxiety .

• Step 1. Make an observation : The researchers choose to focus their study on adults ages 25 to 40 with generalized anxiety disorder.
• Step 2. Ask a question : The question they want to answer in their study is: Do weekly psychotherapy sessions reduce symptoms in adults ages 25 to 40 with generalized anxiety disorder?
• Step 3. Test your hypothesis : Researchers collect data on participants' anxiety symptoms . They work with therapists to create a consistent program that all participants undergo. Group 1 may attend therapy once per week, whereas group 2 does not attend therapy.
• Step 4. Examine the results : Participants record their symptoms and any changes over a period of three months. After this period, people in group 1 report significant improvements in their anxiety symptoms, whereas those in group 2 report no significant changes.
• Step 5. Report the results : Researchers write a report that includes their hypothesis, information on participants, variables, procedure, and conclusions drawn from the study. In this case, they say that "Weekly therapy sessions are shown to reduce anxiety symptoms in adults ages 25 to 40."

Of course, there are many details that go into planning and executing a study such as this. But this general outline gives you an idea of how an idea is formulated and tested, and how researchers arrive at results using the scientific method.

Erol A. How to conduct scientific research ? Noro Psikiyatr Ars . 2017;54(2):97-98. doi:10.5152/npa.2017.0120102

University of Minnesota. Psychologists use the scientific method to guide their research .

Shaughnessy, JJ, Zechmeister, EB, & Zechmeister, JS. Research Methods In Psychology . New York: McGraw Hill Education; 2015.

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

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The Scientific Method: A Need for Something Better?

Here is the last part of the triptych that started with the “Perspectives” on brainstorming that was followed by the one on verbal overshadowing. I have decided to keep this for last because it deals with and in many ways attempts to debunk the use of the scientific method as the Holy Grail of research. Needless to say, the topic is controversial and will anger some.

In the “natural sciences,” advances occur through research that employs the scientific method. Just imagine trying to publish an original investigation or getting funds for a project without using it! Although research in the pure (fundamental) sciences (eg, biology, physics, and chemistry) must adhere to it, investigations pertaining to soft (a pejorative term) sciences (eg, sociology, economics, and anthropology) do not use it and yet produce valid ideas important enough to be published in peer-reviewed journals and even win Nobel Prizes.

The scientific method is better thought of as a set of “methods” or different techniques used to prove or disprove 1 or more hypotheses. A hypothesis is a proposed explanation for observed phenomena. These phenomena are, in general, empirical—that is, they are gathered by observation and/or experimentation. “Hypothesis” is a term often confused with “theory.” A theory is the end result of a previously tested hypothesis, meaning a proved set of principles that explain observed phenomena. Thus, a hypothesis is sometimes called a “working hypothesis,” to avoid this confusion. A working hypothesis needs to be proved or disproved by investigation. The entire approach employed to validate a hypothesis is more broadly called the “hypothetico-deductivism” method. Not all hypotheses are proved by empirical testing, and most of what we know and accept as truth about the economy and ancient civilizations is solely based on … just observation and thoughts. Conversely, the deep thinkers in the non-natural disciplines see many things wrong with the scientific method because it does not entirely reflect the chaotic environment that we live in—that is, the scientific method is rigid and constrained in its design and produces results that are isolated from real environments and that only address specific issues.

One of the most important features of the scientific method is its repeatability. The experiments performed to prove a working hypothesis must clearly record all details so that others may replicate them and eventually allow the hypothesis to become widely accepted. Objectivity must be used in experiments to reduce bias. “Bias” refers to the inclination to favor one perspective over others. The opposite of bias is “neutrality,” and all experiments (and their peer review) need to be devoid of bias and be neutral. In medicine, bias is also a part of conflict of interest and produces corrupt results. In medicine, conflict of interest is often due to relationships with the pharmaceutical/device industries. The American Journal of Neuroradiology ( AJNR ), as do most other serious journals, requires that contributors fill out the standard disclosure form regarding conflict of interest proposed by the International Committee of Medical Journal Editors, and it publishes these at the end of articles. 1

Like many other scientific advances, the scientific method originated in the Muslim world. About 1000 years ago, the Iraqi mathematician Ibn al-Haytham was already using it. In the Western world, the scientific method was first welcomed by astronomers such as Galileo and Kepler, and after the 17th century, its use became widespread. As we now know it, the scientific method dates only from the 1930s. The first step in the scientific method is observation from which one formulates a question. From that question, the hypothesis is generated. A hypothesis must be phrased in a way that it can be proved or disproved (“falsifiable”). The so-called “null hypothesis” represents the default position. For example, if you are trying to prove the relationship between 2 phenomena, the null hypothesis may be a statement that there is no relationship between the observed phenomena. The next step is to test the hypothesis via 1 or more experiments. The best experiments, at least in medicine, are those that are blinded and accompanied by control groups (not submitted to the same experiments). Third is the analysis of the data obtained. The results may support the working hypothesis or “falsify” (disprove) it, leading to the creation of a new hypothesis again to be tested scientifically. Not surprising, the structure of abstracts and articles published in AJNR and other scientific journals reflects the 4 steps in the scientific method (Background and Purpose, Materials and Methods, Results, and Conclusions). Another way in which our journals adhere to the scientific method is peer review—that is, every part of the article must be open to review by others who look for possible mistakes and biases. The last part of the modern scientific method is publication.

Despite its rigid structure, the scientific method still depends on the most human capabilities: creativity, imagination, and intelligence; and without these, it cannot exist. Documentation of experiments is always flawed because everything cannot be recorded. One of the most significant problems with the scientific method is the lack of importance placed on observations that lie outside of the main hypothesis (related to lateral thinking). No matter how carefully you record what you observe, if these observations are not also submitted to the method, they cannot be accepted. This is a common problem found by paleontologists who really have no way of testing their observations; yet many of their observations (primary and secondary) are accepted as valid. Also, think about the works of Sigmund Freud that led to improved understanding of psychological development and related disorders; most were based just on observations. Many argue that because the scientific method discards observations extemporaneous to it, this actually limits the growth of scientific knowledge. Because a hypothesis only reflects current knowledge, data that contradict it may be discarded only to later become important.

Because the scientific method is basically a “trial-and-error” scheme, progress is slow. In older disciplines, there may not have been enough knowledge to develop good theories, which led to the creation of bad theories that have resulted in significant delay of progress. It can also be said that progress is many times fortuitous; while one is trying to test a hypothesis, completely unexpected and often accidental results lead to new discoveries. Just imagine how many important data have been discarded because the results did not fit the initial hypothesis.

A lot of time goes into the trial-and-error phase of an experiment, so why do it when we already know perfectly well what to expect from the results? Just peruse AJNR , and most proposed hypotheses are proved true! Hypotheses proved false are never sexy, and journals are generally not interested in publishing such studies. In the scientific method, unexpected results are not trusted, while expected and understood ones are immediately trusted. The fact that we do “this” to observe “that” may be very misleading in the long run. 2 However, in reality, many controversies could have been avoided if instead of calling it “The Scientific Method,” we simply would have called it “A Scientific Method,” leaving space for development of other methods and acceptance of those used by other disciplines. Some argue that it was called “scientific” because the ones who invented it were arrogant and pretentious.

The term “science” comes from the Latin “scientia,” meaning knowledge. Aristotle equated science with reliability because it could be rationally and logically explained. Curiously, science was, for many centuries, a part of the greater discipline of philosophy. In the 14th and 15th centuries, “natural philosophy” was born; by the start of the 17th century, it had become “natural sciences.” It was during the 16th century that Francis Bacon popularized the inductive reasoning methods that would thereafter become known as the scientific method. Western reasoning is based on our faith in truth, many times absolute truth. Beginning assumptions that then become hypotheses are subjectively accepted as being true; thus, the scientific method took longer to be accepted by Eastern civilizations whose concept of truth differs from ours. It is possible that the scientific method is the greatest unifying activity of the human race. Although medicine and philosophy have been separated from each other by centuries, there is a current trend to unite both again.

The specialty of psychiatry did not become “scientific” until the widespread use of medications and therapeutic procedures offered the possibility of being examined by the scientific method. In the United States and Europe, the number of psychoanalysts has progressively declined; and most surprising, philosophers are taking their place. 3 The benefits philosophy offers are that it puts patients first, supports new models of service delivery, and reconnects researchers in different disciplines (it is the advances in neurosciences that demand answers to the more abstract questions that define a human “being”). Philosophy provides psychiatrists with much-needed generic thinking skills; and because philosophy is more widespread than psychiatry and recognizes its importance, it provides a more universal and open environment. 4 This is an example of a soft discipline merging with a hard one (medicine) for the improvement of us all. However, this is not the case in other areas.

For about 10 years, the National Science Foundation has sponsored the “Empirical Implications of Theoretical Models” initiative in political science. 5 A major complaint is that most political science literature consists of noncumulative empirical studies and very few have a “formal” component. The formal part refers to accumulation of data and use of statistics to prove or disprove an observation (thus, the use of the scientific method). For academics in political science, the problem is that some journals no longer accept publications that are based on unproven theoretic models, and this poses a significant problem to the “non-natural” sciences. 6 In this case, the social sciences try to emulate the “hard” sciences, and this may not be the best approach. These academics and others think that using the scientific method in such instances emphasizes predictions rather than ideas, focuses learning on material activities rather than on a deep understanding of a subject, and lacks epistemic framing relevant to a discipline. 7 So, is there a better approach than the scientific method?

A provocative method called “model-based inquiry” respects the precepts of the scientific method (that knowledge is testable, revisable, explanatory, conjectural, and generative). 7 While the scientific method attempts to find patterns in natural phenomena, the model-based inquiry method attempts to develop defensible explanations. This new system sees models as tools for explanations and not explanations proper and allows going beyond data; thus, new hypotheses, new concepts, and new predictions can be generated at any point along the inquiry, something not allowed within the rigidity of the traditional scientific method.

In a different approach, the National Science Foundation charged scientists, philosophers, and educators from the University of California at Berkeley to come up with a “dynamic” alternative to the scientific method. 8 The proposed method accepts input from serendipitous occurrences and emphasizes that science is a dynamic process engaging many individuals and activities. Unlike the traditional scientific method, this new one accepts data that do not fit into organized and neat conclusions. Science is about discovery, not the justifications it seems to emphasize. 9

Obviously, I am not proposing that we immediately get rid of the traditional scientific method. Until another one is proved better, it should continue to be the cornerstone of our endeavors. However, in a world where information will grow more in the next 50 years than in the past 400 years, where the Internet has 1 trillion links, where 300 billion e-mail messages are generated every day, and 200 million Tweets occur daily, ask yourself whether it is still valid to use the same scientific method that was invented nearly 400 years ago?

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The 6 Scientific Method Steps and How to Use Them

General Education

When you’re faced with a scientific problem, solving it can seem like an impossible prospect. There are so many possible explanations for everything we see and experience—how can you possibly make sense of them all? Science has a simple answer: the scientific method.

The scientific method is a method of asking and answering questions about the world. These guiding principles give scientists a model to work through when trying to understand the world, but where did that model come from, and how does it work?

In this article, we’ll define the scientific method, discuss its long history, and cover each of the scientific method steps in detail.

What Is the Scientific Method?

At its most basic, the scientific method is a procedure for conducting scientific experiments. It’s a set model that scientists in a variety of fields can follow, going from initial observation to conclusion in a loose but concrete format.

The number of steps varies, but the process begins with an observation, progresses through an experiment, and concludes with analysis and sharing data. One of the most important pieces to the scientific method is skepticism —the goal is to find truth, not to confirm a particular thought. That requires reevaluation and repeated experimentation, as well as examining your thinking through rigorous study.

There are in fact multiple scientific methods, as the basic structure can be easily modified.  The one we typically learn about in school is the basic method, based in logic and problem solving, typically used in “hard” science fields like biology, chemistry, and physics. It may vary in other fields, such as psychology, but the basic premise of making observations, testing, and continuing to improve a theory from the results remain the same.

The History of the Scientific Method

The scientific method as we know it today is based on thousands of years of scientific study. Its development goes all the way back to ancient Mesopotamia, Greece, and India.

The Ancient World

In ancient Greece, Aristotle devised an inductive-deductive process , which weighs broad generalizations from data against conclusions reached by narrowing down possibilities from a general statement. However, he favored deductive reasoning, as it identifies causes, which he saw as more important.

Aristotle wrote a great deal about logic and many of his ideas about reasoning echo those found in the modern scientific method, such as ignoring circular evidence and limiting the number of middle terms between the beginning of an experiment and the end. Though his model isn’t the one that we use today, the reliance on logic and thorough testing are still key parts of science today.

The Middle Ages

The next big step toward the development of the modern scientific method came in the Middle Ages, particularly in the Islamic world. Ibn al-Haytham, a physicist from what we now know as Iraq, developed a method of testing, observing, and deducing for his research on vision. al-Haytham was critical of Aristotle’s lack of inductive reasoning, which played an important role in his own research.

Other scientists, including Abū Rayhān al-Bīrūnī, Ibn Sina, and Robert Grosseteste also developed models of scientific reasoning to test their own theories. Though they frequently disagreed with one another and Aristotle, those disagreements and refinements of their methods led to the scientific method we have today.

Following those major developments, particularly Grosseteste’s work, Roger Bacon developed his own cycle of observation (seeing that something occurs), hypothesis (making a guess about why that thing occurs), experimentation (testing that the thing occurs), and verification (an outside person ensuring that the result of the experiment is consistent).

After joining the Franciscan Order, Bacon was granted a special commission to write about science; typically, Friars were not allowed to write books or pamphlets. With this commission, Bacon outlined important tenets of the scientific method, including causes of error, methods of knowledge, and the differences between speculative and experimental science. He also used his own principles to investigate the causes of a rainbow, demonstrating the method’s effectiveness.

Scientific Revolution

Throughout the Renaissance, more great thinkers became involved in devising a thorough, rigorous method of scientific study. Francis Bacon brought inductive reasoning further into the method, whereas Descartes argued that the laws of the universe meant that deductive reasoning was sufficient. Galileo’s research was also inductive reasoning-heavy, as he believed that researchers could not account for every possible variable; therefore, repetition was necessary to eliminate faulty hypotheses and experiments.

All of this led to the birth of the Scientific Revolution , which took place during the sixteenth and seventeenth centuries. In 1660, a group of philosophers and physicians joined together to work on scientific advancement. After approval from England’s crown , the group became known as the Royal Society, which helped create a thriving scientific community and an early academic journal to help introduce rigorous study and peer review.

Previous generations of scientists had touched on the importance of induction and deduction, but Sir Isaac Newton proposed that both were equally important. This contribution helped establish the importance of multiple kinds of reasoning, leading to more rigorous study.

As science began to splinter into separate areas of study, it became necessary to define different methods for different fields. Karl Popper was a leader in this area—he established that science could be subject to error, sometimes intentionally. This was particularly tricky for “soft” sciences like psychology and social sciences, which require different methods. Popper’s theories furthered the divide between sciences like psychology and “hard” sciences like chemistry or physics.

Paul Feyerabend argued that Popper’s methods were too restrictive for certain fields, and followed a less restrictive method hinged on “anything goes,” as great scientists had made discoveries without the Scientific Method. Feyerabend suggested that throughout history scientists had adapted their methods as necessary, and that sometimes it would be necessary to break the rules. This approach suited social and behavioral scientists particularly well, leading to a more diverse range of models for scientists in multiple fields to use.

The Scientific Method Steps

Though different fields may have variations on the model, the basic scientific method is as follows:

#1: Make Observations 

Notice something, such as the air temperature during the winter, what happens when ice cream melts, or how your plants behave when you forget to water them.

#2: Ask a Question

Turn your observation into a question. Why is the temperature lower during the winter? Why does my ice cream melt? Why does my toast always fall butter-side down?

This step can also include doing some research. You may be able to find answers to these questions already, but you can still test them!

#3: Make a Hypothesis

A hypothesis is an educated guess of the answer to your question. Why does your toast always fall butter-side down? Maybe it’s because the butter makes that side of the bread heavier.

A good hypothesis leads to a prediction that you can test, phrased as an if/then statement. In this case, we can pick something like, “If toast is buttered, then it will hit the ground butter-first.”

#4: Experiment

Your experiment is designed to test whether your predication about what will happen is true. A good experiment will test one variable at a time —for example, we’re trying to test whether butter weighs down one side of toast, making it more likely to hit the ground first.

The unbuttered toast is our control variable. If we determine the chance that a slice of unbuttered toast, marked with a dot, will hit the ground on a particular side, we can compare those results to our buttered toast to see if there’s a correlation between the presence of butter and which way the toast falls.

If we decided not to toast the bread, that would be introducing a new question—whether or not toasting the bread has any impact on how it falls. Since that’s not part of our test, we’ll stick with determining whether the presence of butter has any impact on which side hits the ground first.

#5: Analyze Data

After our experiment, we discover that both buttered toast and unbuttered toast have a 50/50 chance of hitting the ground on the buttered or marked side when dropped from a consistent height, straight down. It looks like our hypothesis was incorrect—it’s not the butter that makes the toast hit the ground in a particular way, so it must be something else.

Since we didn’t get the desired result, it’s back to the drawing board. Our hypothesis wasn’t correct, so we’ll need to start fresh. Now that you think about it, your toast seems to hit the ground butter-first when it slides off your plate, not when you drop it from a consistent height. That can be the basis for your new experiment.

#6: Communicate Your Results

Good science needs verification. Your experiment should be replicable by other people, so you can put together a report about how you ran your experiment to see if other peoples’ findings are consistent with yours.

This may be useful for class or a science fair. Professional scientists may publish their findings in scientific journals, where other scientists can read and attempt their own versions of the same experiments. Being part of a scientific community helps your experiments be stronger because other people can see if there are flaws in your approach—such as if you tested with different kinds of bread, or sometimes used peanut butter instead of butter—that can lead you closer to a good answer.

A Scientific Method Example: Falling Toast

We’ve run through a quick recap of the scientific method steps, but let’s look a little deeper by trying again to figure out why toast so often falls butter side down.

#1: Make Observations

At the end of our last experiment, where we learned that butter doesn’t actually make toast more likely to hit the ground on that side, we remembered that the times when our toast hits the ground butter side first are usually when it’s falling off a plate.

The easiest question we can ask is, “Why is that?”

We can actually search this online and find a pretty detailed answer as to why this is true. But we’re budding scientists—we want to see it in action and verify it for ourselves! After all, good science should be replicable, and we have all the tools we need to test out what’s really going on.

Why do we think that buttered toast hits the ground butter-first? We know it’s not because it’s heavier, so we can strike that out. Maybe it’s because of the shape of our plate?

That’s something we can test. We’ll phrase our hypothesis as, “If my toast slides off my plate, then it will fall butter-side down.”

Just seeing that toast falls off a plate butter-side down isn’t enough for us. We want to know why, so we’re going to take things a step further—we’ll set up a slow-motion camera to capture what happens as the toast slides off the plate.

We’ll run the test ten times, each time tilting the same plate until the toast slides off. We’ll make note of each time the butter side lands first and see what’s happening on the video so we can see what’s going on.

When we review the footage, we’ll likely notice that the bread starts to flip when it slides off the edge, changing how it falls in a way that didn’t happen when we dropped it ourselves.

That answers our question, but it’s not the complete picture —how do other plates affect how often toast hits the ground butter-first? What if the toast is already butter-side down when it falls? These are things we can test in further experiments with new hypotheses!

Now that we have results, we can share them with others who can verify our results. As mentioned above, being part of the scientific community can lead to better results. If your results were wildly different from the established thinking about buttered toast, that might be cause for reevaluation. If they’re the same, they might lead others to make new discoveries about buttered toast. At the very least, you have a cool experiment you can share with your friends!

Key Scientific Method Tips

Though science can be complex, the benefit of the scientific method is that it gives you an easy-to-follow means of thinking about why and how things happen. To use it effectively, keep these things in mind!

Don’t Worry About Proving Your Hypothesis

One of the important things to remember about the scientific method is that it’s not necessarily meant to prove your hypothesis right. It’s great if you do manage to guess the reason for something right the first time, but the ultimate goal of an experiment is to find the true reason for your observation to occur, not to prove your hypothesis right.

Good science sometimes means that you’re wrong. That’s not a bad thing—a well-designed experiment with an unanticipated result can be just as revealing, if not more, than an experiment that confirms your hypothesis.

Be Prepared to Try Again

If the data from your experiment doesn’t match your hypothesis, that’s not a bad thing. You’ve eliminated one possible explanation, which brings you one step closer to discovering the truth.

The scientific method isn’t something you’re meant to do exactly once to prove a point. It’s meant to be repeated and adapted to bring you closer to a solution. Even if you can demonstrate truth in your hypothesis, a good scientist will run an experiment again to be sure that the results are replicable. You can even tweak a successful hypothesis to test another factor, such as if we redid our buttered toast experiment to find out whether different kinds of plates affect whether or not the toast falls butter-first. The more we test our hypothesis, the stronger it becomes!

What’s Next?

Want to learn more about the scientific method? These important high school science classes will no doubt cover it in a variety of different contexts.

Test your ability to follow the scientific method using these at-home science experiments for kids !

Need some proof that science is fun? Try making slime

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What is the Scientific Method: Steps, Definition, and Examples

Table of Contents

What is the Scientific Method?

The scientific method is an empirical process used to acquire scientific knowledge. It is broadly applied to various sciences and enables the testing and validation of a scientific hypothesis. The problem is defined based on various observations. For example, a question can arise from the observation of a natural phenomenon. This question can lead to the formulation of a hypothesis and predictions. These can be tested by collecting data using the appropriate methodology. The final steps of the scientific method include data analysis and validation of the hypothesis. Altogether, the conclusions drawn from the scientific method will lead to new questions. This will ultimately improve our knowledge towards a better comprehension of the world surrounding us.

When was the Scientific Method Invented? Who Invented the Scientific Method?

Even though various scientific methodologies were elaborated in ancient Egypt and Babylonia, the inventor of the scientific method is usually considered to be Aristotle 1 . This antique Greek philosopher introduced empiricism to science in his text Posterior Analytics 2 . In other words, empiricism means that our scientific knowledge must be based on observations and empirical evidence. This is a key concept of the scientific method. The term “scientific method” became popular much later during the 19 th and 20 th centuries when it was broadly introduced into dictionaries and encyclopedias 3 . 

What are the Steps of the Scientific Method?

What is the first step of the scientific method, step 1- what is a scientific question and how to use the scientific method.

First of all, the scientific method begins with a question, something that needs to be answered. This problem can arise from initial observations leading to a specific question, which would ideally be something that you can measure or quantify. This initial question will later lead to the formulation of the working hypothesis.

What is the Second Step of the Scientific Method?

Step 2- literature search.

Before performing scientific experiments in a laboratory, every scientist will begin his research by doing an extensive literature search. This is a crucial step of the scientific method because it will reveal what is already known about the problem. The idea is to see if anything relevant to the question is already known. In addition, the literature search can be used to determine the appropriate methodology to address the question.

What is the Third Step of the Scientific Method?

Step 3- formulation of the hypothesis and predictions.

Following extensive background research, the scientist can then formulate the hypothesis. It is a plausible assumption based on the scientific knowledge and the methodology available. The scientist can then predict the possible outcome before performing any experiments. For example, a scientist will formulate the hypothesis that if he changes the parameter or variable X, it could result in different effects (A, B, or C).

What is the Fourth Step of the Scientific Method?

Step 4- experimental design, scientific experiment, and data collection.

Obviously, experiments are an important part of the scientific method. Every rigorous scientific experiment needs to be performed using the appropriate methodology. For instance, the instrument used to test the hypothesis must be accurate and efficient. In order to be valid, the experiment must be performed along with appropriate control groups and in controlled conditions to assess the effect of a single parameter at a time. Furthermore, the scientist must take into account all the factors that can introduce a bias during data collection. The experiment also needs to be reproduced a few times to make sure that the results are reproducible and are not obtained randomly. Finally, different methodologies can be used to test the same hypothesis, therefore strengthening the validity of the scientific findings.

What is the Fifth Step of the Scientific Method?

Step 5- data analysis.

Once data collection is over, the scientist can proceed to its analysis. The collected data can be presented in different ways such as pictures, schemas, videos, etc. If numerical data was obtained, it can be presented in a chart. The type of chart selected for graphical representations depends on the type of question. For example, proportions are easily represented in a pie chart whereas a bar chart will be better suited to show the evolution of monthly sales of a company through the years. In addition, the scientist can perform various mathematical equations and statistical analyses to further characterize his dataset.

What is the Sixth Step of the Scientific Method?

Step 6- hypothesis validation or invalidation, and formulation of new related questions.

It is now time to draw conclusions about the initial question. The data collected and analyzed can either validate or invalidate the hypothesis. When drawing conclusions, the scientist must be critical regarding the quality of the data obtained and he should also consider the limitations of the methodology used for testing. Often, the conclusions will lead to additional questions and the formulation of new hypotheses.

What is the Seventh Step of the Scientific Method?

Step 7- sharing the scientific discoveries: publication and peer review.

Someone could easily become an improvised scientist and apply the scientific method to validate or invalidate his own hypothesis. However, what makes the strength of the scientific method is to share the knowledge gained from a scientific experiment that was performed. This way, the scientific community can benefit from the work of others before establishing their own hypotheses. Every research project published therefore contribute to broader scientific advances, even when the initial hypothesis was proven wrong.  In addition, our comprehension of a specific scientific topic is constantly evolving as it can be either validated or even sometimes challenged by the completion of more advanced research projects.

The scientific method is a cornerstone of science and this is why it is important to teach it to kids. This concept is generally taught to children during the 4 th , 5 th, or 6 th grade. The scientific method can help these kids to develop critical thinking and to give them the tools required to solve complex problems.

How to Use the Scientific Method and How to Design an Experiment Using the Scientific Method? An Example Applied to Drug Discovery

The scientific method can be applied to answer various questions related to biology, psychology, sociology, etc. Here, we have already explained all the steps constituting the scientific method and their respective order. Let’s now see a fictional example to show how the scientific method can be applied to solve complex problems in the pharmaceutical industry.

Step1: What is a Scientific Question?

Let’s say that a chemist is looking for new drugs that could be used in the pharmaceutical industry. The initial question could be something like “Is there a better treatment to control the blood pressure of patients?”. This is a good example showing how the rigorous application of the scientific method can answer a complex question.

Step 2: Literature research

The scientist will then proceed to an extensive literature search and gather all the information available for the active molecules already used as treatments. During his research, the chemist noticed a molecule that could be chemically transformed to alter its structure. In addition, the structure of the original molecule is available, and bio-informatics analysis indicates that the modification would occur in the active site of the molecule.

Step 3: What is an Example of a Hypothesis, How to Write a Hypothesis, and What is a Prediction in Science?

The scientist, therefore, emits the hypothesis that this modification could increase the efficiency of the treatment. He then predicts that the modification of the molecule will increase its binding to receptors located on the surface of blood vessels and that it will reduce blood pressure and side effects.

Step 4: Experiment and data collection

In vitro experiments.

The scientist decides to first test his hypothesis by measuring how the alteration of the active molecule can affect its capacity to bind the receptor. He will use purified molecules from either the original formula or the altered version of the molecule. Then, he will measure the binding capacity of the molecules towards their target receptor in a test tube.

In Vivo Experiments

To assess the biological properties of the newly identified molecule, the scientist will next use animals to analyze how the molecule can affect a complex organism such as rats. This is a complex experiment that needs to be designed properly in order to draw the right conclusions. The scientist decides to use obese rats that are prone to high blood pressure to test the efficiency of his new drug. Three groups will be monitored. The first group will be obese rats receiving no treatment at all. The second will contain animals receiving the original form of the molecule whereas the third will be administered the new molecule.

The experiment must be performed in controlled conditions

In order to be valid, the experiment needs to be performed in controlled conditions. To consider additional factors that might introduce a bias during data analysis, the groups compared must be homogeneous. Many factors can influence data interpretation and to make sure to draw the right conclusions, the scientist decides to use only male rats of approximately the same age. The blood pressure of these animals will then be monitored over the weeks and blood samples will be taken to reveal changes in its content.

Step 5: Data analysis

The results obtained during data collection can be presented in various graphical representations. For instance, the strength of the binding exhibited by these different molecules can be easily compared in a simple bar chart. The blood pressure measurements for each group can be presented as a function of time since the beginning of the treatment in a scatter plot. In addition, a trend line or regression line can be drawn on the graph to emphasize the various trends exhibited by each group of animals.

Step 6: Validation of the hypothesis

Once the different scientific experiments are performed, the scientist will be able to re-examine the initial hypothesis. If the methodology was appropriate and the influence of external factors was reduced to a minimum, the scientist will then be able to use his data and analysis to validate or invalidate his initial hypothesis.

In this example, the scientist will conclude that the modification of an existing molecule used to regulate blood pressure can increase its efficiency in comparison with the original drug. However, a major limitation of this study is that it was performed on an animal model. One could therefore ask if this newly identified molecule would be equally efficient on human patients. As you can see, the application of the scientific method for this research raised another important question, which can then be addressed by other scientists.

Step 7: Publication and peer review

In order to benefit the entire scientific community, a scientist must publish his findings. First, the scientist will first write an article summarizing his research project. He can then submit his article to a scientific journal where it will be reviewed by peers to ensure the quality of the results before their publication. Once the results are published, they can be accessible to the whole scientific community and can be cited in the work of other scientists. Altogether, this process allows the expansion of knowledge in a particular scientific field.

The Scientific Method – A Short Quiz

Question 1: classify these steps of the scientific method in the right order.

• Literature search
• Ask a question
• Publication
• Data analysis
• Validation of the hypothesis
• Formulation of the hypothesis and predictions

A) 2-3-7-1-5-6-4

B) 3-2-7-1-5-6-4

C) 3-2-7-1-5-4-6

D) 2-3-7-1-5-4-6

Question 2: To be able to draw valid conclusions, a scientist must use a methodology that…

• Generate reproducible data
• Can appropriately test the hypothesis
• Is precise enough to distinguish between conditions
• Is performed in a controlled environment

B) 1, 2 and 3

C) 2, 3 and 4

D) 1, 2, 3 and 4

Understanding Science

How science REALLY works...

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Correcting misconceptions.

Many students have misconceptions about what science is and how it works. This section explains and corrects some of the most common misconceptions that students are likely have trouble with. If you are interested in common misconceptions about  teaching  the nature and process of science, visit our page on that topic .

Jump to: Misinterpretations of the scientific process | Misunderstandings of the limits of science | Misleading stereotypes of scientists | Vocabulary mix-ups | Roadblocks to learning science

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Misinterpretations of the scientific process

Misconception: science is a collection of facts..

CORRECTION:

Because science classes sometimes revolve around dense textbooks, it’s easy to think that’s all there is to science: facts in a textbook. But that’s only part of the picture. Science  is  a body of knowledge that one can learn about in textbooks, but it is also a process. Science is an exciting and dynamic process for discovering how the world works and building that knowledge into powerful and coherent frameworks. To learn more about the process of science, visit our section on  How science works .

MISCONCEPTION: Science is complete.

Since much of what is taught in introductory science courses is knowledge that was constructed in the 19th and 20th centuries, it’s easy to think that science is finished — that we’ve already discovered most of what there is to know about the natural world . This is far from accurate. Science is an ongoing process, and there is much more yet to learn about the world. In fact, in science, making a key discovery often leads to many new questions ripe for investigation. Furthermore, scientists are constantly elaborating, refining, and revising established scientific ideas based on new evidence and perspectives. To learn more about this, visit our page describing how scientific ideas lead to ongoing research .

MISCONCEPTION: There is a single Scientific Method that all scientists follow.

“The Scientific Method” is often taught in science courses as a simple way to understand the basics of scientific testing. In fact, the Scientific Method represents how scientists usually write up the results of their studies (and how a few investigations are actually done), but it is a grossly oversimplified representation of how scientists generally build knowledge. The process of science is exciting, complex, and unpredictable. It involves many different people, engaged in many different activities, in many different orders. To review a more accurate representation of the process of science, explore our  flowchart .

MISCONCEPTION: The process of science is purely analytic and does not involve creativity.

Perhaps because the Scientific Method presents a linear and rigid representation of the process of science, many people think that doing science involves closely following a series of steps, with no room for creativity and inspiration. In fact, many scientists recognize that creative thinking is one of the most important skills they have — whether that creativity is used to come up with an alternative hypothesis, to devise a new way of testing an idea, or to look at old data in a new light. Creativity is critical to science!

MISCONCEPTION: When scientists analyze a problem, they must use either inductive or deductive reasoning.

Scientists use all sorts of different reasoning modes at different times — and sometimes at the same time — when analyzing a problem. They also use their creativity to come up with new ideas, explanations, and tests. This isn’t an either/or choice between induction and deduction. Scientific analysis often involves jumping back and forth among different modes of reasoning and creative brainstorming! What’s important about scientific reasoning is not what all the different modes of reasoning are called, but the fact that the process relies on careful, logical consideration of how evidence supports or does not support an idea, of how different scientific ideas are related to one another, and of what sorts of things we can expect to observe if a particular idea is true. If you are interested in learning about the difference between induction and deduction, visit our  FAQ on the topic .

MISCONCEPTION: Experiments are a necessary part of the scientific process. Without an experiment, a study is not rigorous or scientific.

Perhaps because the Scientific Method and popular portrayals of science emphasize  experiments , many people think that science can’t be done  without  an experiment. In fact, there are  many  ways to test almost any scientific idea; experimentation is only one approach. Some ideas are best tested by setting up a  controlled experiment  in a lab, some by making detailed observations of the natural world, and some with a combination of strategies. To study detailed examples of how scientific ideas can be tested fairly, with and without experiments, check out our side trip  Fair tests: A do-it-yourself guide .

MISCONCEPTION: "Hard" sciences are more rigorous and scientific than "soft" sciences.

Some scientists and philosophers have tried to draw a line between “hard” sciences (e.g., chemistry and physics) and “soft” ones (e.g., psychology and sociology). The thinking was that hard science used more rigorous, quantitative methods than soft science did and so were more trustworthy. In fact, the rigor of a scientific study has much more to do with the investigator’s approach than with the discipline. Many psychology studies, for example, are carefully controlled, rely on large sample sizes, and are highly quantitative. To learn more about how rigorous and fair tests are designed, regardless of discipline, check out our side trip  Fair tests: A do-it-yourself guide .

MISCONCEPTION: Scientific ideas are absolute and unchanging.

Because science textbooks change very little from year to year, it’s easy to imagine that scientific ideas don’t change at all. It’s true that some scientific ideas are so well established and supported by so many lines of evidence, they are unlikely to be completely overturned. However, even these established ideas are subject to modification based on new evidence and perspectives. Furthermore, at the cutting edge of scientific research — areas of knowledge that are difficult to represent in introductory textbooks — scientific ideas may change rapidly as scientists test out many different possible explanations trying to figure out which are the most accurate. To learn more about this, visit our page describing  how science aims to build knowledge .

MISCONCEPTION: Because scientific ideas are tentative and subject to change, they can't be trusted.

Especially when it comes to scientific findings about health and medicine, it can sometimes seem as though scientists are always changing their minds. One month the newspaper warns you away from chocolate’s saturated fat and sugar; the next month, chocolate companies are bragging about chocolate’s antioxidants and lack of trans-fats. There are several reasons for such apparent reversals. First, press coverage tends to draw particular attention to disagreements or ideas that conflict with past views. Second, ideas at the cutting edge of research (e.g., regarding new medical studies) may change rapidly as scientists test out many different possible explanations trying to figure out which are the most accurate. This is a normal and healthy part of the process of science. While it’s true that all scientific ideas are subject to change if warranted by the evidence, many scientific ideas (e.g., evolutionary theory, foundational ideas in chemistry) are supported by many lines of evidence, are extremely reliable, and are unlikely to change. To learn more about provisionality in science and its portrayal by the media, visit a section from our  Science Toolkit .

MISCONCEPTION: Scientists' observations directly tell them how things work (i.e., knowledge is "read off" nature, not built).

Because science relies on observation and because the process of science is unfamiliar to many, it may seem as though scientists build knowledge directly through observation. Observation  is  critical in science, but scientists often make  inferences  about what those observations mean. Observations are part of a complex process that involves coming up with ideas about how the natural world works and seeing if observations back those explanations up. Learning about the inner workings of the natural world is less like reading a book and more like writing a non-fiction book — trying out different ideas, rephrasing, running drafts by other people, and modifying text in order to present the clearest and most accurate explanations for what we observe in the natural world. To learn more about how scientific knowledge is built, visit our section  How science works .

MISCONCEPTION: Science proves ideas.

Journalists often write about “scientific proof” and some scientists talk about it, but in fact, the concept of proof — real, absolute proof — is not particularly scientific. Science is based on the principle that  any  idea, no matter how widely accepted today, could be overturned tomorrow if the evidence warranted it. Science accepts or rejects ideas based on the evidence; it does not prove or disprove them. To learn more about this, visit our page describing  how science aims to build knowledge .

MISCONCEPTION: Science can only disprove ideas.

This misconception is based on the idea of falsification, philosopher Karl Popper’s influential account of scientific justification, which suggests that all science can do is reject, or falsify, hypotheses — that science cannot find evidence that  supports  one idea over others. Falsification was a popular philosophical doctrine — especially with scientists — but it was soon recognized that falsification wasn’t a very complete or accurate picture of how scientific knowledge is built. In science, ideas can never be completely proved or completely disproved. Instead, science accepts or rejects ideas based on supporting and refuting evidence, and may revise those conclusions if warranted by new evidence or perspectives.

MISCONCEPTION: If evidence supports a hypothesis, it is upgraded to a theory. If the theory then garners even more support, it may be upgraded to a law.

This misconception may be reinforced by introductory science courses that treat hypotheses as “things we’re not sure about yet” and that only explore established and accepted theories. In fact, hypotheses, theories, and laws are rather like apples, oranges, and kumquats: one cannot grow into another, no matter how much fertilizer and water are offered. Hypotheses, theories, and laws are all scientific explanations that differ in breadth — not in level of support. Hypotheses are explanations that are limited in scope, applying to fairly narrow range of phenomena. The term  law  is sometimes used to refer to an idea about how observable phenomena are related — but the term is also used in other ways within science. Theories are deep explanations that apply to a broad range of phenomena and that may integrate many hypotheses and laws. To learn more about this, visit our page on  the different levels of explanation in science .

MISCONCEPTION: Scientific ideas are judged democratically based on popularity.

When newspapers make statements like, “most scientists agree that human activity is the culprit behind global warming,” it’s easy to imagine that scientists hold an annual caucus and vote for their favorite hypotheses. But of course, that’s not quite how it works. Scientific ideas are judged not by their popularity, but on the basis of the evidence supporting or contradicting them. A hypothesis or theory comes to be accepted by many scientists (usually over the course of several years — or decades!) once it has garnered many lines of supporting evidence and has stood up to the scrutiny of the scientific community. A hypothesis accepted by “most scientists,” may not be “liked” or have positive repercussions, but it is one that science has judged likely to be accurate based on the evidence. To learn more about  how science judges ideas , visit our series of pages on the topic in our section on how science works.

MISCONCEPTION: The job of a scientist is to find support for his or her hypotheses.

This misconception likely stems from introductory science labs, with their emphasis on getting the “right” answer and with congratulations handed out for having the “correct” hypothesis all along. In fact, science gains as much from figuring out which hypotheses are likely to be wrong as it does from figuring out which are supported by the evidence. Scientists may have personal favorite hypotheses, but they strive to consider multiple hypotheses and be unbiased when evaluating them against the evidence. A scientist who finds evidence contradicting a favorite hypothesis may be surprised and probably disappointed, but can rest easy knowing that he or she has made a valuable contribution to science.

MISCONCEPTION: Scientists are judged on the basis of how many correct hypotheses they propose (i.e., good scientists are the ones who are "right" most often).

The scientific community  does  value individuals who have good intuition and think up creative explanations that turn out to be correct — but it  also  values scientists who are able to think up creative ways to test a new idea (even if the test ends up contradicting the idea) and who spot the fatal flaw in a particular argument or test. In science, gathering evidence to determine the accuracy of an explanation is just as important as coming up with the explanation that winds up being supported by the evidence.

MISCONCEPTION: Investigations that don't reach a firm conclusion are useless and unpublishable.

Perhaps because the last step of the Scientific Method is usually “draw a conclusion,” it’s easy to imagine that studies that don’t reach a clear conclusion must not be scientific or important. In fact,  most  scientific studies don’t reach “firm” conclusions. Scientific articles usually end with a discussion of the limitations of the tests performed and the alternative hypotheses that might account for the phenomenon. That’s the nature of scientific knowledge — it’s inherently tentative and could be overturned if new evidence, new interpretations, or a better explanation come along. In science, studies that carefully analyze the strengths and weaknesses of the test performed and of the different alternative explanations are particularly valuable since they encourage others to more thoroughly scrutinize the ideas and evidence and to develop new ways to test the ideas. To learn more about publishing and scrutiny in science, visit our discussion of  peer review .

MISCONCEPTION: Scientists are completely objective in their evaluation of scientific ideas and evidence.

Scientists do strive to be unbiased as they consider different scientific ideas, but scientists are people too. They have different personal beliefs and goals — and may favor different hypotheses for different reasons. Individual scientists may not be completely objective, but science can overcome this hurdle through the action of the scientific community, which scrutinizes scientific work and helps balance biases. To learn more, visit  Scientific scrutiny  in our section on the social side of science.

MISCONCEPTION: Scientists' personal traits, experiences, emotions, and values don't factor into the process of science.

Scientists’ personal traits, experiences, emotions, and values influence their selection of research topic, hypotheses, chosen research methods, and interpretations of results and evidence, shaping the course of science in many ways. For example, a social scientist who has experienced poverty might be more likely to study this topic and might formulate different hypotheses about its causes than someone from a different background. Furthermore, experiencing curiosity and wonder is a key motivation for many scientists to pursue their work. Because science is a human endeavor, these fundamentally human traits (our unique identities, emotions, and values) play their role in the process. This means that scientists cannot be completely objective (see above). However, individual biases can be overcome through community scrutiny, helping science self-correct and continue to build more and more accurate explanations for how the world works.

MISCONCEPTION: Science is pure. Scientists work without considering the applications of their ideas.

It’s true that some scientific research is performed without any attention to its applications, but this is certainly not true of all science. Many scientists choose specific areas of research (e.g., malaria genetics) because of the practical ramifications new knowledge in these areas might have. And often, basic research that is performed without any aim toward potential applications later winds up being extremely useful. To learn about some of the many applications of scientific knowledge visit  What has science done for you lately?

Misunderstandings of the limits of science

Misconception: science contradicts the existence of god..

Because of some vocal individuals (both inside and outside of science) stridently declaring their beliefs, it’s easy to get the impression that science and religion are at war. In fact, people of many different faiths and levels of scientific expertise see no contradiction at all between science and religion. Because science deals only with  natural  phenomena and explanations, it cannot support or contradict the existence of  supernatural  entities — like God. To learn more, visit our side trip  Science and religion: Reconcilable differences .

MISCONCEPTION: Science and technology can solve all our problems.

The feats accomplished through the application of scientific knowledge are truly astounding. Science has helped us eradicate deadly diseases, communicate with people all over the world, and build  technologies  that make our lives easier everyday. But for all scientific innovations, the costs must be carefully weighed against the benefits. And, of course, there’s no guarantee that solutions for some problems (e.g., finding an HIV vaccine) exist — though science is likely to help us discover them if they do exist. Furthermore, some important human concerns (e.g. some spiritual and aesthetic questions) cannot be addressed by science at all. Science is a marvelous tool for helping us understand the natural world, but it is not a cure-all for whatever problems we encounter.

Misleading stereotypes of scientists

Misconception: science is a solitary pursuit..

When scientists are portrayed in movies and television shows, they are often ensconced in silent laboratories, alone with their bubbling test-tubes. This can make science seem isolating. In fact, many scientists work in busy labs or field stations, surrounded by other scientists and students. Scientists often collaborate on studies with one another, mentor less experienced scientists, and just chat about their work over coffee. Even the rare scientist who works entirely alone depends on interactions with the rest of the scientific community to scrutinize his or her work and get ideas for new studies. Science is a social endeavor. To learn more, visit our section on the  Social side of science .

MISCONCEPTION: Science is done by "old, white men."

While it is true that Western science used to be the domain of white males, this is no longer the case. The diversity of the scientific community is expanding rapidly. Science is open to anyone who is curious about the natural world and who wants to take a scientific approach to his or her investigations. To see how science benefits from a diverse community, visit  Diversity makes the difference .

MISCONCEPTION: Scientists are atheists.

This is far from true. A 2005 survey of scientists at top research universities found that more than 48% had a religious affiliation and that more than 75% believed that religions convey important truths. 1  Some scientists are not religious, but many others subscribe to a specific faith and/or believe in higher powers. Science itself is a secular pursuit, but welcomes participants from all religious faiths. To learn more, visit our side trip  Science and religion: Reconcilable differences .

Vocabulary mix-ups

Some misconceptions occur simply because scientific language and everyday language use some of the same words differently.

Facts  are statements that we know to be true through direct  observation . In everyday usage, facts are a highly valued form of knowledge because we can be so confident in them. Scientific thinking, however, recognizes that, though facts are important, we can only be completely confident about relatively simple statements. For example, it may be a fact that there are three trees in your backyard. However, our knowledge of how all trees are related to one another is not a fact; it is a complex body of knowledge based on many different  lines of evidence  and reasoning that may change as new  evidence  is discovered and as old evidence is interpreted in new ways. Though our knowledge of tree relationships is not a fact, it is broadly applicable, useful in many situations, and synthesizes many individual facts into a broader framework.  Science  values facts but recognizes that many forms of knowledge are more powerful than simple facts.

In everyday language, a  law  is a rule that must be abided or something that can be relied upon to occur in a particular situation. Scientific laws, on the other hand, are less rigid. They may have exceptions, and, like other scientific knowledge, may be modified or rejected based on new evidence and perspectives. In science, the term  law  usually refers to a generalization about  data  and is a compact way of describing what we’d expect to happen in a particular situation. Some laws are non-mechanistic statements about the relationship among observable phenomena. For example, the ideal gas law describes how the pressure, volume, and temperature of a particular amount of gas are related to one another. It does not describe how gases  must  behave; we know that gases do not precisely conform to the ideal gas law. Other laws deal with phenomena that are not directly observable. For example, the second law of thermodynamics deals with entropy, which is not directly observable in the same way that volume and pressure are. Still other laws offer more mechanistic explanations of phenomena. For example, Mendel’s first law offers a  model  of how genes are distributed to gametes and offspring that helps us make  predictions  about the outcomes of genetic crosses. The term  law  may be used to describe many different forms of scientific knowledge, and whether or not a particular idea is called a law has much to do with its discipline and the time period in which it was first developed.

Observation

In everyday language, the word  observation  generally means something that we’ve seen with our own eyes. In science, the term is used more broadly. Scientific observations can be made directly with our own senses or may be made indirectly through the use of tools like thermometers, pH test kits, Geiger counters, etc. We can’t actually  see  beta particles, but we can observe them using a Geiger counter. To learn more about the role of observation in science, visit  Observation beyond our eyes  in our section on how science works.

In everyday language, the word  hypothesis  usually refers to an educated guess — or an idea that we are quite uncertain about. Scientific hypotheses, however, are much more informed than any guess and are usually based on prior experience, scientific background knowledge, preliminary observations, and logic. In addition, hypotheses are often supported by many different lines of evidence — in which case, scientists are more confident in them than they would be in any mere “guess.” To further complicate matters, science textbooks frequently misuse the term in a slightly different way. They may ask students to make a  hypothesis  about the outcome of an experiment (e.g., table salt will dissolve in water more quickly than rock salt will). This is simply a prediction or a guess (even if a well-informed one) about the outcome of an experiment. Scientific hypotheses, on the other hand, have explanatory power — they are explanations for phenomena. The idea that table salt dissolves faster than rock salt is not very hypothesis-like because it is not very explanatory. A more scientific (i.e., more explanatory) hypothesis might be “The amount of surface area a substance has affects how quickly it can dissolve. More surface area means a faster rate of dissolution.” This hypothesis has some explanatory power — it gives us an idea of  why  a particular phenomenon occurs — and it is testable because it generates expectations about what we should observe in different situations. If the hypothesis is accurate, then we’d expect that, for example, sugar processed to a powder should dissolve more quickly than granular sugar. Students could examine rates of dissolution of many different substances in powdered, granular, and pellet form to further test the idea. The statement “Table salt will dissolve in water more quickly than rock salt” is not a hypothesis, but an expectation generated by a hypothesis. Textbooks and science labs can lead to confusions about the difference between a hypothesis and an expectation regarding the outcome of a scientific test. To learn more about scientific hypotheses, visit  Science at multiple levels  in our section on how science works.

In everyday language, the word  theory  is often used to mean a hunch with little evidential support. Scientific theories, on the other hand, are broad explanations for a wide range of phenomena. They are concise (i.e., generally don’t have a long list of exceptions and special rules), coherent, systematic, and can be used to make predictions about many different sorts of situations. A theory is most  acceptable  to the scientific community when it is strongly supported by many different lines of evidence — but even theories may be modified or overturned if warranted by new evidence and perspectives. To learn more about scientific theories, visit  Science at multiple levels  in our section on how science works.

Falsifiable

The word  falsifiable  isn’t used much in everyday language, but when it is, it is often applied to ideas that have been shown to be untrue. When that’s the case — when an idea has been shown to be false — a scientist would say that it has been falsified. A falsifi able  idea, on the other hand, is one for which there is a conceivable  test  that might produce evidence proving the idea false. Scientists and others influenced by the ideas of the philosopher Karl Popper sometimes assert that only falsifiable ideas are scientific. However, we now recognize that science cannot once-and-for-all prove any idea to be false (or true for that matter). Furthermore, it’s clear that evidence can play a role in supporting particular ideas over others — not just in ruling some ideas out, as implied by the falsifiability criterion. When a scientist says  falsifiable , he or she probably actually means something like  testable , the term we use in this website to avoid confusion. A testable idea is one about which we could gather evidence to help determine whether or not the idea is accurate.

Uncertainty

In everyday language,  uncertainty  suggests the state of being unsure of something. Scientists, however, usually use the word when referring to measurements. The uncertainty of a measurement (not to be confused with the inherent provisionality of all scientific ideas!) is the range of values within which the true value is likely to fall. In science, uncertainty is not a bad thing; it’s simply a fact of life. Every measurement has some uncertainty. If you measure the length of a pen with a standard ruler, you won’t be able to tell whether its length is 5.880 inches, 5.875 inches, or 5.870 inches. A ruler with more precision will help narrow that range, but cannot eliminate uncertainty entirely. For more on a related idea, see our discussion of  error  below.

In everyday language, an error is simply a mistake, but in science, error has a precise statistical meaning. An error is the difference between a measurement and the true value, often resulting from taking a  sample . For example, imagine that you want to know if corn plants produce more massive ears when grown with a new fertilizer, and so you weigh ears of corn from those plants. You take the mass of your sample of 50 ears of corn and calculate an average. That average is a good estimate of what you are really interested in: the average mass of  all  ears of corn that could be grown with this fertilizer. Your estimate is not a mistake — but it does have an error (in the statistical sense of the word) since your estimate is not the true value. Sampling error of the sort described above is inherent whenever a smaller sample is taken to represent a larger entity. Another sort of error results from systematic biases in measurement (e.g., if your scale were calibrated improperly, all of your measurements would be off). Systematic error biases measurements in a particular direction and can be more difficult to quantify than sampling error.

In everyday language,  prediction  generally refers to something that a fortune teller makes about the future. In science, the term  prediction  generally means “what we would expect to happen or what we would expect to observe if this idea were accurate.” Sometimes, these scientific predictions have nothing at all to do with the future. For example, scientists have hypothesized that a huge asteroid struck the Earth 4.5 billion years ago, flinging off debris that formed the moon. If this idea were true, we would  predict  that the moon today would have a similar composition to that of the Earth’s crust 4.5 billion years ago — a prediction which does seem to be accurate. This hypothesis deals with the deep history of our solar system and yet it involves predictions — in the scientific sense of the word. Ironically, scientific predictions often have to do with past events. In this website, we’ve tried to reduce confusion by using the words  expect and  expectation  instead of  predict  and  prediction . To learn more, visit  Predicting the past  in our section on the core of science.

Belief/believe

When we, in everyday language, say that we believe in something, we may mean many things — that we support a cause, that we have faith in an idea, or that we think something is accurate. The word  belief  is often associated with ideas about which we have strong convictions, regardless of the evidence for or against them. This can generate confusion when a scientist claims to “believe in” a scientific hypothesis or theory. In fact, the scientist probably means that he or she “ accepts ” the idea — in other words, that he or she thinks the scientific idea is the most accurate available based on a critical evaluation of the evidence. Scientific ideas should always be accepted or rejected based on the evidence for or against them — not based on faith, dogma, or personal conviction.

Roadblocks to learning science

In school, many students get the wrong impression of science. While not technically misconceptions, these overgeneralizations are almost always inaccurate — and can make it more difficult for the students who hold them to learn science.

MISCONCEPTION: Science is boring.

  Memorizing facts from a textbook can be boring — but science is much more than the knowledge that makes its way into school books. Science is an ongoing and unfinished process of discovery. Some scientists travel all over the world for their research. Others set up experiments that no one has ever tried before. And all scientists are engaged in a thrilling quest — to learn something brand new about the natural world. Some parts of scientific training or investigations may be tedious, but science itself is exciting! To see how a scientific perspective can make the world a more exciting and intriguing place, visit our side trip  Think science .

MISCONCEPTION: Science isn't important in my life.

It’s easy to think that what scientists do in far-off laboratories and field stations has little relevance to your everyday life — after all, not many of us deal with super colliders or arctic plankton on a regular basis — but take another look around you. All the technologies, medical advances, and knowledge that improve our lives everyday are partly the result of scientific research. Furthermore, the choices you make when you vote in elections and support particular causes can influence the course of science. Science is deeply interwoven with our everyday lives. To see how society influences science, visit  Science and society . To learn more about how scientific advances affect your life, visit  What has science done for you lately?

MISCONCEPTION: I am not good at science.

Some students find science class difficult — but this doesn’t translate to not being good at science. First of all, school science can be very different from real science. The background knowledge that one learns in school is important for practicing scientists, but it is only part of the picture. Scientific research also involves creative problem-solving, communicating with others, logical reasoning, and many other skills that might or might not be a part of every science class. Second, science encompasses a remarkably broad set of activities. So maybe you don’t care much for the periodic table — but that doesn’t mean that you wouldn’t be great at observing wild chimpanzee behavior, building computer models of tectonic plate movement, or giving talks about psychology experiments at scientific meetings. Often when a student claims to “not be good at science,” it really just means that he or she hasn’t yet found a part of science that clicks with his or her interests and talents.

1 Ecklund, E.H., and C.P. Scheitle. 2007. Religion among academic scientists: Distinctions, disciplines, and demographics.  Social Problems  54(2):289-307.

• Teaching resources
• Unfortunately, many textbooks promulgate misconceptions about the nature and process of science. Use this list to review your textbook, and then discuss any misrepresentations with students.
• You can highlight misconceptions about science that are promulgated in the media by starting a bulletin board that highlights examples of misconceptions found in the popular press — for example, misuses of the word theory, implications that scientists always use “the scientific method,” or that experimental science is more rigorous than non-experimental science.
• Use word lists to combat misconceptions about science that stem from vocabulary mix-ups. Find out how in this article distributed with permission from Science Scope.

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• Open access
• Published: 24 August 2024

Mediation effect of perceived social support and psychological distress between psychological resilience and sleep quality among Chinese medical staff

• Nannan Wu 1 ,
• Fan Ding 2 ,
• Ronghua Zhang 4 &
• Yaoyao Cai 1  

Scientific Reports volume  14 , Article number:  19674 ( 2024 ) Cite this article

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• Health care
• Medical research

Sleep quality is crucial for the personal well-being of healthcare professionals and the health outcomes of their patients. This study aims to explore the relationship between psychological resilience (PR), perceived social support (PSS), psychological distress (comprising anxiety,depression,and stress), and sleep quality. It also examines whether PSS and psychological distress function as chain mediators between PR and sleep quality. A cross-sectional online survey was conducted using a convenient sampling method, with 454 participants included. The survey instruments included the Connor and Davidson Resilience Scale, the Perceived Social Support Scale, the 21-item Depression Anxiety Stress Scale, and the Pittsburgh Sleep Quality Index. Structural equation modeling revealed that PR significantly predicted sleep quality of Chinese medical staff. Psychological distress was identified as a mediating factor between PR and sleep quality. However, PSS did not directly mediate the relationship between PR and sleep quality. Instead, PSS and psychological distress were found to play a chain mediating role in the relationship between PR and sleep quality. This study provides new insights into the impact of PR on sleep quality, highlights the importance of PSS and psychological distress, and suggests practical implications for enhancing sleep quality among medical staff.

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Introduction.

Sleep quality is a vital component of overall health, characterized by factors such as sleep duration, sleep onset latency, and deep sleep percentage. It is well-documented that poor sleep quality is not only associated with various physical diseases but also with mental health disorders and stress-related syndromes 1 . This relationship is particularly acute among medical staff, who face unique challenges that compromise their sleep. In China, medical professionals, including doctors, nurses, and technical staff like X-ray operators, often contend with poor sleep quality due to the demanding nature of their roles. Factors such as shift work, long hours, and high occupational stress are prevalent and are exacerbated by a lower ratio of medical staff to the population and higher workload intensities compared to more developed countries 2 . These conditions are not only a concern for individual health but also pose a significant public health issue. Recent studies, including a meta-analysis, indicate that 39.2% of Chinese medical personnel experience sleep disorders-significantly higher than the general population 3 . The COVID-19 pandemic has further increased sleep disturbances among healthcare professionals to 45.1%, which is 1.5 times the rate of the general population 4 . These statistics highlight the urgent need for effective strategies to improve sleep quality among healthcare workers, crucial for their well-being and the overall efficiency of the healthcare system.

Sleep is influenced by an interplay of biological, psychological, and social factors, not only at a specific time but throughout an individual’s development 5 . Impaired sleep can significantly impact the daytime functionality and efficiency of healthcare workers, potentially compromising the quality of patient care. Biologically, irregular shifts disrupt circadian rhythms, complicating sleep initiation and maintenance. Psychologically, the high-stakes nature of medical work heightens stress and anxiety, which can further degrade sleep quality 6 . Socially, social factors within the healthcare environment such as the working conditions, work relationships, and an individual’s social support system can either alleviate or exacerbate these issues 7 . In this context, the Biopsychosocial Model, introduced by George Engel in 1977 8 , provides a comprehensive framework for analyzing the multifaceted sleep challenges faced by healthcare workers. This model integrates biological factors such as circadian rhythm disruptions, psychological elements like stress and anxiety, and social dimensions including the workplace environment and demands 9 . By adopting this holistic approach, a deeper understanding of the factors affecting sleep quality can be achieved, leading to the development of more effective interventions that consider the complex interactions between biological, psychological, and social influences.

Psychological resilience and sleep quality

Medical work is high-pressure and demanding, often involving life-and-death situations, emotional challenges, and a heavy workload. In such intense circumstances, resilience is crucial for medical professionals to maintain effectiveness and achieve mental and physical balance. Psychological resilience is regarded as an important psychological resource, commonly defined as the ability to bounce back or overcome adversity 10 . The socio-ecological model of resilience elaborates on this construct, portraying it as a dynamic and multifaceted process through which individuals retrieve or sustain their psychological well-being by tapping into psychological, social, cultural, and physical resources 11 . Resilience can be categorized into two forms: as an explanatory ability or trait-a consistent personality attribute that aids in overcoming risk elements and preserving developmental functions, adaptability, and mental health; and as a positive psychological outcome or adaptation process 12 . Previous studies have suggested that both forms of psychological resilience positively influence individuals’ sleep quality. For example, a study by Cai et al. found that resilience was negatively correlated with Pittsburgh Sleep Quality Index (PSQI) scores among disabled elders, indicating that higher levels of resilience significantly improved sleep quality 13 . Similarly, a survey of postpartum women in Saudi Arabia showed that higher levels of resilience were associated with better sleep quality 14 . Additionally, a recent longitudinal study revealed that resilience assessed during the first wave predicted sleep quality during the second wave of the COVID-19 pandemic 15 . Very recently, a cross-sectional survey also found that resilience could predict sleep quality among nurses 16 .

Resilience theory highlights adaptability and recovery as key to stress response and health maintenance, positioning psychological resilience as crucial for managing work-related stress and sleep quality in medical staff. Grounded in theory and empirical evidence, we propose the following hypothesis:

Hypothesis 1

Psychological resilience significantly predicts sleep quality in medical staff.

The potential mediating role of perceived social support between resilience and sleep quality

Social support is generally conceptualized as the belief in the availability of support from family, friends, and significant others in his/her life. It comprises two dimensions: perceived social support and received social support. Received social support quantifies the objective assistance individuals receive from their social network, while perceived social support pertains to individuals’ subjective perception and assessment of support from family, friends, and significant others 17 . Research has shown that perceived social support is a stronger predictor of an individual’s mental health compared to received social support 18 .

Perceived social support is widely recognized as an external protective factor that bolsters psychological resilience, exhibiting a positive correlation with it both in cross-sectional 19 and longitudinal 20 , 21 studies, indicating that individuals perceiving greater social support generally exhibit higher levels of resilience. However, it should be noted that while a plethora of studies affirm the positive correlation between perceived social support and resilience, a limited number of previous studies have also indicated a reverse causation-that is, resilience may enhance the perception of social support. For example, Hou et al. found that resilience was positively associated with perceived social support among Chinese nurses 22 . Chunyu et al. found that patients with substance use disorders who had high levels of resilience also reported high levels of perceived social support 23 . Resilient individuals are more likely to seek support from their surroundings and tend to expand their social networks, acquiring more support from their established networks 24 , in contrast with the non-resilient counterparts. On the other hand, scholars have long agreed that perceived social support and sleep quality are strongly related, and that perceived social support significantly predicts an individual’s sleep quality 25 . Evolutionary psychology proposes that close associations during sleep provide protection from potential dangers, enhancing sleep quality 26 . Evidence from previous studies has indicated a significantly negative relationship between perceived social support and sleep quality. For instance, Mohamed et al. showed that perceived social support significantly predicted subjective sleep quality in a sample of patients in Somalia 27 , and Guo et al. reported that adolescents with higher perceived social support exhibited better sleep quality 28 . Furthermore, medical personnel who perceived higher levels of social support reported better sleep quality during the COVID-19 pandemic 29 .

In summary, previous findings have highlighted a positive correlation between psychological resilience and perceived social support, as well as a negative association between perceived social support and sleep quality. However, the applicability of these findings to medical staff remains under-explored. Therefore, we hypothesize that:

Hypothesis 2

Perceived social support may mediate the relationship between psychological resilience and sleep quality.

The potential mediating role of psychological distress between resilience and sleep quality

Psychological distress is a well-established and practical concept in mental health, particularly in the development of public health strategies. It is defined as a state of emotional suffering characterized by a combination of depression, anxiety, and stress symptoms that collectively encapsulate intense negative emotional states indicative of maladaptive psychological responses 30 .

Empirical studies have consistently shown that resilience is negatively correlated with negative indicators of mental health, such as depression, anxiety, stress, and negative affect. Specifically, a meta-analysis revealed a negative correlation between resilience and these negative mental health indicators 31 . Additionally, extensive research has confirmed the protective role of resilience in the mental health outcomes of medical staff. For instance, a systematic review highlighted resilience as a promotable protective factor against anxiety and work-related stress among physicians 32 . Our previous research also showed that resilience mitigates the negative impacts, such as depression, resulting from adversity or exposure to traumatic events in Chinese medical staff 33 .

On the other hand, psychological distress is widely recognized as a factor that can significantly impact sleep quality 34 . Symptoms of psychological distress, such as anxiety and depression, could lead to considerable suffering and long-term adverse consequences, including poor sleep quality 35 , including poor sleep quality. A previous cross-sectional study found that Chinese resident physicians who experienced poor sleep quality had a high prevalence of depressive symptoms 36 . Another study demonstrated that the depression-anxiety-stress state in nurses was positively associated with poor sleep quality 37 . Furthermore, both cross-sectional and longitudinal studies have established that the relationship between psychological distress and sleep quality is bidirectional, indicating that poor sleep quality can exacerbate symptoms of anxiety and depression, and vice versa 38 .

The aforementioned studies illustrate a complex interrelationship between resilience, psychological distress, and sleep quality, where each may significantly influence the others. While many studies have investigated psychological resilience as a mediating factor between psychological distress and sleep quality, few have explored the role of psychological distress as a mediator between resilience and sleep quality, especially in medical staff. Therefore, we propose the following hypothesis:

Hypothesis 3

Psychological distress may mediate the relationship between psychological resilience and sleep quality.

The chain mediating role of perceived social support and psychological distress

According to the stress buffering theory, social support serves as a resource to mitigate the negative impact of stress and problems on health, thus maintaining and improving an individual’s mental health outcomes 39 . Numerous previous studies have indicated an inverse relationship between social support and psychological distress. For instance, Wang et al. found that perceived social support among healthcare workers negatively predicted anxiety symptoms 40 . Similarly, a systematic review revealed that lower levels of perceived social support were associated with more severe depressive symptoms 41 . When individuals face psychological distress, social support can serve as a coping mechanism. Therefore, individuals with sleep issues or functional limitations may experience less psychological distress if they perceive adequate social support.

Additionally, prior research has demonstrated that perceived social support may act as a mediator in the relationship between resilience and psychological problems. Hou et al. found that perceived social support partially mediated the relationship between resilience and anxiety in nurses 22 . A cross-sectional study further confirmed that perceived social support partially mediated the relationship between resilience and burden among caregivers of older adults in Singapore 42 . Furthermore, several mediation analyses have indicated that higher levels of social support are beneficial in reducing symptoms of anxiety and depression, thus leading to better sleep quality and fewer sleep problems in various population groups, including adolescents and young adults 43 , stroke patients 35 , and medical staff 29 . In summary, the relationship between psychological resilience and sleep quality may be affected first through perceived social support and then via the impact on psychological distress. Thus we finally put forward the following hypothesis:

Hypothesis 4

Psychological resilience may indirectly impact the sleep quality of medical staff by exerting a chain mediating effect on the link between perceived social support and psychological distress.

The current study

Social support and psychological distress significantly influence sleep quality, yet their effects are transient and vary with changing circumstances. In contrast, psychological resilience is a relatively stable trait that not only indicates an individual’s ability to cope with stress but also represents the psychological component of the Biopsychosocial Model, providing consistent protection across various situations 44 . This study investigates the direct effects of psychological resilience on sleep quality, as well as its indirect influences through perceived social support (a social factor) and psychological distress (a psychological factor) among medical staff.

The relationship between psychological resilience and sleep quality has been explored previously; however, the biopsychosocial mechanisms, particularly among medical staff, require further elucidation. The biological dimension is crucial, as resilience likely influences physiological stress responses and health behaviors, which in turn affect sleep quality. It remains to be determined how resilience impacts healthcare professionals’ perceptions of social support and levels of psychological distress, and whether these factors serve as independent or sequential mediators in the relationship between resilience and sleep quality, ultimately influencing biological processes related to sleep.

Drawing upon extant literature, we have devised a theoretical model, which underpins the following hypotheses grounded in the Biopsychosocial Model: (1) Psychological resilience negatively predicts sleep quality among medical staff, encapsulating the psychological component; (2) Perceived social support acts as a mediator between psychological resilience and sleep quality, illustrating the social component; (3) Psychological distress also serves as a mediator, further emphasizing the psychological component; (4) Psychological resilience exerts an indirect effect on sleep quality through the sequential mediation of perceived social support and psychological distress, elucidating the complex interactions between social, psychological, and biological pathways.

Ethical statement

The study was approved by the Ethics Committee of Shaoguan University (approval code: yxyllscb202202; approval date: 15 June 2022). All methods adhered to relevant guidelines and regulations. Participants were informed about the study’s purpose and provided voluntary, anonymous consent prior to participation, ensuring privacy protection.

Participants and procedure

This cross-sectional study was conducted in Shaoguan city, Guangdong province, China, between July 5 and 25, 2022. Participants were recruited from two hospitals in Shaoguan using a convenience sampling method. Inclusion criteria required at least one year of hospital work experience and no history of psychiatric illness or family history of psychosis. The survey was conducted on the “Wenjuan Xing” platform, a professional online questionnaire survey network platform ( https://www.wjx.cn/app/survey.aspx accessed on 5 July 2022). It was subsequently released on WeChat (known as Weixin in Chinese), a social media tool commonly used by academics to communicate research developments and findings. To ensure the quality of the questionnaire, we excluded questionnaires with an answering time of less than 5 minutes or more than 30 minutes. Additionally, each IP address was limited to one response. Also, we discarded questionnaires with inappropriate response patterns, such as repeatedly reporting of the same response to all questions.

Demographic variables

The study utilized a self-designed demographic questionnaire to collect participant information, including age, gender, marital status, education level (junior college, undergraduate, and postgraduate), and occupational details such as professional titles (primary, intermediate, and senior), positions (doctors, nurses, and others), monthly income, and years of experience.

Measurement scales

Resilience scale.

The Chinese version of the Connor-Davidson Resilience Scale (CD-RISC) was utilized to assess the levels of participants’ psychological resilience. The CD-RISC was originally developed by Connor and Davidson 45 and was translated into Chinese by Yu et al. in 2007 46 . The scale consists of 25 items and measures three dimensions: tenacity, strength, and optimism. Participants responded on a 5-point Likert scale ranging from 0 (’never’) to 4 (’always this’). The total scale’s score ranged from 0 to 100 points, with higher scores indicating greater resilience levels. In this study, the Cronbach’s alpha of the CD-RISC was 0.964.

Perceived social support scale

    The Multidimensional Scale of Perceived Social Support (MSPSS) developed by Zimet et al. 47 was utilized to to assess the levels of perceived social support. The MSPSS scale consists of 12 items, 3 dimensions: family support, friends support and significant other supports. Participants rated each item rated on a 7-point Likert scale (from 1 = very strongly disagree to 7 = very strongly agree), whose scores ranged from 12 to 84. The mean total score for the MSPSS scale ranged from 1 to 7 points, with higher scores indicating higher perceived social support. The Chinese version of MSPSS translated by Jiang et al. 48 has been demonstrated to be a reliable and valid measure among Chinese population. The Cronbach’s alpha of the MSPSS in this study was 0.954.

Depression anxiety stress scale

The Chinese version short-form 21-item Depression Anxiety Stress Scale (DASS-21) was used to measure psychological distress in this sample 49 . The DASS-21 scale contains three subscales, including depression, anxiety, and stress. Each subscale contains 7 items and there are four options are used to answer each item, ranging from 0 (not applicable) to 3 (mostly applicable). The range of scores spans from 0 to 42 points, whereby scores that are higher in value correspond to more severe levels of distress. The Cronbach’s alpha of the DASS-21 in this study was 0.949, showing excellent internal consistency in the Chinese medical staff.

Pittsburgh sleep quality index scale

    The Pittsburgh Sleep Quality Index (PSQI), developed by D. J. Buysse 50 and translated into Chinese by Tsai et al. 51 , was used to measure sleep quality. The PSQI is a 19-item, self-reported measure of subjective sleep over the past month, containing 7 items that included subjective sleep quality, sleep duration, sleep latency, sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. Items are rated on a 4-point Likert scale from 0 (not during the past month) to 3 (three or more times a week). The seven-component scores are added together to get a global PSQI score, which ranges from 0 to 21, with higher scores representing lower sleep quality. Participants with PSQI scores below 7 points are considered to have good sleep quality, while participants with PSQI scores higher than 7 points are considered to have poor sleep quality 52 . The Cronbach’s alpha of the PSQI in this study was 0.822.

Statistical analysis

SPSS version 26.0 was used to analyze sociodemographic characteristics, descriptive statistics, and Pearson correlations for key variables: resilience, perceived social support, psychological distress, and sleep quality. Subsequent structural equation modeling (SEM) analysis was performed using Amos 24.0. In this SEM analysis, an initial measurement model was constructed with observed variables representing their respective latent constructs. The hypothesized chain mediating model was then specified by incorporating paths informed by the underlying theoretical framework. The model’s fit was evaluated using various goodness-of-fit indicators: a non-significant chi-square value ( \(p> 0.05\) ), a root mean square error of approximation (RMSEA) less than 0.05, a comparative fit index (CFI) exceeding 0.95, and a standardized root mean square residual (SRMR) below 0.05 collectively suggest an adequate fit. Upon model validation, path coefficients along with their corresponding p -values were assessed to ascertain the significance of each theorized relationship at p \(< 0.05\) . Additionally, both direct and indirect effects of resilience on sleep quality, mediated through perceived social support and psychological distress, were explored. The overall direct and indirect impacts were quantified, and 95% confidence intervals (CIs) were calculated using 10,000 bootstrapping replicated samples. An indirect effect was deemed significant if its 95% CI excluded zero.

Socio-demographic characteristics

After completing the survey, a total of 533 questionnaires were collected, of which 454 were valid, resulting in a valid return rate of 85.17%. Of the 454 participants, 90 (19.8%) were males, 364 (80.2%) were females. Their ages range from 18 to 60 years old, with a mean age of 34.56 (SD=10.24). Moreover, among the participants, 316 (69.6%) were married, 232 (51.1%) had less than 10 years of work experience, and 269 (59.3%) held primary professional titles. Additionally, 244 participants had a junior college education or below, accounting for 53.8% of the total, and 291 medical staff earned less than 6000 China Yuan (CNY) per month, accounting for 64.1% of the total. Among different positions, there were 149 doctors (32.8%) and 234 nurses (51.6%). Furthermore, the mean and standard deviation of the PSQI score were 7.47 and 3.73, respectively, which are much lower than those reported by a previous study among Chinese medical staff during the peak period of the COVID-19 pandemic 29 . The study revealed that 46% of the participants, or 209 individuals, suffered from poor sleep quality (PSQI \(> 7\) ).

Descriptive statistics and correlation analysis

A Pearson correlation analysis was conducted to examine the bivariate correlations among the variables under study. According to the results presented in Table 1 , resilience was significantly and positively correlated with perceived social support ( \(\gamma = 0.605, p< 0.01\) ), while resilience was significantly negatively correlated with psychological distress ( \(\gamma = -0.576, p < 0.01\) ) and PSQI scores ( \(\gamma =-0.46, p < 0.01\) ), with higher PSQI scores indicating poorer sleep quality. Moreover, perceived social support was significant negative correlated with psychological distress ( \(\gamma = -0.558, p < 0.01\) ), as well as PSQI scores ( \(\gamma = -0.408, p < 0.01\) ). Finally, psychological distress was significant positive correlated with PSQI scores ( \(\gamma = 0.531, p < 0.01\) ).

Measurement model

In the initial measurement model, there are four latent factors (psychological resilience, perceived social support, psychological distress, and sleep quality) and 16 observed variables. Since the factor loadings for both habitual sleep efficiency and the use of sleeping medication on the latent variable of sleep quality were too low ( \(<0.4\) ), they were ruled out from the model. After modification, the test of the revised measurement model resulted in an acceptable fit to the data, indicated by the following goodness of fit statistics: \(\chi ^2 = 156.035, df = 72, {\chi ^2}/{df} = 2.167, p <0.001; SRMR = 0.036; RMSEA = 0.051; NFI = 0.956; CFI = 0.977; GFI = 0.948; AGFI = 0.927\) . Moreover, standardized factor loadings in the revised model ranged from 0.48 to 0.92 (see Fig. 1 ), showing that they loaded significantly in the predicted directions in their respective constructs (all \(p <0.05\) ).

Structural model

To find a better relationship among the study variables, we tested three alternated models. Specifically, a full mediated model (Model I), which contained mediators (perceived social support and psychological distress) and no direct link from resilience to sleep quality, was firstly established and assessed. The results showed that Model I fitted the data well \([~ \chi ^2 = 247.964, df = 99, {\chi ^2}/{df} = 2.505, \textit{p} <0.001; CFI = 0.966; GFI = 0.936; IFI = 0.966; SRMR = 0.0569; RMSEA = 0.058 ]\) .Then, a partially mediated model (Model II) that drew a direct path from resilience to sleep quality was tested. While the fit indices of Model II were acceptable \([ \chi ^2 = 236.486, df = 98, {\chi ^2}/{df} = 2.413, p <0.001; CFI = 0.968; GFI = 0.94; IFI = 0.969; SRMR = 0.0551; RMSEA = 0.056. ]\) , the path between PSS and sleep quality was not significant ( \(\beta = 0.003, {p} = 0.963>0.05\) ). Finnaly, we removed the direct pathway from PSS to Sleep Quality and add the path from PSS to psychological distress (Model III). Model III indicated a good model \([ \chi ^2 = 199.968, df = 98, {\chi ^2}/{df} = 2.04, \textit{p} <0.001; CFI = 0.977; GFI = 0.948; IFI = 0.977; SRMR = 0.0413; RMSEA = 0.048 ]\) . In addition, all path coefficients were found to be significant. Moreover, it was seen that the AIC and ECVI values in Model III (AIC=222.035; ECVI=0.49) were lower than those in Model I and Model II. Therefore, Model III was preferred (see Fig. 1 ) because of the significant pathways, better-fit indices, and lower AIC and ECVI values, as shown in Table 2 .

The chain-mediation model of psychological resilience, perceived social support,  psychological distress, and sleep quality. It includes values for standardized regression coefficients for direct paths. *** p \(< 0.001\) .

Bootstapping

The study employed the bootstrapping procedure of AMOS 24.0 to test the significance of the partially chain mediated model, i.e., Model III, with a bootstrap sample of 10,000 was specified. Moreover, control variables, including age, work years, professional title, monthly incomes, and positions, were set to reduce statistical errors 52 .

Direct, indirect, and total effects are presented in Table 3 and Fig. 1 . It was seen that the link from psychological resilience to PSQI scores was significant ( \(\beta = - 0.26\) , p \(< 0.001\) ). Furthermore, psychological resilience was found to have a significant positive correlation with perceived social support ( \(\beta = 0.669, p < 0.001\) ) and a significant negative correlation with psychological distress ( \(\beta = -0.433, p < 0.001\) ). In addition, PSS was found to have a significant negatively impact on medical staff’s psychological distress ( \(\beta = -0.344\) , \(p < 0.001\) ). Finally, it was found that psychological distress was positively associated with PSQI scores ( \(\beta = 0.489\) , \(p < 0.001\) ).

As shown in Table 3 , the indirect effect of psychological resilience on sleep quality via psychological distress was statistically significant (boot strap estimate = \(-0.027\) , 95% CI = [ \(-.037, -.019\) ]), indicating that psychological distress significantly mediated the relationship between resilience and sleep quality. Therefore, hypothesis 3 was confirmed. Moreover, the indirect effect of psychological resilience on sleep quality, via perceived social support and psychological distress serially (boot strap estimate = \(-0.014\) , 95% CI = [ \(-.023, -.008\) ]), suggested that perceived social support and psychological distress played a chain mediating role in the relationship between psychological resilience and sleep quality. Therefore, hypothesis 4 was supported, confirming the establishment of chain mediation.

The main goal of this research was to explore how resilience and sleep quality are linked in Chinese medical staff. We focused on how the social support they feel and their psychological issues (such as depression, anxiety, and stress) might play a role in this connection. Guided by the biopsychosocial model, we constructed a chain mediation model to dissect the indirect effects of perceived social support and psychological distress on the aforementioned relationship. The results of this study demonstrated that psychological resilience not only directly impacts the sleep quality of medical staff but also exerts an indirect influence via the sequential mediating effects of perceived social support and psychological distress. Our study is the first to examine perceived social support and psychological issues as possible reasons for the link between resilience and sleep quality among medical staff.

Firstly, the results in this study showed that psychological resilience was negatively associated with PSQI scores, indicating that higher levels of psychological resilience could significantly improve the quality of sleep among Chinese medical staff. This finding supported hypothesis 1 and was consistent with previous research on medical workers 16 , 53 , which showed that psychological resilience played a protective role in sleep quality. According to the neuroscience theory of resilience 54 , psychological resilience is linked to brain regional structure, neural circuits, and brain neural networks, and brain function. These factors play a crucial role in emotional regulation, which indirectly affects sleep quality 7 . Furthermore, possessing high levels of psychological resilience could assist medical professionals in confronting occupational stressors and setbacks in a positive manner, effectively coping with adversity, reducing the negative impact of work-related high-stress events, and thus promoting the development of physical and mental health 55 , 56 . Therefore, psychological resilience may improve the quality of sleep among medical professionals by positively impacting their physical and mental health.

Secondly, the study found that perceived social support did not play a significant role in mediating the relationship between psychological resilience and sleep quality among medical staff. Therefore, hypothesis 2 was not supported. Specifically, this study indicated that perceived social support was not a significant predictor of sleep quality in the constructed mediation model. The results revealed that even with high levels of perceived social support, resilient medical staff might still suffer from poor sleep quality. In fact, inconsistencies exist in the literature regarding whether perceived social support could predict sleep quality 57 . While the majority of previous studies showed that (perceived) social support was positively related to sleep quality 25 , 27 , 28 , a minority of studies indicated that there was no significant association between social support and sleep quality in different populations 29 , 57 , 58 . Notably, our study of Chinese healthcare professionals during the COVID-19 pandemic offers a distinct context that could help explain these disparities.The intense workload and stress faced by healthcare professionals, especially during the pandemic, are unprecedented. They often endures lengthy shifts under immense strain, which might negate any direct beneficial effects of social support on sleep quality. Despite robust social support, their substantial work demands and stress could impede the conversion of social support into better sleep.In such a demanding environment, the influence of social support may be overshadowed by immediate job requirements, diminishing its direct effect on sleep outcomes. Moreover, these discrepancies could be due to variations in samples, cultural backgrounds, and measurement tools. Therefore, further studies are strongly recommended.

Thirdly, in line with Hypothesis 3, this study found that psychological distress played a significant mediating role in the relationship between resilience and sleep quality among the medical staff. Specifically, psychological resilience was found to negatively predict psychological distress, which, in turn, positively predicted sleep quality. On one hand, the study found that higher levels of resilience in medical staff were associated with lower levels of psychological distress. This finding is partially consistent with previous research which has shown that resilience can act as a buffer against stress and adversity. For instance, a recent cross-sectional study involving 602 medical staff revealed that high levels of resilience contributed to lower levels of depression 59 . Furthermore, resilience has been identified as a promotable protective factor against anxiety and work-related stress in nursing staff 32 . Psychological researchers generally acknowledge that individuals with higher levels of resilience have access to more psychological resources, such as fortitude, optimism, and emotion regulation abilities 60 . Within this framework, resilient healthcare workers are more likely to possess stronger anti-stress capabilities and better social adaptability, enabling them to actively and positively deal with setbacks or diversity in life 34 . Such attributes not only reduce the impact of negative stress events on their well-being but also further promotes the development of their physical and mental health. Consequently, medical staff with higher levels of psychological resilience may have better mental health, whereas those with lower levels of psychological resilience may be more susceptible to emotional dysregulation following exposure to stressors or other negative emotions 61 . On the other hand, there was a significant positive correlation between psychological distress and lower sleep quality. That is, higher levels of psychological distress were associated with poorer sleep quality. The results are in line with previous studies 62 , 63 , which also found a strong association between more severe depressive symptoms and poor sleep quality in healthcare workers. One potential explanation is that depressive symptoms, such as diminished energy, inattention, and daytime drowsiness, could disrupt circadian rhythms and impair sleep quality 63 . In summary, our research demonstrates that psychological resilience can enhance sleep quality by diminishing psychological distress in healthcare workers, thereby contributing to the expanding body of knowledge on the emotional mechanisms underlying psychological resilience and sleep quality.

Finally, the study contributed to providing evidence for the chain mediating effect of perceived social support and psychological distress on the relationship between psychological resilience and sleep quality among healthcare professionals. On the one hand, the current study found that the psychological resilience could significantly predict perceived social support in medical staff. Although it has been well documented that a high level of perceived social support correlates with increased resilience 64 , 65 , 66 , this study indicates that resilience, in turn, can also predict perceived social support in the medical staff group, corroborating findings from studies involving other populations 24 , 42 . The rationale behind this may involve several factors. Medical staff with greater resilience are prone to express their thoughts and find sympathetic friends, which are salient factors for broadening their social networks 22 . This enables them to acquire and perceived sufficient support from people around them to cope with various stressors and obstacles encountered in their daily professional lives 29 . Furthermore, resilient medical staff tend to maintain a more positive outlook on their current circumstances and exhibit a more proactive approach towards seeking professional assistance 23 . Consequently, resilient medical staff might report higher levels of perceived social support compared to their less resilient counterparts, even if they have the same actual social support. These findings contribute new insights into the emotional mechanisms underlying the positive effect of resilience on perceived social support, particularly among medical staff group. On the other hand, this study found that perceived social support was significantly negatively associated with psychological distress among medical personnel, aligning with previous studies 33 , 67 . A compelling rationale for this result is that high levels of perceived social support can provide emotional comfort and warmth 24 , as well as bolster self-assurance in tackling adverse circumstances and stressful encounters, thereby mitigating the negative emotional experiences of medical staff 29 , 63 . Moreover, this finding also echoed the main effect model of social support, which argued that social support could beneficially influence mental health by alleviating anxiety and depression 68 . Therefore, perceived social support emerges as a pivotal element in shielding medical professionals from the deleterious effects of psychological distress.

In conclusion, while the direct mediating role of perceived social support between resilience and sleep quality in medical professionals was not significant, our analysis uncovered a more intricate relationship. We confirmed through our data an empirical validation of hypothesis 4, which proposed a three-path mediation mechanism: resilience enhances perceived social support, subsequently reducing psychological distress and consequently leading to improved sleep quality. Defining these pathways deepens our comprehension of how resilience engages with social support and emotions to influence sleep-related biological outcomes. This refined analysis is consistent with the Biopsychosocial Model, advancing a comprehensive grasp of the determinants of sleep quality and guiding the design of tailored interventions to address the diverse facets of well-being in healthcare professionals.

Limitations and implications

This study, while providing valuable insights, has several limitations. Firstly, the cross-sectional design restricts the ability to ascertain causal relationships among resilience, perceived social support, negative emotions, and sleep quality. Future research should employ experimental or longitudinal methodologies to establish causality. Secondly, the potential response bias and social desirability bias in online self-reported questionnaires cannot be completely eliminated. Future studies could benefit from incorporating objective measurements to mitigate these biases. Additionally, the generalizability of our findings may be constrained as the sample of medical staff was drawn from only one city in China using a convenience sampling method. It is important to consider that the cultural and operational environments of medical staff in different regions or countries may vary significantly. These differences may influence the relationships among resilience, perceived social support, negative emotions, and sleep quality. Therefore, caution should be exercised when generalizing these results to other populations and cultural contexts. Future studies are encouraged to replicate this study in diverse geographical areas and cultural settings to enhance the external validity of these findings. Finally, while this study explored the mediating roles of perceived social support and negative emotions, other potential mediators such as dimensions of professional quality of life (e.g., compassion satisfaction, burnout, secondary traumatic stress) and mindfulness should be considered in future studies.

From a practical standpoint, the study’s findings provide critical guidance for developing evidence-based interventions aimed at enhancing resilience and sleep quality among medical staff. To this end, several targeted interventions can be considered. Firstly, workshops and educational sessions on sleep hygiene can educate medical staff on adopting healthy sleep habits. These sessions could cover topics such as the importance of a consistent sleep schedule, creating a restful sleep environment, and managing pre-sleep anxiety. Secondly, evidence-based stress management programs, such as cognitive-behavioral therapy (CBT) and mindfulness-based stress reduction (MBSR), have been demonstrated to effectively help medical staff manage stress and improve sleep quality 69 . These programs should be integrated into staff training and development. Thirdly, programs designed to enhance resilience should include elements such as problem-solving training and emotional regulation skills. These interventions can assist medical staff in developing more effective coping mechanisms for workplace stressors, which may in turn improve sleep quality 32 . Additionally, fostering strong social support networks and positive relationships among colleagues is vital. Medical institutions should facilitate a supportive culture through team-building activities and create an environment where seeking help is encouraged and valued. Furthermore, organizational interventions should focus on creating a work environment that promotes open communication, provides adequate breaks, and manages workloads effectively to reduce stress levels. This includes ensuring that counseling services and psychotherapy are readily accessible to staff, helping them to address psychological distress and manage stress.

The current study revealed that higher levels of resilience were associated with enhanced sleep quality, as well as improved perceived social support and decreased negative emotional experiences among medical staff. Furthermore, the relationship between resilience and sleep quality was found to be partially mediated by psychological distress. Additionally, perceived social support and psychological distress were found to play a chain mediating role in the relationship between resilience and sleep quality. These insights suggest that comprehensive improvements in sleep quality among medical personnel require not only fostering greater resilience but also improving perceived social support and alleviating psychological distress.

Data availability

Due to privacy, the datasets from this study are not publicly available but can be requested from the corresponding author.

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Acknowledgements

This work was supported by the Planning Subject for the 13th Five Year Plan of Guangdong Province Education Sciences (Grant No. 2018GXJK173).

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Nannan Wu & Yaoyao Cai

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Institute of Developmental and Educational Psychology, School of Marxism, Wuhan University, Wuhan, 430072, China

Ronghua Zhang

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N.W. and B.A. conceived and designed the the research. N.W. and F.D. carried out the protocol and the questionnaire survey. F.D., and Y.C. analyzed the data. N.W. and F.D. wrote the manuscript. B.A. and R.Z. revised the manuscript. Y.C. and B.A. controlled the quality of the whole article. All authors have reviewed and agreed to the published version of the manuscript.

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Wu, N., Ding, F., Ai, B. et al. Mediation effect of perceived social support and psychological distress between psychological resilience and sleep quality among Chinese medical staff. Sci Rep 14 , 19674 (2024). https://doi.org/10.1038/s41598-024-70754-3

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DOI : https://doi.org/10.1038/s41598-024-70754-3

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August 20, 2024

Trump’s ‘Gish Gallop’ Debate Tactic Comes from Creationists

A dishonest creationist debating tactic shouldn’t go unchallenged in American life. Or in national politics

By Madhusudan Katti

Moor Studio/Getty Images

June’s fateful Biden vs. Trump debate led not just to the sudden ascension of Vice President Kamala Harris as the presumptive Democratic presidential nominee. Donald Trump’s performance also saw the return of a familiar tactic in American public discourse, the “ Gish gallop ”—an avalanche of nonsense presented as fact—on the debate stage. A favorite of creationists, the gallop’s trot into the political arena needs calling out as we head into the home stretch of the 2024 election.

Coined by the National Center for Science Education’s founding director Eugenie Scott in 1994, the Gish gallop takes its name from the creationist Duane Gish , who frequently challenged biologists to debates about evolution. His tactic consisted of talking fast and with confidence, bombarding opponents with falsehoods, non-sequiturs and enough cherry-picked factoids to confuse the audience. Scientists debating him faced the challenge of sifting half-truths from outright lies and finding the right evidence to refute them systematically , all within the few minutes allowed in response. Which effectively meant that when the bell went off, the Gish gallop left the scientist “stumped” and Gish declaring victory for creationism . Such a spectacle leaves the audience less informed than they were before the debate, all at the hands of a debater whose only goal is to discredit their opponent and “win” the debate.

The migration of the Gish gallop from creationist’s patter onto the presidential debate stage , and increasingly onto news opinion pages nationwide, exemplifies a dangerous debasement of honest dialogue in American life. That both the public and its leaders pass over , or applaud , this kind of dishonesty on the highest political stage shows how integrity has taken a back seat to “winning” power in politics, business and the so-called “ culture wars ,” and now shrouding us in a fog of disinformation.

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The notion of “scientific debate” misrepresents how science actually works. Scientific conclusions come only through painstaking collection of data to test hypotheses over and over until the evidence is reviewed by other scientists and becomes irrefutable enough to solidify expert opinion. Anything that might be characterized as a “debate” in science thus occurs in the pages of peer-reviewed journals and can last for years or even decades or centuries. You can’t sell tickets to this contest. Participation requires informed engagement with the scientific literature. Which is rather different from what Americans grow up seeing in their high-school debate clubs or what plays out on their television screens between candidates vying for their votes. The best advice for scientists, honed after years of fighting creationist and climate-denial drivel, is to eschew fake debates on stages as simply lending megaphones to liars.

In politics , the Gish gallop is precisely what Trump deployed in the June debate, putting Joe Biden at a significant disadvantage in attempting to refute even some of the falsehoods in the torrent of lies. Trump, as anyone even half paying attention to his speeches knows, is an expert at the Gish gallop . He projects the utmost confidence while bombastically repeating lie after lie until it overwhelms his audience into either accepting every word he says as the truth, or walking off in disgust because there is no way to get him to acknowledge any truth. At this point, everyone knows this so well that news reports scarcely cover it .

But wait, doesn’t that suggest his past debate performances helped him win the nomination and get elected? Not necessarily, because polling data showed no measurable effect a few weeks after the recent debate, as has been the trend historically . His previous electoral wins came from a variety of other factors that had little to do with the debates. Indeed his hulking performance stalking Hillary Clinton in 2016 on stage made him look unstable.

Now that Biden has withdrawn from the race, the next debate, scheduled for September 10 , will likely feature Harris, the presumptive Democratic nominee, who better be prepared to counter Trump’s Gish gallop more forcefully. She will face a well-practiced con artist and loud dissembler who will flood the zone with enough falsehoods to outshout the former prosecutor and senator. (Speaking as an evolutionary scientist, there are no prizes for guessing which side of the evolution-creation debate these two candidates fall on, either.) When it’s her turn to respond, Harris should turn the tables on Trump by calling him out as a liar without bothering to refute each lie and refocus the audience on her own message. When asked how she might respond if Trump started stalking her on stage, Harris said she would turn around and ask “Why are you being so weird?” Indeed, her campaign has already leaned into this strategy to highlight and mock just how extreme the Republican agenda has become. It just might see her win the next debate as well.

Veterans of the evolution wars have been alarmed at how some of the figures driving the antiscience and anti-intellectual agenda of the modern Republican Party emerged from the creationist movement. A prime example is Manhattan Institute’ Christopher Rufo , who rose from Seattle’s Discovery Institute (birthplace of “intelligent design”) to become a leading conservative intellectual; his attacks on universities have taken on dangerous proportions , linked to attacks on academic freedom in states like Florida. Such mastery of the Gish gallop manifests not just on the debate stage these days, but in the op-ed pages of major newspapers falsely demonizing “critical race theory,” decrying DEI and getting prominent university presidents fired . Rufo and like-minded advocates know how to flood the zone with a steady barrage of disinformation until, as the philosopher and Holocaust survivor Hannah Arendt noted , “people no longer can believe anything”, losing their “capacity to act” or “to think and to judge”, and “with such a people, you can then do what you please.”

With help from a political press hungry for spectacle and trained to normalize dishonesty , Trump might once again Gish gallop his way into the White House. That is not a healthy prospect for American science.

This is an opinion and analysis article, and the views expressed by the author or authors are not necessarily those of Scientific American.