setting up an experiment hypothesis

The Scientific Method Tutorial

The scientific method, steps in the scientific method.

There is a great deal of variation in the specific techniques scientists use explore the natural world. However, the following steps characterize the majority of scientific investigations:

Step 1: Make observations Step 2: Propose a hypothesis to explain observations Step 3: Test the hypothesis with further observations or experiments Step 4: Analyze data Step 5: State conclusions about hypothesis based on data analysis

Each of these steps is explained briefly below, and in more detail later in this section.

Step 1: Make observations

A scientific inquiry typically starts with observations. Often, simple observations will trigger a question in the researcher's mind.

Example: A biologist frequently sees monarch caterpillars feeding on milkweed plants, but rarely sees them feeding on other types of plants. She wonders if it is because the caterpillars prefer milkweed over other food choices.

Step 2: Propose a hypothesis

The researcher develops a hypothesis (singular) or hypotheses (plural) to explain these observations. A hypothesis is a tentative explanation of a phenomenon or observation(s) that can be supported or falsified by further observations or experimentation.

Example: The researcher hypothesizes that monarch caterpillars prefer to feed on milkweed compared to other common plants. (Notice how the hypothesis is a statement, not a question as in step 1.)

Step 3: Test the hypothesis

The researcher makes further observations and/or may design an experiment to test the hypothesis. An experiment is a controlled situation created by a researcher to test the validity of a hypothesis. Whether further observations or an experiment is used to test the hypothesis will depend on the nature of the question and the practicality of manipulating the factors involved.

Example: The researcher sets up an experiment in the lab in which a number of monarch caterpillars are given a choice between milkweed and a number of other common plants to feed on.

Step 4: Analyze data

The researcher summarizes and analyzes the information, or data, generated by these further observations or experiments.

Example: In her experiment, milkweed was chosen by caterpillars 9 times out of 10 over all other plant selections.

Step 5: State conclusions

The researcher interprets the results of experiments or observations and forms conclusions about the meaning of these results. These conclusions are generally expressed as probability statements about their hypothesis.

Example: She concludes that when given a choice, 90 percent of monarch caterpillars prefer to feed on milkweed over other common plants.

Often, the results of one scientific study will raise questions that may be addressed in subsequent research. For example, the above study might lead the researcher to wonder why monarchs seem to prefer to feed on milkweed, and she may plan additional experiments to explore this question. For example, perhaps the milkweed has higher nutritional value than other available plants.

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The Scientific Method Flowchart

The steps in the scientific method are presented visually in the following flow chart. The question raised or the results obtained at each step directly determine how the next step will proceed. Following the flow of the arrows, pass the cursor over each blue box. An explanation and example of each step will appear. As you read the example given at each step, see if you can predict what the next step will be.

Activity: Apply the Scientific Method to Everyday Life Use the steps of the scientific method described above to solve a problem in real life. Suppose you come home one evening and flick the light switch only to find that the light doesn’t turn on. What is your hypothesis? How will you test that hypothesis? Based on the result of this test, what are your conclusions? Follow your instructor's directions for submitting your response.

The above flowchart illustrates the logical sequence of conclusions and decisions in a typical scientific study. There are some important points to note about this process:

1. The steps are clearly linked.

The steps in this process are clearly linked. The hypothesis, formed as a potential explanation for the initial observations, becomes the focus of the study. The hypothesis will determine what further observations are needed or what type of experiment should be done to test its validity. The conclusions of the experiment or further observations will either be in agreement with or will contradict the hypothesis. If the results are in agreement with the hypothesis, this does not prove that the hypothesis is true! In scientific terms, it "lends support" to the hypothesis, which will be tested again and again under a variety of circumstances before researchers accept it as a fairly reliable description of reality.

2. The same steps are not followed in all types of research.

The steps described above present a generalized method followed in a many scientific investigations. These steps are not carved in stone. The question the researcher wishes to answer will influence the steps in the method and how they will be carried out. For example, astronomers do not perform many experiments as defined here. They tend to rely on observations to test theories. Biologists and chemists have the ability to change conditions in a test tube and then observe whether the outcome supports or invalidates their starting hypothesis, while astronomers are not able to change the path of Jupiter around the Sun and observe the outcome!

3. Collected observations may lead to the development of theories.

When a large number of observations and/or experimental results have been compiled, and all are consistent with a generalized description of how some element of nature operates, this description is called a theory. Theories are much broader than hypotheses and are supported by a wide range of evidence. Theories are important scientific tools. They provide a context for interpretation of new observations and also suggest experiments to test their own validity. Theories are discussed in more detail in another section.

The Scientific Method in Detail

In the sections that follow, each step in the scientific method is described in more detail.

Step 1: Observations

Observations in science.

An observation is some thing, event, or phenomenon that is noticed or observed. Observations are listed as the first step in the scientific method because they often provide a starting point, a source of questions a researcher may ask. For example, the observation that leaves change color in the fall may lead a researcher to ask why this is so, and to propose a hypothesis to explain this phenomena. In fact, observations also will provide the key to answering the research question.

In science, observations form the foundation of all hypotheses, experiments, and theories. In an experiment, the researcher carefully plans what observations will be made and how they will be recorded. To be accepted, scientific conclusions and theories must be supported by all available observations. If new observations are made which seem to contradict an established theory, that theory will be re-examined and may be revised to explain the new facts. Observations are the nuts and bolts of science that researchers use to piece together a better understanding of nature.

Observations in science are made in a way that can be precisely communicated to (and verified by) other researchers. In many types of studies (especially in chemistry, physics, and biology), quantitative observations are used. A quantitative observation is one that is expressed and recorded as a quantity, using some standard system of measurement. Quantities such as size, volume, weight, time, distance, or a host of others may be measured in scientific studies.

Some observations that researchers need to make may be difficult or impossible to quantify. Take the example of color. Not all individuals perceive color in exactly the same way. Even apart from limiting conditions such as colorblindness, the way two people see and describe the color of a particular flower, for example, will not be the same. Color, as perceived by the human eye, is an example of a qualitative observation.

Qualitative observations note qualities associated with subjects or samples that are not readily measured. Other examples of qualitative observations might be descriptions of mating behaviors, human facial expressions, or "yes/no" type of data, where some factor is present or absent. Though the qualities of an object may be more difficult to describe or measure than any quantities associated with it, every attempt is made to minimize the effects of the subjective perceptions of the researcher in the process. Some types of studies, such as those in the social and behavioral sciences (which deal with highly variable human subjects), may rely heavily on qualitative observations.

Question: Why are observations important to science?

Limits of Observations

Because all observations rely to some degree on the senses (eyes, ears, or steady hand) of the researcher, complete objectivity is impossible. Our human perceptions are limited by the physical abilities of our sense organs and are interpreted according to our understanding of how the world works, which can be influenced by culture, experience, or education. According to science education specialist, George F. Kneller, "Surprising as it may seem, there is no fact that is not colored by our preconceptions" ("A Method of Enquiry," from Science and Its Ways of Knowing [Upper Saddle River: Prentice-Hall Inc., 1997], 15).

Observations made by a scientist are also limited by the sensitivity of whatever equipment he is using. Research findings will be limited at times by the available technology. For example, Italian physicist and philosopher Galileo Galilei (1564–1642) was reportedly the first person to observe the heavens with a telescope. Imagine how it must have felt to him to see the heavens through this amazing new instrument! It opened a window to the stars and planets and allowed new observations undreamed of before.

In the centuries since Galileo, increasingly more powerful telescopes have been devised that dwarf the power of that first device. In the past decade, we have marveled at images from deep space , courtesy of the Hubble Space Telescope, a large telescope that orbits Earth. Because of its view from outside the distorting effects of the atmosphere, the Hubble can look 50 times farther into space than the best earth-bound telescopes, and resolve details a tenth of the size (Seeds, Michael A., Horizons: Exploring the Universe , 5 th ed. [Belmont: Wadsworth Publishing Company, 1998], 86-87).

Construction is underway on a new radio telescope that scientists say will be able to detect electromagnetic waves from the very edges of the universe! This joint U.S.-Mexican project may allow us to ask questions about the origins of the universe and the beginnings of time that we could never have hoped to answer before. Completion of the new telescope is expected by the end of 2001.

Although the amount of detail observed by Galileo and today's astronomers is vastly different, the stars and their relationships have not changed very much. Yet with each technological advance, the level of detail of observation has been increased, and with it, the power to answer more and more challenging questions with greater precision.

Question: What are some of the differences between a casual observation and a 'scientific observation'?

Step 2: The Hypothesis

A hypothesis is a statement created by the researcher as a potential explanation for an observation or phenomena. The hypothesis converts the researcher's original question into a statement that can be used to make predictions about what should be observed if the hypothesis is true. For example, given the hypothesis, "exposure to ultraviolet (UV) radiation increases the risk of skin cancer," one would predict higher rates of skin cancer among people with greater UV exposure. These predictions could be tested by comparing skin cancer rates among individuals with varying amounts of UV exposure. Note how the hypothesis itself determines what experiments or further observations should be made to test its validity. Results of tests are then compared to predictions from the hypothesis, and conclusions are stated in terms of whether or not the data supports the hypothesis. So the hypothesis serves a guide to the full process of scientific inquiry.

The Qualities of a Good Hypothesis

• A hypothesis must be testable or provide predictions that are testable. It can potentially be shown to be false by further observations or experimentation.
• A hypothesis should be specific. If it is too general it cannot be tested, or tests will have so many variables that the results will be complicated and difficult to interpret. A well-written hypothesis is so specific it actually determines how the experiment should be set up.
• A hypothesis should not include any untested assumptions if they can be avoided. The hypothesis itself may be an assumption that is being tested, but it should be phrased in a way that does not include assumptions that are not tested in the experiment.
• It is okay (and sometimes a good idea) to develop more than one hypothesis to explain a set of observations. Competing hypotheses can often be tested side-by-side in the same experiment.

Question: Why is the hypothesis important to the scientific method?

Step 3: Testing the Hypothesis

A hypothesis may be tested in one of two ways: by making additional observations of a natural situation, or by setting up an experiment. In either case, the hypothesis is used to make predictions, and the observations or experimental data collected are examined to determine if they are consistent or inconsistent with those predictions. Hypothesis testing, especially through experimentation, is at the core of the scientific process. It is how scientists gain a better understanding of how things work.

Testing a Hypothesis by Observation

Some hypotheses may be tested through simple observation. For example, a researcher may formulate the hypothesis that the sun always rises in the east. What might an alternative hypothesis be? If his hypothesis is correct, he would predict that the sun will rise in the east tomorrow. He can easily test such a prediction by rising before dawn and going out to observe the sunrise. If the sun rises in the west, he will have disproved the hypothesis. He will have shown that it does not hold true in every situation. However, if he observes on that morning that the sun does in fact rise in the east, he has not proven the hypothesis. He has made a single observation that is consistent with, or supports, the hypothesis. As a scientist, to confidently state that the sun will always rise in the east, he will want to make many observations, under a variety of circumstances. Note that in this instance no manipulation of circumstance is required to test the hypothesis (i.e., you aren't altering the sun in any way).

Testing a Hypothesis by Experimentation

An experiment is a controlled series of observations designed to test a specific hypothesis. In an experiment, the researcher manipulates factors related to the hypothesis in such a way that the effect of these factors on the observations (data) can be readily measured and compared. Most experiments are an attempt to define a cause-and-effect relationship between two factors or events—to explain why something happens. For example, with the hypothesis "roses planted in sunny areas bloom earlier than those grown in shady areas," the experiment would be testing a cause-and-effect relationship between sunlight and time of blooming.

A major advantage of setting up an experiment versus making observations of what is already available is that it allows the researcher to control all the factors or events related to the hypothesis, so that the true cause of an event can be more easily isolated. In all cases, the hypothesis itself will determine the way the experiment will be set up. For example, suppose my hypothesis is "the weight of an object is proportional to the amount of time it takes to fall a certain distance." How would you test this hypothesis?

The Qualities of a Good Experiment

• The experiment must be conducted on a group of subjects that are narrowly defined and have certain aspects in common. This is the group to which any conclusions must later be confined. (Examples of possible subjects: female cancer patients over age 40, E. coli bacteria, red giant stars, the nicotine molecule and its derivatives.)
• All subjects of the experiment should be (ideally) completely alike in all ways except for the factor or factors that are being tested. Factors that are compared in scientific experiments are called variables. A variable is some aspect of a subject or event that may differ over time or from one group of subjects to another. For example, if a biologist wanted to test the effect of nitrogen on grass growth, he would apply different amounts of nitrogen fertilizer to several plots of grass. The grass in each of the plots should be as alike as possible so that any difference in growth could be attributed to the effect of the nitrogen. For example, all the grass should be of the same species, planted at the same time and at the same density, receive the same amount of water and sunlight, and so on. The variable in this case would be the amount of nitrogen applied to the plants. The researcher would not compare differing amounts of nitrogen across different grass species to determine the effect of nitrogen on grass growth. What is the problem with using different species of plants to compare the effect of nitrogen on plant growth? There are different kinds of variables in an experiment. A factor that the experimenter controls, and changes intentionally to determine if it has an effect, is called an independent variable . A factor that is recorded as data in the experiment, and which is compared across different groups of subjects, is called a dependent variable . In many cases, the value of the dependent variable will be influenced by the value of an independent variable. The goal of the experiment is to determine a cause-and-effect relationship between independent and dependent variables—in this case, an effect of nitrogen on plant growth. In the nitrogen/grass experiment, (1) which factor was the independent variable? (2) Which factor was the dependent variable?
• Nearly all types of experiments require a control group and an experimental group. The control group generally is not changed in any way, but remains in a "natural state," while the experimental group is modified in some way to examine the effect of the variable which of interest to the researcher. The control group provides a standard of comparison for the experimental groups. For example, in new drug trials, some patients are given a placebo while others are given doses of the drug being tested. The placebo serves as a control by showing the effect of no drug treatment on the patients. In research terminology, the experimental groups are often referred to as treatments , since each group is treated differently. In the experimental test of the effect of nitrogen on grass growth, what is the control group? In the example of the nitrogen experiment, what is the purpose of a control group?
• In research studies a great deal of emphasis is placed on repetition. It is essential that an experiment or study include enough subjects or enough observations for the researcher to make valid conclusions. The two main reasons why repetition is important in scientific studies are (1) variation among subjects or samples and (2) measurement error.

Variation among Subjects

There is a great deal of variation in nature. In a group of experimental subjects, much of this variation may have little to do with the variables being studied, but could still affect the outcome of the experiment in unpredicted ways. For example, in an experiment designed to test the effects of alcohol dose levels on reflex time in 18- to 22-year-old males, there would be significant variation among individual responses to various doses of alcohol. Some of this variation might be due to differences in genetic make-up, to varying levels of previous alcohol use, or any number of factors unknown to the researcher.

Because what the researcher wants to discover is average dose level effects for this group, he must run the test on a number of different subjects. Suppose he performed the test on only 10 individuals. Do you think the average response calculated would be the same as the average response of all 18- to 22-year-old males? What if he tests 100 individuals, or 1,000? Do you think the average he comes up with would be the same in each case? Chances are it would not be. So which average would you predict would be most representative of all 18- to 22-year-old males?

A basic rule of statistics is, the more observations you make, the closer the average of those observations will be to the average for the whole population you are interested in. This is because factors that vary among a population tend to occur most commonly in the middle range, and least commonly at the two extremes. Take human height for example. Although you may find a man who is 7 feet tall, or one who is 4 feet tall, most men will fall somewhere between 5 and 6 feet in height. The more men we measure to determine average male height, the less effect those uncommon extreme (tall or short) individuals will tend to impact the average. Thus, one reason why repetition is so important in experiments is that it helps to assure that the conclusions made will be valid not only for the individuals tested, but also for the greater population those individuals represent.

"The use of a sample (or subset) of a population, an event, or some other aspect of nature for an experimental group that is not large enough to be representative of the whole" is called sampling error (Starr, Cecie, Biology: Concepts and Applications , 4 th ed. [Pacific Cove: Brooks/Cole, 2000], glossary). If too few samples or subjects are used in an experiment, the researcher may draw incorrect conclusions about the population those samples or subjects represent.

Use the jellybean activity below to see a simple demonstration of samping error.

Directions: There are 400 jellybeans in the jar. If you could not see the jar and you initially chose 1 green jellybean from the jar, you might assume the jar only contains green jelly beans. The jar actually contains both green and black jellybeans. Use the "pick 1, 5, or 10" buttons to create your samples. For example, use the "pick" buttons now to create samples of 2, 13, and 27 jellybeans. After you take each sample, try to predict the ratio of green to black jellybeans in the jar. How does your prediction of the ratio of green to black jellybeans change as your sample changes?

Measurement Error

The second reason why repetition is necessary in research studies has to do with measurement error. Measurement error may be the fault of the researcher, a slight difference in measuring techniques among one or more technicians, or the result of limitations or glitches in measuring equipment. Even the most careful researcher or the best state-of-the-art equipment will make some mistakes in measuring or recording data. Another way of looking at this is to say that, in any study, some measurements will be more accurate than others will. If the researcher is conscientious and the equipment is good, the majority of measurements will be highly accurate, some will be somewhat inaccurate, and a few may be considerably inaccurate. In this case, the same reasoning used above also applies here: the more measurements taken, the less effect a few inaccurate measurements will have on the overall average.

Step 4: Data Analysis

In any experiment, observations are made, and often, measurements are taken. Measurements and observations recorded in an experiment are referred to as data . The data collected must relate to the hypothesis being tested. Any differences between experimental and control groups must be expressed in some way (often quantitatively) so that the groups may be compared. Graphs and charts are often used to visualize the data and to identify patterns and relationships among the variables.

Statistics is the branch of mathematics that deals with interpretation of data. Data analysis refers to statistical methods of determining whether any differences between the control group and experimental groups are too great to be attributed to chance alone. Although a discussion of statistical methods is beyond the scope of this tutorial, the data analysis step is crucial because it provides a somewhat standardized means for interpreting data. The statistical methods of data analysis used, and the results of those analyses, are always included in the publication of scientific research. This convention limits the subjective aspects of data interpretation and allows scientists to scrutinize the working methods of their peers.

Why is data analysis an important step in the scientific method?

Step 5: Stating Conclusions

The conclusions made in a scientific experiment are particularly important. Often, the conclusion is the only part of a study that gets communicated to the general public. As such, it must be a statement of reality, based upon the results of the experiment. To assure that this is the case, the conclusions made in an experiment must (1) relate back to the hypothesis being tested, (2) be limited to the population under study, and (3) be stated as probabilities.

The hypothesis that is being tested will be compared to the data collected in the experiment. If the experimental results contradict the hypothesis, it is rejected and further testing of that hypothesis under those conditions is not necessary. However, if the hypothesis is not shown to be wrong, that does not conclusively prove that it is right! In scientific terms, the hypothesis is said to be "supported by the data." Further testing will be done to see if the hypothesis is supported under a number of trials and under different conditions.

If the hypothesis holds up to extensive testing then the temptation is to claim that it is correct. However, keep in mind that the number of experiments and observations made will only represent a subset of all the situations in which the hypothesis may potentially be tested. In other words, experimental data will only show part of the picture. There is always the possibility that a further experiment may show the hypothesis to be wrong in some situations. Also, note that the limits of current knowledge and available technologies may prevent a researcher from devising an experiment that would disprove a particular hypothesis.

The researcher must be sure to limit his or her conclusions to apply only to the subjects tested in the study. If a particular species of fish is shown to consume their young 90 percent of the time when raised in captivity, that doesn't necessarily mean that all fish will do so, or that this fish's behavior would be the same in its native habitat.

Finally, the conclusions of the experiment are generally stated as probabilities. A careful scientist would never say, "drug x kills cancer cells;" she would more likely say, "drug x was shown to destroy 85 percent of cancerous skin cells in rats in lab trials." Notice how very different these two statements are. There is a tendency in the media and in the general public to gravitate toward the first statement. This makes a terrific headline and is also easy to interpret; it is absolute. Remember though, in science conclusions must be confined to the population under study; broad generalizations should be avoided. The second statement is sound science. There is data to back it up. Later studies may reveal a more universal effect of the drug on cancerous cells, or they may not. Most researchers would be unwilling to stake their reputations on the first statement.

As a student, you should read and interpret popular press articles about research studies very carefully. From the text, can you determine how the experiment was set up and what variables were measured? Are the observations and data collected appropriate to the hypothesis being tested? Are the conclusions supported by the data? Are the conclusions worded in a scientific context (as probability statements) or are they generalized for dramatic effect? In any researched-based assignment, it is a good idea to refer to the original publication of a study (usually found in professional journals) and to interpret the facts for yourself.

Qualities of a Good Experiment

• narrowly defined subjects
• all subjects treated alike except for the factor or variable being studied
• a control group is used for comparison
• measurements related to the factors being studied are carefully recorded
• enough samples or subjects are used so that conclusions are valid for the population of interest
• conclusions made relate back to the hypothesis, are limited to the population being studied, and are stated in terms of probabilities

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• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

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

Table of contents

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

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

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

Variables in hypotheses

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

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

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Step 1: ask a question.

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

Step 2: Do some preliminary research

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

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

Step 3: Formulate your hypothesis

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

Step 4: Refine your hypothesis

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

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

Step 5: Phrase your hypothesis in three ways

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

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

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

Step 6. Write a null hypothesis

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

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

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

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

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

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McCombes, S. (2022, May 06). How to Write a Strong Hypothesis | Guide & Examples. Scribbr. Retrieved 31 May 2024, from https://www.scribbr.co.uk/research-methods/hypothesis-writing/

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4.14: Experiments and Hypotheses

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

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

Forming a Hypothesis

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

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

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

Practice Questions

Determine whether each following statement is a scientific hypothesis.

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

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

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

[/hidden-answer]

Testing a Vaccine

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

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

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

Practice Question

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

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

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

Experimental Design

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

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

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

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

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

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

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

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

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

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

Experimental Variables

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

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

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

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

What is the independent variable in this experiment?

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

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

List three control variables other than age.

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

What is the dependent variable in this experiment?

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

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

Contributors and Attributions

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

Module 1: Introduction to Biology

Experiments and hypotheses, learning outcomes.

• Form a hypothesis and use it to design a scientific experiment

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

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

Forming a Hypothesis

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

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

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

Practice Questions

Determine whether each following statement is a scientific hypothesis.

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

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

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

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

Testing a Vaccine

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

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

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

Practice Question

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

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

Experimental Design

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

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

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

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

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

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

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

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

Experimental Variables

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

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

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

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

What is the independent variable in this experiment?

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

List three control variables other than age.

What is the dependent variable in this experiment?

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

MA121: Introduction to Statistics

Setting Up Hypotheses

Introduction, learning objectives.

• Describe the logic by which it can be concluded that someone can distinguish between two things
• State whether random assignment ensures that all uncontrolled sources of variation will be equal
• Define precisely what the probability is that is computed to reach the conclusion that a difference is not due to chance
• Distinguish between the probability of an event and the probability of a state of the world
• Define "null hypothesis"
• Be able to determine the null hypothesis from a description of an experiment
• Define "alternative hypothesis"

The statistician R. Fisher explained the concept of hypothesis testing with a story of a lady tasting tea. Here we will present an example based on James Bond who insisted that martinis should be shaken rather than stirred. Let's consider a hypothetical experiment to determine whether Mr. Bond can tell the difference between a shaken and a stirred martini. Suppose we gave Mr. Bond a series of 16 taste tests. In each test, we flipped a fair coin to determine whether to stir or shake the martini. Then we presented the martini to Mr. Bond and asked him to decide whether it was shaken or stirred. Let's say Mr. Bond was correct on 13 of the 16 taste tests. Does this prove that Mr. Bond has at least some ability to tell whether the martini was shaken or stirred?

This result does not prove that he does; it could be he was just lucky and guessed right 13 out of 16 times. But how plausible is the explanation that he was just lucky? To assess its plausibility, we determine the probability that someone who was just guessing would be correct 13/16 times or more. This probability can be computed from the binomial distribution, and the binomial distribution calculator shows it to be 0.0106. This is a pretty low probability, and therefore someone would have to be very lucky to be correct 13 or more times out of 16 if they were just guessing. So either Mr. Bond was very lucky, or he can tell whether the drink was shaken or stirred. The hypothesis that he was guessing is not proven false, but considerable doubt is cast on it. Therefore, there is strong evidence that Mr. Bond can tell whether a drink was shaken or stirred.

Let's consider another example. The case study Physicians' Reactions sought to determine whether physicians spend less time with obese patients. Physicians were sampled randomly and each was shown a chart of a patient complaining of a migraine headache. They were then asked to estimate how long they would spend with the patient. The charts were identical except that for half the charts, the patient was obese and for the other half, the patient was of average weight. The chart a particular physician viewed was determined randomly. Thirty-three physicians viewed charts of average-weight patients and 38 physicians viewed charts of obese patients.

The mean time physicians reported that they would spend with obese patients was 24.7 minutes as compared to a mean of 31.4 minutes for average-weight patients. How might this difference between means have occurred? One possibility is that physicians were influenced by the weight of the patients. On the other hand, perhaps by chance, the physicians who viewed charts of the obese patients tend to see patients for less time than the other physicians. Random assignment of charts does not ensure that the groups will be equal in all respects other than the chart they viewed. In fact, it is certain the two groups differed in many ways by chance. The two groups could not have exactly the same mean age (if measured precisely enough such as in days). Perhaps a physician's age affects how long physicians see patients. There are innumerable differences between the groups that could affect how long they view patients. With this in mind, is it plausible that these chance differences are responsible for the difference in times?

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10.1 - setting the hypotheses: examples.

A significance test examines whether the null hypothesis provides a plausible explanation of the data. The null hypothesis itself does not involve the data. It is a statement about a parameter (a numerical characteristic of the population). These population values might be proportions or means or differences between means or proportions or correlations or odds ratios or any other numerical summary of the population. The alternative hypothesis is typically the research hypothesis of interest. Here are some examples.

Example 10.2: Hypotheses with One Sample of One Categorical Variable Section  

About 10% of the human population is left-handed. Suppose a researcher at Penn State speculates that students in the College of Arts and Architecture are more likely to be left-handed than people found in the general population. We only have one sample since we will be comparing a population proportion based on a sample value to a known population value.

• Research Question : Are artists more likely to be left-handed than people found in the general population?
• Response Variable : Classification of the student as either right-handed or left-handed

State Null and Alternative Hypotheses

• Null Hypothesis : Students in the College of Arts and Architecture are no more likely to be left-handed than people in the general population (population percent of left-handed students in the College of Art and Architecture = 10% or p = .10).
• Alternative Hypothesis : Students in the College of Arts and Architecture are more likely to be left-handed than people in the general population (population percent of left-handed students in the College of Arts and Architecture > 10% or p > .10). This is a one-sided alternative hypothesis.

Example 10.3: Hypotheses with One Sample of One Measurement Variable Section  

A generic brand of the anti-histamine Diphenhydramine markets a capsule with a 50 milligram dose. The manufacturer is worried that the machine that fills the capsules has come out of calibration and is no longer creating capsules with the appropriate dosage.

• Research Question : Does the data suggest that the population mean dosage of this brand is different than 50 mg?
• Response Variable : dosage of the active ingredient found by a chemical assay.
• Null Hypothesis : On the average, the dosage sold under this brand is 50 mg (population mean dosage = 50 mg).
• Alternative Hypothesis : On the average, the dosage sold under this brand is not 50 mg (population mean dosage ≠ 50 mg). This is a two-sided alternative hypothesis.

Example 10.4: Hypotheses with Two Samples of One Categorical Variable Section  

Many people are starting to prefer vegetarian meals on a regular basis. Specifically, a researcher believes that females are more likely than males to eat vegetarian meals on a regular basis.

• Research Question : Does the data suggest that females are more likely than males to eat vegetarian meals on a regular basis?
• Response Variable : Classification of whether or not a person eats vegetarian meals on a regular basis
• Explanatory (Grouping) Variable: Sex
• Null Hypothesis : There is no sex effect regarding those who eat vegetarian meals on a regular basis (population percent of females who eat vegetarian meals on a regular basis = population percent of males who eat vegetarian meals on a regular basis or p females = p males ).
• Alternative Hypothesis : Females are more likely than males to eat vegetarian meals on a regular basis (population percent of females who eat vegetarian meals on a regular basis > population percent of males who eat vegetarian meals on a regular basis or p females > p males ). This is a one-sided alternative hypothesis.

Example 10.5: Hypotheses with Two Samples of One Measurement Variable Section  

Obesity is a major health problem today. Research is starting to show that people may be able to lose more weight on a low carbohydrate diet than on a low fat diet.

• Research Question : Does the data suggest that, on the average, people are able to lose more weight on a low carbohydrate diet than on a low fat diet?
• Response Variable : Weight loss (pounds)
• Explanatory (Grouping) Variable : Type of diet
• Null Hypothesis : There is no difference in the mean amount of weight loss when comparing a low carbohydrate diet with a low fat diet (population mean weight loss on a low carbohydrate diet = population mean weight loss on a low fat diet).
• Alternative Hypothesis : The mean weight loss should be greater for those on a low carbohydrate diet when compared with those on a low fat diet (population mean weight loss on a low carbohydrate diet > population mean weight loss on a low fat diet). This is a one-sided alternative hypothesis.

Example 10.6: Hypotheses about the relationship between Two Categorical Variables Section  

• Research Question : Do the odds of having a stroke increase if you inhale second hand smoke ? A case-control study of non-smoking stroke patients and controls of the same age and occupation are asked if someone in their household smokes.
• Variables : There are two different categorical variables (Stroke patient vs control and whether the subject lives in the same household as a smoker). Living with a smoker (or not) is the natural explanatory variable and having a stroke (or not) is the natural response variable in this situation.
• Null Hypothesis : There is no relationship between whether or not a person has a stroke and whether or not a person lives with a smoker (odds ratio between stroke and second-hand smoke situation is = 1).
• Alternative Hypothesis : There is a relationship between whether or not a person has a stroke and whether or not a person lives with a smoker (odds ratio between stroke and second-hand smoke situation is > 1). This is a one-tailed alternative.

This research question might also be addressed like example 11.4 by making the hypotheses about comparing the proportion of stroke patients that live with smokers to the proportion of controls that live with smokers.

Example 10.7: Hypotheses about the relationship between Two Measurement Variables Section  

• Research Question : A financial analyst believes there might be a positive association between the change in a stock's price and the amount of the stock purchased by non-management employees the previous day (stock trading by management being under "insider-trading" regulatory restrictions).
• Variables : Daily price change information (the response variable) and previous day stock purchases by non-management employees (explanatory variable). These are two different measurement variables.
• Null Hypothesis : The correlation between the daily stock price change (\$) and the daily stock purchases by non-management employees (\$) = 0.
• Alternative Hypothesis : The correlation between the daily stock price change (\$) and the daily stock purchases by non-management employees (\$) > 0. This is a one-sided alternative hypothesis.

Example 10.8: Hypotheses about comparing the relationship between Two Measurement Variables in Two Samples Section  

• Research Question : Is there a linear relationship between the amount of the bill (\$) at a restaurant and the tip (\$) that was left. Is the strength of this association different for family restaurants than for fine dining restaurants?
• Variables : There are two different measurement variables. The size of the tip would depend on the size of the bill so the amount of the bill would be the explanatory variable and the size of the tip would be the response variable.
• Null Hypothesis : The correlation between the amount of the bill (\$) at a restaurant and the tip (\$) that was left is the same at family restaurants as it is at fine dining restaurants.
• Alternative Hypothesis : The correlation between the amount of the bill (\$) at a restaurant and the tip (\$) that was left is the difference at family restaurants then it is at fine dining restaurants. This is a two-sided alternative hypothesis.

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

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

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

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

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

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

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

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

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

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

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

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

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

Elements of a Good Hypothesis

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

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

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

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

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

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

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

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

The Importance of Operational Definitions

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

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

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

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

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

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

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

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

Hypothesis Checklist

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

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

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

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

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

A few examples of simple hypotheses:

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

Examples of a complex hypothesis include:

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

Examples of a null hypothesis include:

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

Examples of an alternative hypothesis:

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

Collecting Data on Your Hypothesis

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

Descriptive Research Methods

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

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

Experimental Research Methods

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

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

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

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

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

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

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

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

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

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

Hypothesis Testing

Hypothesis testing is a tool for making statistical inferences about the population data. It is an analysis tool that tests assumptions and determines how likely something is within a given standard of accuracy. Hypothesis testing provides a way to verify whether the results of an experiment are valid.

A null hypothesis and an alternative hypothesis are set up before performing the hypothesis testing. This helps to arrive at a conclusion regarding the sample obtained from the population. In this article, we will learn more about hypothesis testing, its types, steps to perform the testing, and associated examples.

What is Hypothesis Testing in Statistics?

Hypothesis testing uses sample data from the population to draw useful conclusions regarding the population probability distribution . It tests an assumption made about the data using different types of hypothesis testing methodologies. The hypothesis testing results in either rejecting or not rejecting the null hypothesis.

Hypothesis Testing Definition

Hypothesis testing can be defined as a statistical tool that is used to identify if the results of an experiment are meaningful or not. It involves setting up a null hypothesis and an alternative hypothesis. These two hypotheses will always be mutually exclusive. This means that if the null hypothesis is true then the alternative hypothesis is false and vice versa. An example of hypothesis testing is setting up a test to check if a new medicine works on a disease in a more efficient manner.

Null Hypothesis

The null hypothesis is a concise mathematical statement that is used to indicate that there is no difference between two possibilities. In other words, there is no difference between certain characteristics of data. This hypothesis assumes that the outcomes of an experiment are based on chance alone. It is denoted as \(H_{0}\). Hypothesis testing is used to conclude if the null hypothesis can be rejected or not. Suppose an experiment is conducted to check if girls are shorter than boys at the age of 5. The null hypothesis will say that they are the same height.

Alternative Hypothesis

The alternative hypothesis is an alternative to the null hypothesis. It is used to show that the observations of an experiment are due to some real effect. It indicates that there is a statistical significance between two possible outcomes and can be denoted as \(H_{1}\) or \(H_{a}\). For the above-mentioned example, the alternative hypothesis would be that girls are shorter than boys at the age of 5.

Hypothesis Testing P Value

In hypothesis testing, the p value is used to indicate whether the results obtained after conducting a test are statistically significant or not. It also indicates the probability of making an error in rejecting or not rejecting the null hypothesis.This value is always a number between 0 and 1. The p value is compared to an alpha level, \(\alpha\) or significance level. The alpha level can be defined as the acceptable risk of incorrectly rejecting the null hypothesis. The alpha level is usually chosen between 1% to 5%.

Hypothesis Testing Critical region

All sets of values that lead to rejecting the null hypothesis lie in the critical region. Furthermore, the value that separates the critical region from the non-critical region is known as the critical value.

Hypothesis Testing Formula

Depending upon the type of data available and the size, different types of hypothesis testing are used to determine whether the null hypothesis can be rejected or not. The hypothesis testing formula for some important test statistics are given below:

• z = \(\frac{\overline{x}-\mu}{\frac{\sigma}{\sqrt{n}}}\). \(\overline{x}\) is the sample mean, \(\mu\) is the population mean, \(\sigma\) is the population standard deviation and n is the size of the sample.
• t = \(\frac{\overline{x}-\mu}{\frac{s}{\sqrt{n}}}\). s is the sample standard deviation.
• \(\chi ^{2} = \sum \frac{(O_{i}-E_{i})^{2}}{E_{i}}\). \(O_{i}\) is the observed value and \(E_{i}\) is the expected value.

We will learn more about these test statistics in the upcoming section.

Types of Hypothesis Testing

Selecting the correct test for performing hypothesis testing can be confusing. These tests are used to determine a test statistic on the basis of which the null hypothesis can either be rejected or not rejected. Some of the important tests used for hypothesis testing are given below.

Hypothesis Testing Z Test

A z test is a way of hypothesis testing that is used for a large sample size (n ≥ 30). It is used to determine whether there is a difference between the population mean and the sample mean when the population standard deviation is known. It can also be used to compare the mean of two samples. It is used to compute the z test statistic. The formulas are given as follows:

• One sample: z = \(\frac{\overline{x}-\mu}{\frac{\sigma}{\sqrt{n}}}\).
• Two samples: z = \(\frac{(\overline{x_{1}}-\overline{x_{2}})-(\mu_{1}-\mu_{2})}{\sqrt{\frac{\sigma_{1}^{2}}{n_{1}}+\frac{\sigma_{2}^{2}}{n_{2}}}}\).

Hypothesis Testing t Test

The t test is another method of hypothesis testing that is used for a small sample size (n < 30). It is also used to compare the sample mean and population mean. However, the population standard deviation is not known. Instead, the sample standard deviation is known. The mean of two samples can also be compared using the t test.

• One sample: t = \(\frac{\overline{x}-\mu}{\frac{s}{\sqrt{n}}}\).
• Two samples: t = \(\frac{(\overline{x_{1}}-\overline{x_{2}})-(\mu_{1}-\mu_{2})}{\sqrt{\frac{s_{1}^{2}}{n_{1}}+\frac{s_{2}^{2}}{n_{2}}}}\).

Hypothesis Testing Chi Square

The Chi square test is a hypothesis testing method that is used to check whether the variables in a population are independent or not. It is used when the test statistic is chi-squared distributed.

One Tailed Hypothesis Testing

One tailed hypothesis testing is done when the rejection region is only in one direction. It can also be known as directional hypothesis testing because the effects can be tested in one direction only. This type of testing is further classified into the right tailed test and left tailed test.

Right Tailed Hypothesis Testing

The right tail test is also known as the upper tail test. This test is used to check whether the population parameter is greater than some value. The null and alternative hypotheses for this test are given as follows:

\(H_{0}\): The population parameter is ≤ some value

\(H_{1}\): The population parameter is > some value.

If the test statistic has a greater value than the critical value then the null hypothesis is rejected

Left Tailed Hypothesis Testing

The left tail test is also known as the lower tail test. It is used to check whether the population parameter is less than some value. The hypotheses for this hypothesis testing can be written as follows:

\(H_{0}\): The population parameter is ≥ some value

\(H_{1}\): The population parameter is < some value.

The null hypothesis is rejected if the test statistic has a value lesser than the critical value.

Two Tailed Hypothesis Testing

In this hypothesis testing method, the critical region lies on both sides of the sampling distribution. It is also known as a non - directional hypothesis testing method. The two-tailed test is used when it needs to be determined if the population parameter is assumed to be different than some value. The hypotheses can be set up as follows:

\(H_{0}\): the population parameter = some value

\(H_{1}\): the population parameter ≠ some value

The null hypothesis is rejected if the test statistic has a value that is not equal to the critical value.

Hypothesis Testing Steps

Hypothesis testing can be easily performed in five simple steps. The most important step is to correctly set up the hypotheses and identify the right method for hypothesis testing. The basic steps to perform hypothesis testing are as follows:

• Step 1: Set up the null hypothesis by correctly identifying whether it is the left-tailed, right-tailed, or two-tailed hypothesis testing.
• Step 2: Set up the alternative hypothesis.
• Step 3: Choose the correct significance level, \(\alpha\), and find the critical value.
• Step 4: Calculate the correct test statistic (z, t or \(\chi\)) and p-value.
• Step 5: Compare the test statistic with the critical value or compare the p-value with \(\alpha\) to arrive at a conclusion. In other words, decide if the null hypothesis is to be rejected or not.

Hypothesis Testing Example

The best way to solve a problem on hypothesis testing is by applying the 5 steps mentioned in the previous section. Suppose a researcher claims that the mean average weight of men is greater than 100kgs with a standard deviation of 15kgs. 30 men are chosen with an average weight of 112.5 Kgs. Using hypothesis testing, check if there is enough evidence to support the researcher's claim. The confidence interval is given as 95%.

Step 1: This is an example of a right-tailed test. Set up the null hypothesis as \(H_{0}\): \(\mu\) = 100.

Step 2: The alternative hypothesis is given by \(H_{1}\): \(\mu\) > 100.

Step 3: As this is a one-tailed test, \(\alpha\) = 100% - 95% = 5%. This can be used to determine the critical value.

1 - \(\alpha\) = 1 - 0.05 = 0.95

0.95 gives the required area under the curve. Now using a normal distribution table, the area 0.95 is at z = 1.645. A similar process can be followed for a t-test. The only additional requirement is to calculate the degrees of freedom given by n - 1.

Step 4: Calculate the z test statistic. This is because the sample size is 30. Furthermore, the sample and population means are known along with the standard deviation.

z = \(\frac{\overline{x}-\mu}{\frac{\sigma}{\sqrt{n}}}\).

\(\mu\) = 100, \(\overline{x}\) = 112.5, n = 30, \(\sigma\) = 15

z = \(\frac{112.5-100}{\frac{15}{\sqrt{30}}}\) = 4.56

Step 5: Conclusion. As 4.56 > 1.645 thus, the null hypothesis can be rejected.

Hypothesis Testing and Confidence Intervals

Confidence intervals form an important part of hypothesis testing. This is because the alpha level can be determined from a given confidence interval. Suppose a confidence interval is given as 95%. Subtract the confidence interval from 100%. This gives 100 - 95 = 5% or 0.05. This is the alpha value of a one-tailed hypothesis testing. To obtain the alpha value for a two-tailed hypothesis testing, divide this value by 2. This gives 0.05 / 2 = 0.025.

Related Articles:

• Probability and Statistics
• Data Handling

Important Notes on Hypothesis Testing

• Hypothesis testing is a technique that is used to verify whether the results of an experiment are statistically significant.
• It involves the setting up of a null hypothesis and an alternate hypothesis.
• There are three types of tests that can be conducted under hypothesis testing - z test, t test, and chi square test.
• Hypothesis testing can be classified as right tail, left tail, and two tail tests.

Examples on Hypothesis Testing

• Example 1: The average weight of a dumbbell in a gym is 90lbs. However, a physical trainer believes that the average weight might be higher. A random sample of 5 dumbbells with an average weight of 110lbs and a standard deviation of 18lbs. Using hypothesis testing check if the physical trainer's claim can be supported for a 95% confidence level. Solution: As the sample size is lesser than 30, the t-test is used. \(H_{0}\): \(\mu\) = 90, \(H_{1}\): \(\mu\) > 90 \(\overline{x}\) = 110, \(\mu\) = 90, n = 5, s = 18. \(\alpha\) = 0.05 Using the t-distribution table, the critical value is 2.132 t = \(\frac{\overline{x}-\mu}{\frac{s}{\sqrt{n}}}\) t = 2.484 As 2.484 > 2.132, the null hypothesis is rejected. Answer: The average weight of the dumbbells may be greater than 90lbs
• Example 2: The average score on a test is 80 with a standard deviation of 10. With a new teaching curriculum introduced it is believed that this score will change. On random testing, the score of 38 students, the mean was found to be 88. With a 0.05 significance level, is there any evidence to support this claim? Solution: This is an example of two-tail hypothesis testing. The z test will be used. \(H_{0}\): \(\mu\) = 80, \(H_{1}\): \(\mu\) ≠ 80 \(\overline{x}\) = 88, \(\mu\) = 80, n = 36, \(\sigma\) = 10. \(\alpha\) = 0.05 / 2 = 0.025 The critical value using the normal distribution table is 1.96 z = \(\frac{\overline{x}-\mu}{\frac{\sigma}{\sqrt{n}}}\) z = \(\frac{88-80}{\frac{10}{\sqrt{36}}}\) = 4.8 As 4.8 > 1.96, the null hypothesis is rejected. Answer: There is a difference in the scores after the new curriculum was introduced.
• Example 3: The average score of a class is 90. However, a teacher believes that the average score might be lower. The scores of 6 students were randomly measured. The mean was 82 with a standard deviation of 18. With a 0.05 significance level use hypothesis testing to check if this claim is true. Solution: The t test will be used. \(H_{0}\): \(\mu\) = 90, \(H_{1}\): \(\mu\) < 90 \(\overline{x}\) = 110, \(\mu\) = 90, n = 6, s = 18 The critical value from the t table is -2.015 t = \(\frac{\overline{x}-\mu}{\frac{s}{\sqrt{n}}}\) t = \(\frac{82-90}{\frac{18}{\sqrt{6}}}\) t = -1.088 As -1.088 > -2.015, we fail to reject the null hypothesis. Answer: There is not enough evidence to support the claim.

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FAQs on Hypothesis Testing

What is hypothesis testing.

Hypothesis testing in statistics is a tool that is used to make inferences about the population data. It is also used to check if the results of an experiment are valid.

What is the z Test in Hypothesis Testing?

The z test in hypothesis testing is used to find the z test statistic for normally distributed data . The z test is used when the standard deviation of the population is known and the sample size is greater than or equal to 30.

What is the t Test in Hypothesis Testing?

The t test in hypothesis testing is used when the data follows a student t distribution . It is used when the sample size is less than 30 and standard deviation of the population is not known.

What is the formula for z test in Hypothesis Testing?

The formula for a one sample z test in hypothesis testing is z = \(\frac{\overline{x}-\mu}{\frac{\sigma}{\sqrt{n}}}\) and for two samples is z = \(\frac{(\overline{x_{1}}-\overline{x_{2}})-(\mu_{1}-\mu_{2})}{\sqrt{\frac{\sigma_{1}^{2}}{n_{1}}+\frac{\sigma_{2}^{2}}{n_{2}}}}\).

What is the p Value in Hypothesis Testing?

The p value helps to determine if the test results are statistically significant or not. In hypothesis testing, the null hypothesis can either be rejected or not rejected based on the comparison between the p value and the alpha level.

What is One Tail Hypothesis Testing?

When the rejection region is only on one side of the distribution curve then it is known as one tail hypothesis testing. The right tail test and the left tail test are two types of directional hypothesis testing.

What is the Alpha Level in Two Tail Hypothesis Testing?

To get the alpha level in a two tail hypothesis testing divide \(\alpha\) by 2. This is done as there are two rejection regions in the curve.

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Biology library

Course: biology library   >   unit 1, the scientific method.

• Controlled experiments
• The scientific method and experimental design

Introduction

• Make an observation.
• Ask a question.
• Form a hypothesis , or testable explanation.
• Make a prediction based on the hypothesis.
• Test the prediction.
• Iterate: use the results to make new hypotheses or predictions.

Scientific method example: Failure to toast

1. make an observation., 2. ask a question., 3. propose a hypothesis., 4. make predictions., 5. test the predictions..

• If the toaster does toast, then the hypothesis is supported—likely correct.
• If the toaster doesn't toast, then the hypothesis is not supported—likely wrong.

Logical possibility

Practical possibility, building a body of evidence, 6. iterate..

• If the hypothesis was supported, we might do additional tests to confirm it, or revise it to be more specific. For instance, we might investigate why the outlet is broken.
• If the hypothesis was not supported, we would come up with a new hypothesis. For instance, the next hypothesis might be that there's a broken wire in the toaster.

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• Science Writing

How to Write up a Science Experiment

Last Updated: May 19, 2023 Fact Checked

This article was co-authored by Michael Simpson, PhD . Dr. Michael Simpson (Mike) is a Registered Professional Biologist in British Columbia, Canada. He has over 20 years of experience in ecology research and professional practice in Britain and North America, with an emphasis on plants and biological diversity. Mike also specializes in science communication and providing education and technical support for ecology projects. Mike received a BSc with honors in Ecology and an MA in Society, Science, and Nature from The University of Lancaster in England as well as a Ph.D. from the University of Alberta. He has worked in British, North American, and South American ecosystems, and with First Nations communities, non-profits, government, academia, and industry. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 270,531 times.

Any time you have conducted a science experiment, you should write a lab report detailing why the experiment was performed, the results you expected, the process you used, the actual results, and a discussion of what the results mean. Lab reports often follow a very standard format starting with an abstract and introduction, followed by a materials and methods section, the results and discussion, and finally a conclusion. This format will allow the reader to find answers to common questions that are often asked: Why was the experiment performed? What were the expected results? How was the experiment conducted? What happened in the experiment? What do the results mean? This article explains the basic format of a lab report.

Lab Report Template

Writing an Abstract and Introduction

• The purpose of this short summary is to provide the reader with enough information on the experiment that they can see if they want or need to read the entire report. The abstract helps them determine if your research is relevant to them.
• Devote a sentence to describing the purpose of the project and its significance. Then, very briefly describe the materials and methods used. Follow up with a 1-2 sentence description of the results of the experiment. You might also provide a list of keywords listing subjects related to your research.

• The introduction will outline what the experiment is, why it was done, and why it is important. It must provide the reader with two key pieces of information: what is the question the experiment is supposed to answer and why is answering this question important.

• A research hypothesis should be a brief statement that pares down your problem that you described in your introduction into something that is testable and falsifiable.
• Scientists must create a hypothesis from which an experiment can reasonably be designed and carried out.
• A hypothesis is never proved in an experiment, only "verified" or "supported".

• For example, you might start with "Fertilizer affects how tall a plant will grow". You could expand this idea to a clear hypothesis: "Plants grow faster and taller when they are given fertilizer". To make it a testable hypothesis, you could add experimental details: "Plants which are given a solution with 1ml of fertilizer grow faster than plants without fertilizer because they are given more nutrients."

Explaining Your Research Procedure

• This section is extremely crucial documentation of your methods of analysis.

• For example, if you were testing how fertilizer affects plant growth, you would want to state what brand of fertilizer you used, what species of plant you used and what brand of seed.
• Make sure you include the quantity of all objects used in the experiment.

• Remember all experiments involve controls and variables. Describe these here.
• If you used a published laboratory method, be sure to provide a reference for the original method.

Reporting Results

• For example, if you are testing the effect of fertilizer on plant growth you would want a graph showing the average growth of plants given fertilizer vs. those without.
• You would also want to describe the result. For example "Plants which were given a concentration of 1ml of fertilizer grew an average of 4 cm taller than those that were not given fertilizer."
• As you go along, narrate your results. Tell the reader why a result is significant to the experiment or problem. This will allow the reader to follow your thinking process.
• Compare your results to your original hypothesis. State whether or not your hypothesis was supported or not by your experiment.
• Quantitative data is anything expressed in terms of numerical forms such as percentages or statistics. Qualitative data is derived from broad questions and is expressed in the form of word responses from study participants.

• In this section, the author can address other questions such as: "why did we get an unexpected result?" or "what would happen if one aspect of the procedure was altered?".
• If your results did not verify your hypothesis, explain your reasoning why.

• Be sure to link back to the introduction and whether or not the experiment addressed the goals of your analysis.

• You can use software such as EndNote to help you cite and build a properly referenced bibliography.

Expert Q&A

You Might Also Like

• ↑ Michael Simpson, PhD. Registered Professional Biologist. Expert Interview. 8 September 2021.
• ↑ https://www.matrix.edu.au/how-to-write-a-scientific-report/
• ↑ https://explorable.com/research-hypothesis
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• CAREER COLUMN
• 20 May 2024

How to set up your new lab space

• Marissa Coppola 0 &
• Jessica W. Tsai 1

Marissa Coppola is a research specialist at the Children’s Hospital Los Angeles in California.

You can also search for this author in PubMed   Google Scholar

Jessica W. Tsai is an assistant professor of paediatrics and a paediatric oncologist at Children’s Hospital Los Angeles in California and the Keck School of Medicine of the University of Southern California in Los Angeles.

Marissa Coppola (left) and Jessica Tsai (right) took a systematic approach when setting up their new lab space and organizing equipment and supplies. Credit: Marissa Coppola, Jessica Tsai

The first time that J.W.T. flew to California to check out her new laboratory, she looked around with astonishment. “Oh gosh,” she thought, “now I have to figure out how to fill this space!”

A recently hired faculty member has several challenges to face. They have to find a place to live, and perhaps schools for their children. They need to arrange for their research materials and personal belongings to be shipped to their new home. And they have to make an empty lab space their own.

Planning the layout is more than simply an exercise in ‘lab Tetris’: a well-organized workspace frees lab members to focus on science. And when everyone is fully invested in their work, a thriving scientific community can grow.

J.W.T. spent a lot of time thinking about what was most important for her lab space — even before accepting the position. Her priorities included functionality and flexibility; yours might be different. Being cost-conscious, she also wanted to avoid purchasing equipment or supplies that were already shared among labs in her department or were otherwise available.

M.C., a research specialist, was J.W.T.’s first lab member. Together, we shaped the space to our needs — here’s how we did it.

Our first task was to think about the kinds of experiment that we would be doing. Our lab studies the genomic drivers of paediatric brain tumors, and our experiments fall into four main categories: bacterial, plasmid-free, tissue culture and western blotting. We created a spreadsheet with four tabs, one for each category. In each tab, we drew up an exhaustive list of items that we would need, including equipment and consumables. For example, in the tissue-culture tab, we added everything from biosafety cabinets to pipette tips.

Collection: How to grow a healthy lab

Pro tip number 1: send your spreadsheet to the operations or administrative team at your new institution to find out whether anything on your list is available as shared equipment. Using shared equipment saved us a lot of start-up money and space, and helped us to avoid future maintenance expenses.

Our lab consists of one main room with five benches and a small side room. To visualize the space, we drew a floor plan, which we edited as we made decisions about where to place things.

The backbone of our work is tissue culture, so we dedicated the side room to it. Tissue culture requires many pieces of equipment — including biosafety cabinets, incubators, a centrifuge, a microscope and a cell counter — that we couldn’t put elsewhere. Moreover, culturing cells and maintaining sterility in a biosafety cabinet require focus, and we felt that a separate space would minimize distractions.

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Next, we determined which benches we would use for our other experiments. Bacteria can be a source of contamination, so we assigned bacterial work to Bench 5, which was located farthest from the tissue-culture area. We also wanted to separate the bacterial workbench from the plasmid-free area, because bacteria are used to generate plasmids. The plasmid-free area is where we perform DNA- and RNA-extraction experiments, which can yield false results if they are contaminated by plasmids. So, we assigned the plasmid-free workspace to Benches 1 and 2. That left Benches 3 and 4 for western blotting.

This arrangement might seem obvious in hindsight, but designing our layout step by step allowed us to think about how the physical space could help or hinder our experiments and avoid future contamination problems.

Equipment installation

Our next task was to decide where to put our two biggest pieces of equipment, a refrigerator and a −20 °C freezer. We had originally planned to put them next to each other — but that would have meant that they could not be opened at the same time. We also wanted the exit doors and high-traffic areas to be free of large obstacles. After trying out several arrangements, we decided to place the refrigerator and freezer on opposite sides of the entrance, with clearance on either side.

More than 225 kilograms of consumable supplies were fit into the new space. Credit: Marissa Coppola, Jessica Tsai

Almost immediately, our plans were muddied. Days after finalizing the refrigerator and freezer locations, our department offered us another refrigerator. We had been planning to purchase a mini-fridge to place beneath the bench for bacterial work, but could not turn down the chance to save money by taking free equipment — even if it was too large for its intended location. We ultimately decided that the new fridge would fit perfectly next to the freezer, a placement that would free up space under the bacterial bench for extra consumables.

Pro tip number 2: try several set-ups for equipment and supplies. Your final configuration will probably be the result of a number of iterations, and that’s OK.

Storing supplies

Once our equipment had been placed, we realized that we had a lot of empty wall space. We asked the carpentry team to add rows of open shelving wherever they could, effectively tripling our storage space.

Our last organizational hurdle was deciding how to fill the space with consumables and supplies. We started in the tissue-culture room, because a lot of plasticware is needed to grow brain tumour cells. We use two kinds of culture plate, ultra-low attachment and adherent, which look identical but have very different functions and prices. Our priority in the tissue-culture room was to separate these plastics, so people wouldn’t accidentally grab the wrong type of plate — avoiding confusion and failed experiments. We also needed to allocate space for other supplies. So we grabbed a pack of sticky notes and used our supply spreadsheet to label drawers and shelves with tentative locations for items.

We then tested out working in the space, to see whether our system made sense. Often, it didn’t. For example, we originally labelled some shelves as storage for serological pipettes. But we quickly realized that these could be kept in large drawers, and that the shelves would be better for storing bulkier items. We used the same approach to organize the remaining lab benches. Finally, we replaced the sticky notes with large adhesive labels with removable inserts, so we could easily change how things were organized. Clear labelling means there is a place for everything, and everything has its place.

Let the science begin

Lab organization can seem intimidating, or even boring — but it’s neither. For one thing, it gave us the opportunity to meet people in our neighbouring labs: when we needed inspiration, we checked out how their spaces were organized. These steps were not completed in a day, but took months. And although we are satisfied with our current layout, it could change as the lab grows.

Let’s face it: research is difficult. Taking the time to mindfully set up a new space, with organization as a top priority, is an investment in efficiency. Most importantly, your team will be able to work together cohesively, tackling tough scientific questions with minimal friction. Your future self — and lab members — will thank you.

doi: https://doi.org/10.1038/d41586-024-01479-6

This is an article from the Nature Careers Community, a place for Nature readers to share their professional experiences and advice. Guest posts are encouraged .

Competing Interests

The authors declare no competing interests.

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