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How to Conduct a Science Experiment

Last Updated: June 5, 2024 Fact Checked

This article was co-authored by Meredith Juncker, PhD . Meredith Juncker is a PhD candidate in Biochemistry and Molecular Biology at Louisiana State University Health Sciences Center. Her studies are focused on proteins and neurodegenerative diseases. There are 10 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 196,090 times.

Experimentation is the method by which scientists test natural phenomena in the hopes of gaining new knowledge.. Good experiments follow a logical design to isolate and test specific, precisely-defined variables. By learning the fundamental principles behind experimental design, you'll be able to apply these principles to your own experiments. Regardless of their scope, all good experiments operate according to the logical, deductive principles of the scientific method, from fifth-grade potato clock science fair projects to cutting-edge Higgs Boson research. [1] X Research source

Designing a Scientifically Sound Experiment

• For instance, if you want to do an experiment on agricultural fertilizer, don't seek to answer the question, "Which kind of fertilizer is best for growing plants?" There are many different types of fertilizer and many different kinds of plants in the world - one experiment won't be able to draw universal conclusions about either. A much better question to design an experiment around would be "What concentration of nitrogen in fertilizer produces the largest corn crops?"
• Modern scientific knowledge is very, very vast. If you intend to do serious scientific research, research your topic extensively before you even begin to plan your experiment. Have past experiments answered the question you want your experiment to study? If so, is there a way to adjust your topic so that it addresses questions left unanswered by existing research?

• For instance, in our fertilizer experiment example, our scientist would grow multiple corn crops in soil supplemented with fertilizers whose nitrogen concentration differs. He would give each corn crop the exact same amount of fertilizer. He would make sure the chemical composition of his fertilizers used did not differ in some way besides its nitrogen concentration - for instance, he would not use a fertilizer with a higher concentration of magnesium for one of his corn crops. He would also grow the exact same number and species of corn crops at the same time and in the same type of soil in each replication of his experiment.

• Typically, a hypothesis is expressed as a quantitative declarative sentence. A hypothesis also takes into account the ways that the experimental parameters will be measured. A good hypothesis for our fertilizer example is: "Corn crops supplemented with 1 pound of nitrogen per bushel will result in a greater yield mass than equivalent corn crops grown with differing nitrogen supplements."

• Timing is incredibly important, so stick to your plan as close as possible. That way, if you see changes in your results, you can rule out different time constraints as the cause of the change.
• Making a data table beforehand is a great idea - you'll be able to simply insert your data values into the table as you record them.
• Know the difference between your dependent and independent variables. An independent variable is a variable that you change and a dependent variable is the one affected by the independent variable. In our example, "nitrogen content" is the independent variable, and "yield (in kg)" is the dependent variable. A basic table will have columns for both variables as they change over time.

• Good experimental design incorporates what's known as a control. One of your experimental replications should not include the variable you're testing for at all. In our fertilizer example, we'll include one corn crop which receives a fertilizer with no nitrogen in it. This will be our control - it will be the baseline against which we'll measure the growth of our other corn crops.
• Observe any and all safety measures associated with hazardous materials or processes in your experiment. [6] X Research source

• It's always a good idea to represent your data visually if you can. Plot data points on a graph and express trends with a line or curve of best fit. This will help you (and anyone else who sees the graph) visualize patterns in the data. For most basic experiments, the independent variable is represented on the horizontal x axis and the dependent variable is on the vertical y axis.

• To share your results, write a comprehensive scientific paper. Knowing how to write a scientific paper is a useful skill - the results of most new research must be written and published according to a specific format, often dictated by the style guide for a relevant, peer-reviewed academic journal.

Running an Example Experiment

• In this case, the type of aerosol fuel we use is the independent variable (the variable we change), while the range of the projectile is the dependent variable.
• Things to consider for this experiment - is there a way to ensure each potato projectile has the same weight? Is there a way to administer the same amount of aerosol fuel for each firing? Both of these can potentially affect the range of the gun. Weigh each projectile beforehand and fuel each shot with the same amount of aerosol spray.

• The furthest-left column will be labeled "Trial #." The cells in this column will simply contain the numbers 1-10, signifying each firing attempt.
• The following four columns will be labeled with the names of the aerosol sprays we're using in our experiment. The ten cells beneath each column header will contain the range (in meters) of each firing attempt.
• Below the four columns for each fuel, leave a space to write the average value of the ranges.

• Like many experiments, our experiment has certain safety concerns we need to observe. The aerosol fuels we're using are flammable - we should be sure to close the potato gun's firing cap securely and to wear heavy gloves while igniting the fuel. To avoid accidental injuries from the projectiles, we should also make sure that we (and any observers) are standing to the side of the gun as it fires - not in front of it or behind it.

• We can even share our results with the world in the form of a scientific paper - given the subject matter of our experiment, it may be more appropriate to present this information in the form of a tri-fold science fair display.

Community Q&A

• Science is about asking big questions. Don't be afraid to choose a topic you haven't looked at before. Thanks Helpful 0 Not Helpful 0
• Have fun and stay safe. Thanks Helpful 0 Not Helpful 0
• In upper-level sciences, most data isn't used unless it is reproducible at least 3 times. Thanks Helpful 0 Not Helpful 0

• Wear eye protection Thanks Helpful 29 Not Helpful 1
• Wash your hands before and after an experiment. Thanks Helpful 29 Not Helpful 3
• Do not have any food or drinks near your workstation. Thanks Helpful 25 Not Helpful 5
• If anything gets in your eyes rinse them out thoroughly with water for 15 minutes, then seek immediate medical attention. Thanks Helpful 7 Not Helpful 0
• When using sharp knives, dangerous chemicals, or hot flames, make sure you have an adult supervising you at all times. Thanks Helpful 15 Not Helpful 3
• Tie loose hair back Thanks Helpful 23 Not Helpful 7
• Wear rubber gloves when handling chemicals Thanks Helpful 23 Not Helpful 8

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• ↑ https://www.khanacademy.org/science/high-school-biology/hs-biology-foundations/hs-biology-and-the-scientific-method/a/experiments-and-observations
• ↑ https://www.sciencebuddies.org/science-fair-projects/project-ideas/list
• ↑ https://www.sciencebuddies.org/science-fair-projects/science-fair/variables
• ↑ https://www.livescience.com/21490-what-is-a-scientific-hypothesis-definition-of-hypothesis.html
• ↑ https://sciencing.com/collect-data-science-project-5988780.html
• ↑ https://ehsdailyadvisor.blr.com/2012/04/11-rules-for-safe-handling-of-hazardous-materials/
• ↑ https://www.sciencebuddies.org/science-fair-projects/science-fair/conducting-an-experiment
• ↑ https://www.sciencebuddies.org/science-fair-projects/science-fair/writing-a-hypothesis
• ↑ https://www.sciencebuddies.org/science-fair-projects/science-fair/steps-of-the-scientific-method
• ↑ https://www.sciencebuddies.org/science-fair-projects/science-fair/data-analysis-graphs

About This Article

If you want to conduct a science experiment, first come up with a question you want to answer, then devise a way to test that question. Make sure you have a control, or an untested component to your experiment. For example, if you want to find out which fertilizer is best for growing crops, you would have one plant for each type of fertilizer, plus one plant that doesn’t get any fertilizer. Write down each step of your experiment carefully, along with the final result. For tips on organizing your data collection, read on! Did this summary help you? Yes No

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Conducting an Experiment

Science revolves around experiments, and learning the best way of conducting an experiment is crucial to obtaining useful and valid results.

This article is a part of the guide:

• Experimental Research
• Pretest-Posttest
• Third Variable
• Research Bias
• Independent Variable

Browse Full Outline

• 1 Experimental Research
• 2.1 Independent Variable
• 2.2 Dependent Variable
• 2.3 Controlled Variables
• 2.4 Third Variable
• 3.1 Control Group
• 3.2 Research Bias
• 3.3.1 Placebo Effect
• 3.3.2 Double Blind Method
• 4.1 Randomized Controlled Trials
• 4.2 Pretest-Posttest
• 4.3 Solomon Four Group
• 4.4 Between Subjects
• 4.5 Within Subject
• 4.6 Repeated Measures
• 4.7 Counterbalanced Measures
• 4.8 Matched Subjects

When scientists speak of experiments, in the strictest sense of the word, they mean a true experiment , where the scientist controls all of the factors and conditions.

Real world observations, and case studies , should be referred to as observational research , rather than experiments.

For example, observing animals in the wild is not a true experiment, because it does not isolate and manipulate an independent variable .

The Basis of Conducting an Experiment

With an experiment, the researcher is trying to learn something new about the world, an explanation of 'why' something happens.

The experiment must maintain internal and external validity, or the results will be useless.

When designing an experiment , a researcher must follow all of the steps of the scientific method , from making sure that the hypothesis is valid and testable , to using controls and statistical tests.

Whilst all scientists use reasoning , operationalization and the steps of the scientific process , it is not always a conscious process.

Experience and practice mean that many scientists follow an instinctive process of conducting an experiment, the 'streamlined' scientific process . Following the basic steps will usually generate valid results, but where experiments are complex and expensive, it is always advisable to follow the rigorous scientific protocols. Conducting an experiment has a number of stages, where the parameters and structure of the experiment are made clear.

Whilst it is rarely practical to follow each step strictly, any aberrations must be justified, whether they arise because of budget, impracticality or ethics .

After deciding upon a hypothesis , and making predictions, the first stage of conducting an experiment is to specify the sample groups. These should be large enough to give a statistically viable study, but small enough to be practical.

Ideally, groups should be selected at random , from a wide selection of the sample population. This allows results to be generalized to the population as a whole.

In the physical sciences, this is fairly easy, but the biological and behavioral sciences are often limited by other factors.

For example, medical trials often cannot find random groups. Such research often relies upon volunteers, so it is difficult to apply any realistic randomization . This is not a problem, as long as the process is justified, and the results are not applied to the population as a whole.

If a psychological researcher used volunteers who were male students, aged between 18 and 24, the findings can only be generalized to that specific demographic group within society.

The sample groups should be divided, into a control group and a test group, to reduce the possibility of confounding variables .

This, again, should be random, and the assigning of subjects to groups should be blind or double blind . This will reduce the chances of experimental error , or bias, when conducting an experiment.

Ethics are often a barrier to this process, because deliberately withholding treatment, as with the Tuskegee study , is not permitted.

Again, any deviations from this process must be explained in the conclusion. There is nothing wrong with compromising upon randomness, where necessary, as long as other scientists are aware of how, and why, the researcher selected groups on that basis.

Stage Three

This stage of conducting an experiment involves determining the time scale and frequency of sampling , to fit the type of experiment.

For example, researchers studying the effectiveness of a cure for colds would take frequent samples, over a period of days. Researchers testing a cure for Parkinson's disease would use less frequent tests, over a period of months or years.

The penultimate stage of the experiment involves performing the experiment according to the methods stipulated during the design phase.

The independent variable is manipulated, generating a usable data set for the dependent variable .

The raw data from the results should be gathered, and analyzed, by statistical means. This allows the researcher to establish if there is any relationship between the variables and accept, or reject, the null hypothesis .

These steps are essential to providing excellent results. Whilst many researchers do not want to become involved in the exact processes of inductive reasoning , deductive reasoning and operationalization , they all follow the basic steps of conducting an experiment. This ensures that their results are valid .

Preparing a Coordination Schema of the Whole Research Plan

Preparing a coordination schema of the research plan may be another useful tool in undertaking research planning. While preparing a coordination schema, one may have to identify the broad variable in the form of parameters, complex variables and disaggregate those in the form of simple variables. Coordination Schema: A Methodological Tool in Research Planning by Purnima Mohapatra is a very useful tool. Arranging everything in a schema not only makes the research more organised, it also saves a lot of valuable time for the researcher. 

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Martyn Shuttleworth (May 24, 2008). Conducting an Experiment. Retrieved Sep 03, 2024 from Explorable.com: https://explorable.com/conducting-an-experiment

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• Guide to Experimental Design | Overview, Steps, & Examples

Guide to Experimental Design | Overview, 5 steps & Examples

Published on December 3, 2019 by Rebecca Bevans . Revised on June 21, 2023.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design create a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying.

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead. This minimizes several types of research bias, particularly sampling bias , survivorship bias , and attrition bias as time passes.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, other interesting articles, frequently asked questions about experiments.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalized and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomized design vs a randomized block design .
• A between-subjects design vs a within-subjects design .

Randomization

An experiment can be completely randomized or randomized within blocks (aka strata):

• In a completely randomized design , every subject is assigned to a treatment group at random.
• In a randomized block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomization isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs. within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomizing or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimize research bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalized to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

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

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

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What Is an Experiment? Definition and Design

The Basics of an Experiment

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Science is concerned with experiments and experimentation, but do you know what exactly an experiment is? Here's a look at what an experiment is... and isn't!

Key Takeaways: Experiments

• An experiment is a procedure designed to test a hypothesis as part of the scientific method.
• The two key variables in any experiment are the independent and dependent variables. The independent variable is controlled or changed to test its effects on the dependent variable.
• Three key types of experiments are controlled experiments, field experiments, and natural experiments.

What Is an Experiment? The Short Answer

In its simplest form, an experiment is simply the test of a hypothesis . A hypothesis, in turn, is a proposed relationship or explanation of phenomena.

Experiment Basics

The experiment is the foundation of the scientific method , which is a systematic means of exploring the world around you. Although some experiments take place in laboratories, you could perform an experiment anywhere, at any time.

Take a look at the steps of the scientific method:

• Make observations.
• Formulate a hypothesis.
• Design and conduct an experiment to test the hypothesis.
• Evaluate the results of the experiment.
• Accept or reject the hypothesis.
• If necessary, make and test a new hypothesis.

Types of Experiments

• Natural Experiments : A natural experiment also is called a quasi-experiment. A natural experiment involves making a prediction or forming a hypothesis and then gathering data by observing a system. The variables are not controlled in a natural experiment.
• Controlled Experiments : Lab experiments are controlled experiments , although you can perform a controlled experiment outside of a lab setting! In a controlled experiment, you compare an experimental group with a control group. Ideally, these two groups are identical except for one variable , the independent variable .
• Field Experiments : A field experiment may be either a natural experiment or a controlled experiment. It takes place in a real-world setting, rather than under lab conditions. For example, an experiment involving an animal in its natural habitat would be a field experiment.

Variables in an Experiment

Simply put, a variable is anything you can change or control in an experiment. Common examples of variables include temperature, duration of the experiment, composition of a material, amount of light, etc. There are three kinds of variables in an experiment: controlled variables, independent variables and dependent variables .

Controlled variables , sometimes called constant variables are variables that are kept constant or unchanging. For example, if you are doing an experiment measuring the fizz released from different types of soda, you might control the size of the container so that all brands of soda would be in 12-oz cans. If you are performing an experiment on the effect of spraying plants with different chemicals, you would try to maintain the same pressure and maybe the same volume when spraying your plants.

The independent variable is the one factor that you are changing. It is one factor because usually in an experiment you try to change one thing at a time. This makes measurements and interpretation of the data much easier. If you are trying to determine whether heating water allows you to dissolve more sugar in the water then your independent variable is the temperature of the water. This is the variable you are purposely controlling.

The dependent variable is the variable you observe, to see whether it is affected by your independent variable. In the example where you are heating water to see if this affects the amount of sugar you can dissolve , the mass or volume of sugar (whichever you choose to measure) would be your dependent variable.

Examples of Things That Are Not Experiments

• Making a model volcano.
• Making a poster.
• Changing a lot of factors at once, so you can't truly test the effect of the dependent variable.
• Trying something, just to see what happens. On the other hand, making observations or trying something, after making a prediction about what you expect will happen, is a type of experiment.
• Bailey, R.A. (2008). Design of Comparative Experiments . Cambridge: Cambridge University Press. ISBN 9780521683579.
• Beveridge, William I. B., The Art of Scientific Investigation . Heinemann, Melbourne, Australia, 1950.
• di Francia, G. Toraldo (1981). The Investigation of the Physical World . Cambridge University Press. ISBN 0-521-29925-X.
• Hinkelmann, Klaus and Kempthorne, Oscar (2008). Design and Analysis of Experiments, Volume I: Introduction to Experimental Design (Second ed.). Wiley. ISBN 978-0-471-72756-9.
• Shadish, William R.; Cook, Thomas D.; Campbell, Donald T. (2002). Experimental and quasi-experimental designs for generalized causal inference (Nachdr. ed.). Boston: Houghton Mifflin. ISBN 0-395-61556-9.
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A Complete Guide to Experimental Research

Published by Carmen Troy at August 14th, 2021 , Revised On August 25, 2023

A Quick Guide to Experimental Research

Experimental research refers to the experiments conducted in the laboratory or observation under controlled conditions. Researchers try to find out the cause-and-effect relationship between two or more variables. 

The subjects/participants in the experiment are selected and observed. They receive treatments such as changes in room temperature, diet, atmosphere, or given a new drug to observe the changes. Experiments can vary from personal and informal natural comparisons. It includes three  types of variables ;

• Independent variable
• Dependent variable
• Controlled variable

Before conducting experimental research, you need to have a clear understanding of the experimental design. A true experimental design includes  identifying a problem , formulating a  hypothesis , determining the number of variables, selecting and assigning the participants,  types of research designs , meeting ethical values, etc.

There are many  types of research  methods that can be classified based on:

• The nature of the problem to be studied
• Number of participants (individual or groups)
• Number of groups involved (Single group or multiple groups)
• Types of data collection methods (Qualitative/Quantitative/Mixed methods)
• Number of variables (single independent variable/ factorial two independent variables)
• The experimental design

Types of Experimental Research

Laboratory Experiment  

It is also called experimental research. This type of research is conducted in the laboratory. A researcher can manipulate and control the variables of the experiment.

Example: Milgram’s experiment on obedience.

Field Experiment

Field experiments are conducted in the participants’ open field and the environment by incorporating a few artificial changes. Researchers do not have control over variables under measurement. Participants know that they are taking part in the experiment.

Natural Experiments

The experiment is conducted in the natural environment of the participants. The participants are generally not informed about the experiment being conducted on them.

Examples: Estimating the health condition of the population. Did the increase in tobacco prices decrease the sale of tobacco? Did the usage of helmets decrease the number of head injuries of the bikers?

Quasi-Experiments

A quasi-experiment is an experiment that takes advantage of natural occurrences. Researchers cannot assign random participants to groups.

Example: Comparing the academic performance of the two schools.

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How to Conduct Experimental Research?

Step 1. identify and define the problem.

You need to identify a problem as per your field of study and describe your  research question .

Example: You want to know about the effects of social media on the behavior of youngsters. It would help if you found out how much time students spend on the internet daily.

Example: You want to find out the adverse effects of junk food on human health. It would help if you found out how junk food frequent consumption can affect an individual’s health.

Step 2. Determine the Number of Levels of Variables

You need to determine the number of  variables . The independent variable is the predictor and manipulated by the researcher. At the same time, the dependent variable is the result of the independent variable.

In the first example, we predicted that increased social media usage negatively correlates with youngsters’ negative behaviour.

In the second example, we predicted the positive correlation between a balanced diet and a good healthy and negative relationship between junk food consumption and multiple health issues.

Step 3. Formulate the Hypothesis

One of the essential aspects of experimental research is formulating a hypothesis . A researcher studies the cause and effect between the independent and dependent variables and eliminates the confounding variables. A  null hypothesis is when there is no significant relationship between the dependent variable and the participants’ independent variables. A researcher aims to disprove the theory. H0 denotes it.  The  Alternative hypothesis  is the theory that a researcher seeks to prove.  H1or HA denotes it. 

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Step 4. Selection and Assignment of the Subjects

It’s an essential feature that differentiates the experimental design from other research designs . You need to select the number of participants based on the requirements of your experiment. Then the participants are assigned to the treatment group. There should be a control group without any treatment to study the outcomes without applying any changes compared to the experimental group.

Randomisation:  The participants are selected randomly and assigned to the experimental group. It is known as probability sampling. If the selection is not random, it’s considered non-probability sampling.

Stratified sampling : It’s a type of random selection of the participants by dividing them into strata and randomly selecting them from each level. 

Matching:   Even though participants are selected randomly, they can be assigned to the various comparison groups. Another procedure for selecting the participants is ‘matching.’ The participants are selected from the controlled group to match the experimental groups’ participants in all aspects based on the dependent variables.  

What is Replicability?

When a researcher uses the same methodology  and subject groups to carry out the experiments, it’s called ‘replicability.’ The  results will be similar each time. Researchers usually replicate their own work to strengthen external validity.

Step 5. Select a Research Design

You need to select a  research design  according to the requirements of your experiment. There are many types of experimental designs as follows.

Step 6. Meet Ethical and Legal Requirements

• Participants of the research should not be harmed.
• The dignity and confidentiality of the research should be maintained.
• The consent of the participants should be taken before experimenting.
• The privacy of the participants should be ensured.
• Research data should remain confidential.
• The anonymity of the participants should be ensured.
• The rules and objectives of the experiments should be followed strictly.
• Any wrong information or data should be avoided.

Tips for Meeting the Ethical Considerations

To meet the ethical considerations, you need to ensure that.

• Participants have the right to withdraw from the experiment.
• They should be aware of the required information about the experiment.
• It would help if you avoided offensive or unacceptable language while framing the questions of interviews, questionnaires, or Focus groups.
• You should ensure the privacy and anonymity of the participants.
• You should acknowledge the sources and authors in your dissertation using any referencing styles such as APA/MLA/Harvard referencing style.

Step 7. Collect and Analyse Data.

Collect the data  by using suitable data collection according to your experiment’s requirement, such as observations,  case studies ,  surveys ,  interviews , questionnaires, etc. Analyse the obtained information.

Step 8. Present and Conclude the Findings of the Study.

Write the report of your research. Present, conclude, and explain the outcomes of your study .  

Frequently Asked Questions

What is the first step in conducting an experimental research.

The first step in conducting experimental research is to define your research question or hypothesis. Clearly outline the purpose and expectations of your experiment to guide the entire research process.

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What are the different research strategies you can use in your dissertation? Here are some guidelines to help you choose a research strategy that would make your research more credible.

Statistical significance is described as the measure of the null hypothesis being plausible as compared to the acceptable level of vagueness regarding the true answer.

Statistical tests are used for testing the hypothesis to determine the relaltionship between variables but which statistical test you should use?

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1.3 - steps for planning, conducting and analyzing an experiment.

The practical steps needed for planning and conducting an experiment include: recognizing the goal of the experiment, choice of factors, choice of response, choice of the design, analysis and then drawing conclusions. This pretty much covers the steps involved in the scientific method.

• Recognition and statement of the problem
• Choice of factors, levels, and ranges
• Selection of the response variable(s)
• Choice of design
• Conducting the experiment
• Statistical analysis
• Drawing conclusions, and making recommendations

What this course will deal with primarily is the choice of the design. This focus includes all the related issues about how we handle these factors in conducting our experiments.

Factors Section  

We usually talk about "treatment" factors, which are the factors of primary interest to you. In addition to treatment factors, there are nuisance factors which are not your primary focus, but you have to deal with them. Sometimes these are called blocking factors, mainly because we will try to block on these factors to prevent them from influencing the results.

There are other ways that we can categorize factors:

Experimental Design: Types, Examples & Methods

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

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Experimental design refers to how participants are allocated to different groups in an experiment. Types of design include repeated measures, independent groups, and matched pairs designs.

Probably the most common way to design an experiment in psychology is to divide the participants into two groups, the experimental group and the control group, and then introduce a change to the experimental group, not the control group.

The researcher must decide how he/she will allocate their sample to the different experimental groups.  For example, if there are 10 participants, will all 10 participants participate in both groups (e.g., repeated measures), or will the participants be split in half and take part in only one group each?

Three types of experimental designs are commonly used:

1. Independent Measures

Independent measures design, also known as between-groups , is an experimental design where different participants are used in each condition of the independent variable.  This means that each condition of the experiment includes a different group of participants.

This should be done by random allocation, ensuring that each participant has an equal chance of being assigned to one group.

Independent measures involve using two separate groups of participants, one in each condition. For example:

• Con : More people are needed than with the repeated measures design (i.e., more time-consuming).
• Pro : Avoids order effects (such as practice or fatigue) as people participate in one condition only.  If a person is involved in several conditions, they may become bored, tired, and fed up by the time they come to the second condition or become wise to the requirements of the experiment!
• Con : Differences between participants in the groups may affect results, for example, variations in age, gender, or social background.  These differences are known as participant variables (i.e., a type of extraneous variable ).
• Control : After the participants have been recruited, they should be randomly assigned to their groups. This should ensure the groups are similar, on average (reducing participant variables).

2. Repeated Measures Design

Repeated Measures design is an experimental design where the same participants participate in each independent variable condition.  This means that each experiment condition includes the same group of participants.

Repeated Measures design is also known as within-groups or within-subjects design .

• Pro : As the same participants are used in each condition, participant variables (i.e., individual differences) are reduced.
• Con : There may be order effects. Order effects refer to the order of the conditions affecting the participants’ behavior.  Performance in the second condition may be better because the participants know what to do (i.e., practice effect).  Or their performance might be worse in the second condition because they are tired (i.e., fatigue effect). This limitation can be controlled using counterbalancing.
• Pro : Fewer people are needed as they participate in all conditions (i.e., saves time).
• Control : To combat order effects, the researcher counter-balances the order of the conditions for the participants.  Alternating the order in which participants perform in different conditions of an experiment.

Counterbalancing

Suppose we used a repeated measures design in which all of the participants first learned words in “loud noise” and then learned them in “no noise.”

We expect the participants to learn better in “no noise” because of order effects, such as practice. However, a researcher can control for order effects using counterbalancing.

The sample would be split into two groups: experimental (A) and control (B).  For example, group 1 does ‘A’ then ‘B,’ and group 2 does ‘B’ then ‘A.’ This is to eliminate order effects.

Although order effects occur for each participant, they balance each other out in the results because they occur equally in both groups.

3. Matched Pairs Design

A matched pairs design is an experimental design where pairs of participants are matched in terms of key variables, such as age or socioeconomic status. One member of each pair is then placed into the experimental group and the other member into the control group .

One member of each matched pair must be randomly assigned to the experimental group and the other to the control group.

• Con : If one participant drops out, you lose 2 PPs’ data.
• Pro : Reduces participant variables because the researcher has tried to pair up the participants so that each condition has people with similar abilities and characteristics.
• Con : Very time-consuming trying to find closely matched pairs.
• Pro : It avoids order effects, so counterbalancing is not necessary.
• Con : Impossible to match people exactly unless they are identical twins!
• Control : Members of each pair should be randomly assigned to conditions. However, this does not solve all these problems.

Experimental design refers to how participants are allocated to an experiment’s different conditions (or IV levels). There are three types:

1. Independent measures / between-groups : Different participants are used in each condition of the independent variable.

2. Repeated measures /within groups : The same participants take part in each condition of the independent variable.

3. Matched pairs : Each condition uses different participants, but they are matched in terms of important characteristics, e.g., gender, age, intelligence, etc.

Learning Check

Read about each of the experiments below. For each experiment, identify (1) which experimental design was used; and (2) why the researcher might have used that design.

1 . To compare the effectiveness of two different types of therapy for depression, depressed patients were assigned to receive either cognitive therapy or behavior therapy for a 12-week period.

The researchers attempted to ensure that the patients in the two groups had similar severity of depressed symptoms by administering a standardized test of depression to each participant, then pairing them according to the severity of their symptoms.

2 . To assess the difference in reading comprehension between 7 and 9-year-olds, a researcher recruited each group from a local primary school. They were given the same passage of text to read and then asked a series of questions to assess their understanding.

3 . To assess the effectiveness of two different ways of teaching reading, a group of 5-year-olds was recruited from a primary school. Their level of reading ability was assessed, and then they were taught using scheme one for 20 weeks.

At the end of this period, their reading was reassessed, and a reading improvement score was calculated. They were then taught using scheme two for a further 20 weeks, and another reading improvement score for this period was calculated. The reading improvement scores for each child were then compared.

4 . To assess the effect of the organization on recall, a researcher randomly assigned student volunteers to two conditions.

Condition one attempted to recall a list of words that were organized into meaningful categories; condition two attempted to recall the same words, randomly grouped on the page.

Experiment Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. Extraneous variables should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of taking part in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

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Chapter 6: Experimental Research

Conducting Experiments

Learning Objectives

• Describe several strategies for recruiting participants for an experiment.
• Explain why it is important to standardize the procedure of an experiment and several ways to do this.
• Explain what pilot testing is and why it is important.

The information presented so far in this chapter is enough to design a basic experiment. When it comes time to conduct that experiment, however, several additional practical issues arise. In this section, we consider some of these issues and how to deal with them. Much of this information applies to nonexperimental studies as well as experimental ones.

Recruiting Participants

Of course, at the start of any research project you should be thinking about how you will obtain your participants. Unless you have access to people with schizophrenia or incarcerated juvenile offenders, for example, then there is no point designing a study that focuses on these populations. But even if you plan to use a convenience sample, you will have to recruit participants for your study.

There are several approaches to recruiting participants. One is to use participants from a formal  subject pool —an established group of people who have agreed to be contacted about participating in research studies. For example, at many colleges and universities, there is a subject pool consisting of students enrolled in introductory psychology courses who must participate in a certain number of studies to meet a course requirement. Researchers post descriptions of their studies and students sign up to participate, usually via an online system. Participants who are not in subject pools can also be recruited by posting or publishing advertisements or making personal appeals to groups that represent the population of interest. For example, a researcher interested in studying older adults could arrange to speak at a meeting of the residents at a retirement community to explain the study and ask for volunteers.

The Volunteer Subject

Even if the participants in a study receive compensation in the form of course credit, a small amount of money, or a chance at being treated for a psychological problem, they are still essentially volunteers. This is worth considering because people who volunteer to participate in psychological research have been shown to differ in predictable ways from those who do not volunteer. Specifically, there is good evidence that on average, volunteers have the following characteristics compared with nonvolunteers (Rosenthal & Rosnow, 1976) [1] :

• They are more interested in the topic of the research.
• They are more educated.
• They have a greater need for approval.
• They have higher intelligence quotients (IQs).
• They are more sociable.
• They are higher in social class.

This difference can be an issue of external validity if there is reason to believe that participants with these characteristics are likely to behave differently than the general population. For example, in testing different methods of persuading people, a rational argument might work better on volunteers than it does on the general population because of their generally higher educational level and IQ.

In many field experiments, the task is not recruiting participants but selecting them. For example, researchers Nicolas Guéguen and Marie-Agnès de Gail conducted a field experiment on the effect of being smiled at on helping, in which the participants were shoppers at a supermarket. A confederate walking down a stairway gazed directly at a shopper walking up the stairway and either smiled or did not smile. Shortly afterward, the shopper encountered another confederate, who dropped some computer diskettes on the ground. The dependent variable was whether or not the shopper stopped to help pick up the diskettes (Guéguen & de Gail, 2003) [2] . Notice that these participants were not “recruited,” but the researchers still had to select them from among all the shoppers taking the stairs that day. It is extremely important that this kind of selection be done according to a well-defined set of rules that is established before the data collection begins and can be explained clearly afterward. In this case, with each trip down the stairs, the confederate was instructed to gaze at the first person he encountered who appeared to be between the ages of 20 and 50. Only if the person gazed back did he or she become a participant in the study. The point of having a well-defined selection rule is to avoid bias in the selection of participants. For example, if the confederate was free to choose which shoppers he would gaze at, he might choose friendly-looking shoppers when he was set to smile and unfriendly-looking ones when he was not set to smile. As we will see shortly, such biases can be entirely unintentional.

Standardizing the Procedure

It is surprisingly easy to introduce extraneous variables during the procedure. For example, the same experimenter might give clear instructions to one participant but vague instructions to another. Or one experimenter might greet participants warmly while another barely makes eye contact with them. To the extent that such variables affect participants’ behaviour, they add noise to the data and make the effect of the independent variable more difficult to detect. If they vary across conditions, they become confounding variables and provide alternative explanations for the results. For example, if participants in a treatment group are tested by a warm and friendly experimenter and participants in a control group are tested by a cold and unfriendly one, then what appears to be an effect of the treatment might actually be an effect of experimenter demeanor. When there are multiple experimenters, the possibility for introducing extraneous variables is even greater, but is often necessary for practical reasons.

Experimenter’s Sex as an Extraneous Variable

It is well known that whether research participants are male or female can affect the results of a study. But what about whether the  experimenter  is male or female? There is plenty of evidence that this matters too. Male and female experimenters have slightly different ways of interacting with their participants, and of course participants also respond differently to male and female experimenters (Rosenthal, 1976) [3] .

For example, in a recent study on pain perception, participants immersed their hands in icy water for as long as they could (Ibolya, Brake, & Voss, 2004) [4] . Male participants tolerated the pain longer when the experimenter was a woman, and female participants tolerated it longer when the experimenter was a man.

Researcher Robert Rosenthal has spent much of his career showing that this kind of unintended variation in the procedure does, in fact, affect participants’ behaviour. Furthermore, one important source of such variation is the experimenter’s expectations about how participants “should” behave in the experiment. This outcome is referred to as an  experimenter expectancy effect  (Rosenthal, 1976) [5] . For example, if an experimenter expects participants in a treatment group to perform better on a task than participants in a control group, then he or she might unintentionally give the treatment group participants clearer instructions or more encouragement or allow them more time to complete the task. In a striking example, Rosenthal and Kermit Fode had several students in a laboratory course in psychology train rats to run through a maze. Although the rats were genetically similar, some of the students were told that they were working with “maze-bright” rats that had been bred to be good learners, and other students were told that they were working with “maze-dull” rats that had been bred to be poor learners. Sure enough, over five days of training, the “maze-bright” rats made more correct responses, made the correct response more quickly, and improved more steadily than the “maze-dull” rats (Rosenthal & Fode, 1963) [6] . Clearly it had to have been the students’ expectations about how the rats would perform that made the difference. But how? Some clues come from data gathered at the end of the study, which showed that students who expected their rats to learn quickly felt more positively about their animals and reported behaving toward them in a more friendly manner (e.g., handling them more).

The way to minimize unintended variation in the procedure is to standardize it as much as possible so that it is carried out in the same way for all participants regardless of the condition they are in. Here are several ways to do this:

• Create a written protocol that specifies everything that the experimenters are to do and say from the time they greet participants to the time they dismiss them.
• Create standard instructions that participants read themselves or that are read to them word for word by the experimenter.
• Automate the rest of the procedure as much as possible by using software packages for this purpose or even simple computer slide shows.
• Anticipate participants’ questions and either raise and answer them in the instructions or develop standard answers for them.
• Train multiple experimenters on the protocol together and have them practice on each other.
• Be sure that each experimenter tests participants in all conditions.

Another good practice is to arrange for the experimenters to be “blind” to the research question or to the condition that each participant is tested in. The idea is to minimize experimenter expectancy effects by minimizing the experimenters’ expectations. For example, in a drug study in which each participant receives the drug or a placebo, it is often the case that neither the participants nor the experimenter who interacts with the participants know which condition he or she has been assigned to. Because both the participants and the experimenters are blind to the condition, this technique is referred to as a  double-blind study . (A single-blind study is one in which the participant, but not the experimenter, is blind to the condition.) Of course, there are many times this blinding is not possible. For example, if you are both the investigator and the only experimenter, it is not possible for you to remain blind to the research question. Also, in many studies the experimenter  must  know the condition because he or she must carry out the procedure in a different way in the different conditions.

Record Keeping

It is essential to keep good records when you conduct an experiment. As discussed earlier, it is typical for experimenters to generate a written sequence of conditions before the study begins and then to test each new participant in the next condition in the sequence. As you test them, it is a good idea to add to this list basic demographic information; the date, time, and place of testing; and the name of the experimenter who did the testing. It is also a good idea to have a place for the experimenter to write down comments about unusual occurrences (e.g., a confused or uncooperative participant) or questions that come up. This kind of information can be useful later if you decide to analy z e sex differences or effects of different experimenters, or if a question arises about a particular participant or testing session.

It can also be useful to assign an identification number to each participant as you test them. Simply numbering them consecutively beginning with 1 is usually sufficient. This number can then also be written on any response sheets or questionnaires that participants generate, making it easier to keep them together.

Pilot Testing

It is always a good idea to conduct a  pilot test  of your experiment. A pilot test is a small-scale study conducted to make sure that a new procedure works as planned. In a pilot test, you can recruit participants formally (e.g., from an established participant pool) or you can recruit them informally from among family, friends, classmates, and so on. The number of participants can be small, but it should be enough to give you confidence that your procedure works as planned. There are several important questions that you can answer by conducting a pilot test:

• Do participants understand the instructions?
• What kind of misunderstandings do participants have, what kind of mistakes do they make, and what kind of questions do they ask?
• Do participants become bored or frustrated?
• Is an indirect manipulation effective? (You will need to include a manipulation check.)
• Can participants guess the research question or hypothesis?
• How long does the procedure take?
• Are computer programs or other automated procedures working properly?
• Are data being recorded correctly?

Of course, to answer some of these questions you will need to observe participants carefully during the procedure and talk with them about it afterward. Participants are often hesitant to criticize a study in front of the researcher, so be sure they understand that their participation is part of a pilot test and you are genuinely interested in feedback that will help you improve the procedure. If the procedure works as planned, then you can proceed with the actual study. If there are problems to be solved, you can solve them, pilot test the new procedure, and continue with this process until you are ready to proceed.

Key Takeaways

• There are several effective methods you can use to recruit research participants for your experiment, including through formal subject pools, advertisements, and personal appeals. Field experiments require well-defined participant selection procedures.
• It is important to standardize experimental procedures to minimize extraneous variables, including experimenter expectancy effects.
• It is important to conduct one or more small-scale pilot tests of an experiment to be sure that the procedure works as planned.
• elderly adults
• unemployed people
• regular exercisers
• math majors
• Discussion: Imagine a study in which you will visually present participants with a list of 20 words, one at a time, wait for a short time, and then ask them to recall as many of the words as they can. In the stressed condition, they are told that they might also be chosen to give a short speech in front of a small audience. In the unstressed condition, they are not told that they might have to give a speech. What are several specific things that you could do to standardize the procedure?

Image Descriptions

A comic of two stick figures talking.

Person 1: Some researchers are starting to figure out the mechanism behind the placebo effect. We’ve used their work to create a new drug: A placebo effect blocker. Now we just need to run a trial. We’ll get two groups, give them both placebos, then give one the REAL placebo blocker, and the other a…. wait.

[The two people scratch their heads]

Person 2: My head hurts.

Person 1: Mine too. Here, want a sugar pill?

[Return to Image]

Media Attributions

• Study   by XKCD   CC BY-NC (Attribution NonCommercial)
• Placebo blocker   by XKCD   CC BY-NC (Attribution NonCommercial)
• Rosenthal, R., & Rosnow, R. L. (1976). The volunteer subject . New York, NY: Wiley. ↵
• Guéguen, N., & de Gail, Marie-Agnès. (2003). The effect of smiling on helping behaviour: Smiling and good Samaritan behaviour. Communication Reports, 16 , 133–140. ↵
• Rosenthal, R. (1976). Experimenter effects in behavioural research (enlarged ed.). New York, NY: Wiley. ↵
• Ibolya, K., Brake, A., & Voss, U. (2004). The effect of experimenter characteristics on pain reports in women and men. Pain, 112 , 142–147. ↵
• Rosenthal, R., & Fode, K. (1963). The effect of experimenter bias on performance of the albino rat. Behavioural Science, 8 , 183-189. ↵

An established group of people who have agreed to be contacted about participating in research studies.

A source of variation in which the experimenter’s expectations about how participants “should” be have in the experiment.

An experiment in which both the participants and the experimenters are blind to which condition the participants have been assigned to.

A small-scale study conducted to make sure that a new procedure works as planned.

Research Methods in Psychology - 2nd Canadian Edition Copyright © 2015 by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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