control variable in an experiment

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Control Variables: Definition, Uses & Examples

By Jim Frost 4 Comments

What is a Control Variable?

Control variables, also known as controlled variables, are properties that researchers hold constant for all observations in an experiment. While these variables are not the primary focus of the research, keeping their values consistent helps the study establish the true relationships between the independent and dependent variables. The capacity to control variables directly is highest in experiments that researchers conduct under lab conditions. In observational studies, researchers can’t directly control the variables. Instead, they record the values of control variables and then use statistical procedures to account for them.

In science, researchers assess the effects that the independent variables have on the dependent variable. However, other variables can also affect the outcome. If the scientists do not control these other variables, they can distort the primary results of interest. In other words, left uncontrolled, those other factors become confounders that can bias the findings. The uncontrolled variables may be responsible for the changes in the outcomes rather than your treatment or experimental variables. Consequently, researchers control the values of these other variables.

Suppose you are performing an experiment involving different types of fertilizers and plant growth. Those are your primary variables of interest. However, you also know that soil moisture, sunlight, and temperature affect plant growth. If you don’t hold these variables constant for all observations, they might explain the plant growth differences you observe. Consequently, moisture, sunlight, and temperature are essential control variables for your study.

If you perform the study in a controlled lab setting, you can control these environmental conditions and keep their values constant for all observations in your experiment. That way, if you do see plant growth differences, you can be more confident that the fertilizers caused them.

When researchers use control variables, they should identify them, record their values, and include the details in their write-up. This process helps other researchers understand and replicate the results.

Related posts : Independent and Dependent Variables and Confounding Variables

Control Variables and Internal Validity

By controlling variables, you increase the internal validity of your research. Internal validity is the degree of confidence that a causal relationship exists between the treatment and the difference in outcomes. In other words, how likely is it that your treatment caused the differences you observe? Are the researcher’s conclusions correct? Or, can changes in the outcome be attributed to other causes?

If the relevant variables are not controlled, you might need to attribute the changes to confounders rather than the treatment. Control variables reduce the impact of confounding variables.

Controlled Variable Examples

How to Control Variables in Science

Scientists can control variables using several methods. In some cases, variables can be controlled directly. For example, researchers can control the growing conditions for the fertilizer experiment. Or use standardized procedures and processes for all subjects to reduce other sources of variation. These efforts attempt to eliminate all differences between the treatment and control groups other than the treatments themselves.

However, sometimes that’s not possible. Fortunately, there are other approaches.

Random assignment

In some experiments, there can be too many variables to control. Additionally, the researchers might not even know all the potential confounding variables. In these cases, they can randomly assign subjects to the experimental groups. This process controls variables by averaging out all traits across the experimental groups, making them roughly equivalent when the experiment begins. The randomness helps prevent any systematic differences between the experimental groups. Learn more in my post about Random Assignment in Experiments .

Statistical control

Directly controlled variables and random assignment are methods that equalize the experimental groups. However, they aren’t always feasible. In some cases, there are too many variables to control. In other situations, random assignment might not be possible. Try randomly assigning people to smoking and non-smoking groups!

Fortunately, statistical techniques, such as multiple regression analysis , don’t balance the groups but instead use a model that statistically controls the variables. The model accounts for confounding variables.

In multiple regression analysis, including a variable in the model holds it constant while the treatment variable fluctuates. This process allows you to isolate the role of the treatment while accounting for confounders. You can also use ANOVA and ANCOVA.

For more information, read my posts, When to Use Regression and ANOVA Overview .

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Reader Interactions

July 13, 2024 at 2:19 am

Sir you are doing a good job. much appreciated. Could you please tell us how to read the values of control variables like ranges and what do they mean. For instance how to read this (F=1.83; p= 0.07). Thank YOU

February 28, 2024 at 2:09 pm

In your explanation of control variables you use the example of a research study of plant fertilizers and their growth, wanting to control for moisture, sunshine and temperature. You state “Consequently, moisture, sunlight, and temperature are essential control variables for your study. These variables can be controlled by keeping their values constant for all observations in your experiment. You do not go further as to how you control for these values, particularly when such variables are continually changing. Al Wassler

February 28, 2024 at 2:13 pm

Presumably, this experiment would occur in a lab setting where you can control these variables. Plants would be raised with the same humidity, soil moisture, and light conditions.

I’ll add some text to the article to clarify that. Thanks!

January 26, 2023 at 7:00 pm

I have a question please about when a control variable is also itself part of the dependent variable. I see this referred to in the medical research literature as ‘mathematical coupling’, where – for example – the beats per minute (BPM) is the dependent variable and researchers want to put minutes also as a control variable. This seems to create a problem because ‘minutes’ appears on both sides of the equation, and the medical literature talks about spurious correlation, and the model needing to be redesigned. But do you have a simple text or reference – ideally just plain statistics/OLS rather than linked to medical research – where this could be explained in theory terms ? What goes wrong in the regression when a variable is both a control variable and part of the dependent variable (perhaps as part of a ratio or measurement of change)? I just haven’t found a textbook reference that says definitively ‘you can’t have the same variable in both sides of the regression simultaneously’, so I’m not sure whether this violates OLS and so is something to avoid entirely (with a new model design or different research question) or to live with.

Any help would be great, thank you for your work,

Comments and Questions Cancel reply

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• Knowledge Base

Methodology

• What Is a Controlled Experiment? | Definitions & Examples

What Is a Controlled Experiment? | Definitions & Examples

Published on April 19, 2021 by Pritha Bhandari . Revised on June 22, 2023.

In experiments , researchers manipulate independent variables to test their effects on dependent variables. In a controlled experiment , all variables other than the independent variable are controlled or held constant so they don’t influence the dependent variable.

Controlling variables can involve:

• holding variables at a constant or restricted level (e.g., keeping room temperature fixed).
• measuring variables to statistically control for them in your analyses.
• balancing variables across your experiment through randomization (e.g., using a random order of tasks).

Table of contents

Why does control matter in experiments, methods of control, problems with controlled experiments, other interesting articles, frequently asked questions about controlled experiments.

Control in experiments is critical for internal validity , which allows you to establish a cause-and-effect relationship between variables. Strong validity also helps you avoid research biases , particularly ones related to issues with generalizability (like sampling bias and selection bias .)

• Your independent variable is the color used in advertising.
• Your dependent variable is the price that participants are willing to pay for a standard fast food meal.

Extraneous variables are factors that you’re not interested in studying, but that can still influence the dependent variable. For strong internal validity, you need to remove their effects from your experiment.

• Design and description of the meal,
• Study environment (e.g., temperature or lighting),
• Participant’s frequency of buying fast food,
• Participant’s familiarity with the specific fast food brand,
• Participant’s socioeconomic status.

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You can control some variables by standardizing your data collection procedures. All participants should be tested in the same environment with identical materials. Only the independent variable (e.g., ad color) should be systematically changed between groups.

Other extraneous variables can be controlled through your sampling procedures . Ideally, you’ll select a sample that’s representative of your target population by using relevant inclusion and exclusion criteria (e.g., including participants from a specific income bracket, and not including participants with color blindness).

By measuring extraneous participant variables (e.g., age or gender) that may affect your experimental results, you can also include them in later analyses.

After gathering your participants, you’ll need to place them into groups to test different independent variable treatments. The types of groups and method of assigning participants to groups will help you implement control in your experiment.

Control groups

Controlled experiments require control groups . Control groups allow you to test a comparable treatment, no treatment, or a fake treatment (e.g., a placebo to control for a placebo effect ), and compare the outcome with your experimental treatment.

You can assess whether it’s your treatment specifically that caused the outcomes, or whether time or any other treatment might have resulted in the same effects.

To test the effect of colors in advertising, each participant is placed in one of two groups:

• A control group that’s presented with red advertisements for a fast food meal.
• An experimental group that’s presented with green advertisements for the same fast food meal.

Random assignment

To avoid systematic differences and selection bias between the participants in your control and treatment groups, you should use random assignment .

This helps ensure that any extraneous participant variables are evenly distributed, allowing for a valid comparison between groups .

Random assignment is a hallmark of a “true experiment”—it differentiates true experiments from quasi-experiments .

Masking (blinding)

Masking in experiments means hiding condition assignment from participants or researchers—or, in a double-blind study , from both. It’s often used in clinical studies that test new treatments or drugs and is critical for avoiding several types of research bias .

Sometimes, researchers may unintentionally encourage participants to behave in ways that support their hypotheses , leading to observer bias . In other cases, cues in the study environment may signal the goal of the experiment to participants and influence their responses. These are called demand characteristics . If participants behave a particular way due to awareness of being observed (called a Hawthorne effect ), your results could be invalidated.

Using masking means that participants don’t know whether they’re in the control group or the experimental group. This helps you control biases from participants or researchers that could influence your study results.

You use an online survey form to present the advertisements to participants, and you leave the room while each participant completes the survey on the computer so that you can’t tell which condition each participant was in.

Although controlled experiments are the strongest way to test causal relationships, they also involve some challenges.

Difficult to control all variables

Especially in research with human participants, it’s impossible to hold all extraneous variables constant, because every individual has different experiences that may influence their perception, attitudes, or behaviors.

But measuring or restricting extraneous variables allows you to limit their influence or statistically control for them in your study.

Risk of low external validity

Controlled experiments have disadvantages when it comes to external validity —the extent to which your results can be generalized to broad populations and settings.

The more controlled your experiment is, the less it resembles real world contexts. That makes it harder to apply your findings outside of a controlled setting.

There’s always a tradeoff between internal and external validity . It’s important to consider your research aims when deciding whether to prioritize control or generalizability in your experiment.

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
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

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In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment.
• Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

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.

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.

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Home » Control Variable – Definition, Types and Examples

Control Variable – Definition, Types and Examples

Table of Contents

Control Variable

Definition :

Control variable, also known as a “constant variable,” is a variable that is held constant or fixed during an experiment or study to prevent it from affecting the outcome. In other words, a control variable is a variable that is kept the same or held constant to isolate the effects of the independent variable on the dependent variable.

For example, if you were conducting an experiment to test how temperature affects plant growth, you might want to control variables such as the amount of water, the amount of sunlight, and the type of soil to ensure that these variables do not interfere with the results. By controlling these variables, you can isolate the effect of temperature on plant growth and draw more accurate conclusions from the experiment.

Types of Control Variables

Types of Control Variables are as follows:

Environmental Control Variables

These are variables related to the physical environment in which the experiment is conducted, such as temperature, humidity, light, and sound.

Participant Control Variables

These are variables related to the participants in the experiment, such as age, gender, prior knowledge, or experience.

Experimental Control Variables

These are variables that the researcher manipulates or controls to ensure that they do not affect the outcome of the experiment. For example, in a study on the effects of a new medication, the researcher might control the dosage, frequency, or duration of the treatment.

Procedural Control Variables

These are variables related to the procedures or methods used in the experiment, such as the order in which tasks are completed, the timing of measurements, or the instructions given to participants.

Equipment Control Variables

These are variables related to the equipment or instruments used in the experiment, such as calibration, maintenance, or proper functioning.

How to Control a Variable

To control a variable in a scientific experiment, you need to ensure that it is kept constant or unchanged throughout the experiment. Here are some steps to help you control a variable:

Identify the Variable

Start by identifying the variable that you want to control. This can be an environmental, subject, procedural, or instrumentation variable.

Determine the Level of Control Needed

Depending on the variable, you may need to exert varying levels of control. For example, environmental variables may require you to control the temperature, humidity, and lighting in your experiment, while subject variables may require you to select a specific group of participants that meet certain criteria.

Establish a Standard Level

Determine the standard level or value of the variable that you want to control. For example, if you are controlling the temperature, you may set the temperature to a specific degree and ensure that it is maintained at that level throughout the experiment.

Monitor the Variable

Throughout the experiment, monitor the variable to ensure that it remains constant. Use appropriate equipment or instruments to measure the variable and make adjustments as necessary to maintain the desired level.

Document the Process

Document the process of controlling the variable to ensure that the experiment is replicable. This includes documenting the standard level, monitoring procedures, and any adjustments made during the experiment.

Examples of Control Variables

Here are some examples of control variables in Scientific Experiments and Research:

• Environmental Control Variables Example: Suppose you are conducting an experiment to study the effect of light on plant growth. You would want to control environmental factors such as temperature, humidity, and soil nutrients. In this case, you might keep the temperature and humidity constant and use the same type and amount of soil for all the plants.
• Subject Control Variables Example : If you are conducting an experiment to study the effect of a new medication on blood pressure, you would want to control subject variables such as age, gender, and health status. In this case, you might select a group of participants with similar ages, genders, and health conditions to ensure that these variables do not affect the results.
• Procedural Control Variables Example : Suppose you are conducting an experiment to study the effect of distraction on reaction time. You would want to control procedural variables such as the time of day, the order of the tasks, and the instructions given to the participants. In this case, you might ensure that all participants perform the tasks in the same order, at the same time of day, and receive the same instructions.
• Instrumentation Control Variables Example : If you are conducting an experiment to study the effect of a new measurement device on the accuracy of readings, you would want to control instrumentation variables such as the type and calibration of the device. In this case, you might use the same type and model of the device and ensure that it is calibrated before each use.

Applications of Control Variable

Control variables are widely used in scientific research across various fields, including physics, biology, psychology, and engineering. Here are some applications of control variables:

• In medical research , control variables are used to ensure that any observed effects of a new treatment or medication are due to the treatment and not some other variable. By controlling subject variables such as age, gender, and health status, researchers can isolate the effects of the treatment and determine its effectiveness.
• In environmental research , control variables are used to study the effects of changes in the environment on various species or ecosystems. By controlling environmental variables such as temperature, humidity, and lighting, researchers can determine how different species adapt to changes in the environment.
• In psychology research, control variables are used to study the effects of different interventions or therapies on cognitive or behavioral outcomes. By controlling procedural variables such as the order of tasks, the length of time allotted for each task, and the instructions given to participants, researchers can isolate the effects of the intervention and determine its effectiveness.
• In engineering research, control variables are used to study the effects of different design parameters on the performance of a system or device. By controlling instrumentation variables such as the type of measurement device used and the calibration of instruments, researchers can ensure that the measurements are accurate and reliable.

Purpose of Control Variable

The purpose of a control variable in an experiment is to ensure that any observed changes or effects are a result of the manipulation of the independent variable and not some other variable. By keeping certain variables constant, researchers can isolate the effects of the independent variable and determine whether it has a significant effect on the dependent variable.

Control variables are important because they help to increase the reliability and validity of the experiment. Reliability refers to the consistency and reproducibility of the results, while validity refers to the accuracy and truthfulness of the results. By controlling variables, researchers can reduce the potential for extraneous or confounding variables that can affect the outcome of the experiment and increase the likelihood that the results accurately reflect the effect of the independent variable on the dependent variable.

Characteristics of Control Variable

Control variables have the following characteristics:

• Constant : Control variables are kept constant or unchanged throughout the experiment. This means that their values do not vary or change during the experiment. Keeping control variables constant helps to ensure that any observed effects or changes are due to the manipulation of the independent variable and not some other variable.
• Independent : Control variables are independent of the independent variable being studied. This means that they do not affect the relationship between the independent and dependent variables. By controlling for independent variables, researchers can isolate the effect of the independent variable and determine its impact on the dependent variable.
• Documented: Control variables are documented in the experiment. This means that their values and methods of control are recorded and reported in the results section of the research paper. By documenting control variables, researchers can demonstrate the rigor and transparency of their study and allow other researchers to replicate their methods.
• Relevant: Control variables are relevant to the research question. This means that they are chosen based on their potential to affect the outcome of the experiment. By selecting relevant control variables, researchers can reduce the potential for extraneous or confounding variables that can affect the outcome of the experiment and increase the reliability and validity of the results.
• Varied : Control variables can be varied across different conditions or groups. This means that different levels of control may be needed depending on the research question or hypothesis being tested. By varying control variables, researchers can test different hypotheses and determine the factors that affect the outcome of the experiment.

Advantages of Control Variable

The advantages of using control variables in an experiment are:

• Increased accuracy : Control variables help to increase the accuracy of the results by reducing the potential for extraneous or confounding variables that can affect the outcome of the experiment. By controlling for these variables, researchers can isolate the effect of the independent variable on the dependent variable and determine whether it has a significant impact.
• Increased reliability : Control variables help to increase the reliability of the results by reducing the variability in the experiment. By keeping certain variables constant, researchers can ensure that any observed changes or effects are due to the manipulation of the independent variable and not some other variable.
• Reproducibility: Control variables help to increase the reproducibility of the results by ensuring that the same results can be obtained when the experiment is repeated. By documenting and reporting control variables, researchers can demonstrate the rigor and transparency of their study and allow other researchers to replicate their methods.
• Generalizability : Control variables help to increase the generalizability of the results by reducing the potential for bias and increasing the external validity of the experiment. By controlling for relevant variables, researchers can ensure that their findings are applicable to a broader population or context.
• Causality : Control variables help to establish causality by ensuring that any observed changes or effects are due to the manipulation of the independent variable and not some other variable. By controlling for confounding variables, researchers can increase the internal validity of the experiment and establish a cause-and-effect relationship between the independent and dependent variables.

Disadvantages of Control Variable

There are some potential disadvantages or limitations of using control variables in an experiment:

• Complexity : Controlling for multiple variables can make an experiment more complex and time-consuming. This can increase the likelihood of errors and reduce the feasibility of the experiment, especially if the control variables require a lot of resources or are difficult to measure.
• Artificiality : Controlling for variables can make the experimental conditions artificial and not reflective of real-world situations. This can reduce the external validity of the experiment and limit the generalizability of the findings to real-world settings.
• Limited scope : Controlling for specific variables can limit the scope of the experiment and make it difficult to generalize the results to other situations or populations. This can reduce the external validity of the experiment and limit its practical applications.
• Assumptions: Controlling for variables requires making assumptions about which variables are relevant and how they should be controlled. These assumptions may not be valid or accurate, and the results of the experiment may be affected by uncontrolled variables that were not considered.
• Cost : Controlling for variables can be costly, especially if the control variables require additional resources or equipment. This can limit the feasibility of the experiment, especially for researchers with limited funding or resources.

Limitations of Control Variable

There are several limitations of using control variables in an experiment, including:

• Not all variables can be controlled : There may be some variables that cannot be controlled or manipulated in an experiment. For example, some variables may be too difficult or expensive to measure or control, or they may be affected by factors outside of the researcher’s control.
• Interaction effects : Control variables can interact with each other, which can lead to unexpected results. For example, controlling for one variable may have a different effect when another variable is also controlled, or when the two variables interact with each other. These interaction effects can be difficult to predict or control for.
• Over-reliance on statistical significance: Controlling for variables can increase the statistical significance of the results, but this may not always translate to practical significance or real-world significance. Researchers should interpret the results of an experiment in light of the practical significance, not just the statistical significance.
• Limited generalizability : Controlling for variables can limit the generalizability of the results to other populations or situations. If the control variables are not representative of other populations or situations, the results of the experiment may not be applicable to those contexts.
• May mask important effects : Controlling for variables can mask important effects that are related to the independent variable. By controlling for certain variables, researchers may miss important interactions between the independent variable and the controlled variable, which can limit the understanding of the causal relationship between the two.

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Biology archive

Course: biology archive   >   unit 1.

• The scientific method

Controlled experiments

• The scientific method and experimental design

Introduction

How are hypotheses tested.

• One pot of seeds gets watered every afternoon.
• The other pot of seeds doesn't get any water at all.

Control and experimental groups

Independent and dependent variables, independent variables, dependent variables, variability and repetition, controlled experiment case study: co 2 ‍   and coral bleaching.

• What your control and experimental groups would be
• What your independent and dependent variables would be
• What results you would predict in each group

Experimental setup

• Some corals were grown in tanks of normal seawater, which is not very acidic ( pH ‍   around 8.2 ‍   ). The corals in these tanks served as the control group .
• Other corals were grown in tanks of seawater that were more acidic than usual due to addition of CO 2 ‍   . One set of tanks was medium-acidity ( pH ‍   about 7.9 ‍   ), while another set was high-acidity ( pH ‍   about 7.65 ‍   ). Both the medium-acidity and high-acidity groups were experimental groups .
• In this experiment, the independent variable was the acidity ( pH ‍   ) of the seawater. The dependent variable was the degree of bleaching of the corals.
• The researchers used a large sample size and repeated their experiment. Each tank held 5 ‍   fragments of coral, and there were 5 ‍   identical tanks for each group (control, medium-acidity, and high-acidity). Note: None of these tanks was "acidic" on an absolute scale. That is, the pH ‍   values were all above the neutral pH ‍   of 7.0 ‍   . However, the two groups of experimental tanks were moderately and highly acidic to the corals , that is, relative to their natural habitat of plain seawater.

Analyzing the results

Non-experimental hypothesis tests, case study: coral bleaching and temperature, attribution:, works cited:.

• Hoegh-Guldberg, O. (1999). Climate change, coral bleaching, and the future of the world's coral reefs. Mar. Freshwater Res. , 50 , 839-866. Retrieved from www.reef.edu.au/climate/Hoegh-Guldberg%201999.pdf.
• Anthony, K. R. N., Kline, D. I., Diaz-Pulido, G., Dove, S., and Hoegh-Guldberg, O. (2008). Ocean acidification causes bleaching and productivity loss in coral reef builders. PNAS , 105 (45), 17442-17446. http://dx.doi.org/10.1073/pnas.0804478105 .
• University of California Museum of Paleontology. (2016). Misconceptions about science. In Understanding science . Retrieved from http://undsci.berkeley.edu/teaching/misconceptions.php .
• Hoegh-Guldberg, O. and Smith, G. J. (1989). The effect of sudden changes in temperature, light and salinity on the density and export of zooxanthellae from the reef corals Stylophora pistillata (Esper, 1797) and Seriatopora hystrix (Dana, 1846). J. Exp. Mar. Biol. Ecol. , 129 , 279-303. Retrieved from http://www.reef.edu.au/ohg/res-pic/HG%20papers/HG%20and%20Smith%201989%20BLEACH.pdf .

Additional references:

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• Knowledge Base
• Methodology
• Controlled Experiments | Methods & Examples of Control

Controlled Experiments | Methods & Examples of Control

Published on 19 April 2022 by Pritha Bhandari . Revised on 10 October 2022.

In experiments , researchers manipulate independent variables to test their effects on dependent variables. In a controlled experiment , all variables other than the independent variable are controlled or held constant so they don’t influence the dependent variable.

Controlling variables can involve:

• Holding variables at a constant or restricted level (e.g., keeping room temperature fixed)
• Measuring variables to statistically control for them in your analyses
• Balancing variables across your experiment through randomisation (e.g., using a random order of tasks)

Table of contents

Why does control matter in experiments, methods of control, problems with controlled experiments, frequently asked questions about controlled experiments.

Control in experiments is critical for internal validity , which allows you to establish a cause-and-effect relationship between variables.

• Your independent variable is the colour used in advertising.
• Your dependent variable is the price that participants are willing to pay for a standard fast food meal.

Extraneous variables are factors that you’re not interested in studying, but that can still influence the dependent variable. For strong internal validity, you need to remove their effects from your experiment.

• Design and description of the meal
• Study environment (e.g., temperature or lighting)
• Participant’s frequency of buying fast food
• Participant’s familiarity with the specific fast food brand
• Participant’s socioeconomic status

Prevent plagiarism, run a free check.

You can control some variables by standardising your data collection procedures. All participants should be tested in the same environment with identical materials. Only the independent variable (e.g., advert colour) should be systematically changed between groups.

Other extraneous variables can be controlled through your sampling procedures . Ideally, you’ll select a sample that’s representative of your target population by using relevant inclusion and exclusion criteria (e.g., including participants from a specific income bracket, and not including participants with colour blindness).

By measuring extraneous participant variables (e.g., age or gender) that may affect your experimental results, you can also include them in later analyses.

After gathering your participants, you’ll need to place them into groups to test different independent variable treatments. The types of groups and method of assigning participants to groups will help you implement control in your experiment.

Control groups

Controlled experiments require control groups . Control groups allow you to test a comparable treatment, no treatment, or a fake treatment, and compare the outcome with your experimental treatment.

You can assess whether it’s your treatment specifically that caused the outcomes, or whether time or any other treatment might have resulted in the same effects.

• A control group that’s presented with red advertisements for a fast food meal
• An experimental group that’s presented with green advertisements for the same fast food meal

Random assignment

To avoid systematic differences between the participants in your control and treatment groups, you should use random assignment .

This helps ensure that any extraneous participant variables are evenly distributed, allowing for a valid comparison between groups .

Random assignment is a hallmark of a ‘true experiment’ – it differentiates true experiments from quasi-experiments .

Masking (blinding)

Masking in experiments means hiding condition assignment from participants or researchers – or, in a double-blind study , from both. It’s often used in clinical studies that test new treatments or drugs.

Sometimes, researchers may unintentionally encourage participants to behave in ways that support their hypotheses. In other cases, cues in the study environment may signal the goal of the experiment to participants and influence their responses.

Using masking means that participants don’t know whether they’re in the control group or the experimental group. This helps you control biases from participants or researchers that could influence your study results.

Although controlled experiments are the strongest way to test causal relationships, they also involve some challenges.

Difficult to control all variables

Especially in research with human participants, it’s impossible to hold all extraneous variables constant, because every individual has different experiences that may influence their perception, attitudes, or behaviors.

But measuring or restricting extraneous variables allows you to limit their influence or statistically control for them in your study.

Risk of low external validity

Controlled experiments have disadvantages when it comes to external validity – the extent to which your results can be generalised to broad populations and settings.

The more controlled your experiment is, the less it resembles real world contexts. That makes it harder to apply your findings outside of a controlled setting.

There’s always a tradeoff between internal and external validity . It’s important to consider your research aims when deciding whether to prioritise control or generalisability in your experiment.

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

• A testable hypothesis
• One or more independent variables that you will manipulate
• One or more dependent variables that you will measure

When designing the experiment, first decide:

• How your variable(s) will be manipulated
• How you will control for any potential confounding or lurking variables
• How many subjects you will include
• How you will assign treatments to your subjects

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Independent and Dependent Variables Examples

The independent and dependent variables are key to any scientific experiment, but how do you tell them apart? Here are the definitions of independent and dependent variables, examples of each type, and tips for telling them apart and graphing them.

Independent Variable

The independent variable is the factor the researcher changes or controls in an experiment. It is called independent because it does not depend on any other variable. The independent variable may be called the “controlled variable” because it is the one that is changed or controlled. This is different from the “ control variable ,” which is variable that is held constant so it won’t influence the outcome of the experiment.

Dependent Variable

The dependent variable is the factor that changes in response to the independent variable. It is the variable that you measure in an experiment. The dependent variable may be called the “responding variable.”

Examples of Independent and Dependent Variables

Here are several examples of independent and dependent variables in experiments:

• In a study to determine whether how long a student sleeps affects test scores, the independent variable is the length of time spent sleeping while the dependent variable is the test score.
• You want to know which brand of fertilizer is best for your plants. The brand of fertilizer is the independent variable. The health of the plants (height, amount and size of flowers and fruit, color) is the dependent variable.
• You want to compare brands of paper towels, to see which holds the most liquid. The independent variable is the brand of paper towel. The dependent variable is the volume of liquid absorbed by the paper towel.
• You suspect the amount of television a person watches is related to their age. Age is the independent variable. How many minutes or hours of television a person watches is the dependent variable.
• You think rising sea temperatures might affect the amount of algae in the water. The water temperature is the independent variable. The mass of algae is the dependent variable.
• In an experiment to determine how far people can see into the infrared part of the spectrum, the wavelength of light is the independent variable and whether the light is observed is the dependent variable.
• If you want to know whether caffeine affects your appetite, the presence/absence or amount of caffeine is the independent variable. Appetite is the dependent variable.
• You want to know which brand of microwave popcorn pops the best. The brand of popcorn is the independent variable. The number of popped kernels is the dependent variable. Of course, you could also measure the number of unpopped kernels instead.
• You want to determine whether a chemical is essential for rat nutrition, so you design an experiment. The presence/absence of the chemical is the independent variable. The health of the rat (whether it lives and reproduces) is the dependent variable. A follow-up experiment might determine how much of the chemical is needed. Here, the amount of chemical is the independent variable and the rat health is the dependent variable.

How to Tell the Independent and Dependent Variable Apart

If you’re having trouble identifying the independent and dependent variable, here are a few ways to tell them apart. First, remember the dependent variable depends on the independent variable. It helps to write out the variables as an if-then or cause-and-effect sentence that shows the independent variable causes an effect on the dependent variable. If you mix up the variables, the sentence won’t make sense. Example : The amount of eat (independent variable) affects how much you weigh (dependent variable).

This makes sense, but if you write the sentence the other way, you can tell it’s incorrect: Example : How much you weigh affects how much you eat. (Well, it could make sense, but you can see it’s an entirely different experiment.) If-then statements also work: Example : If you change the color of light (independent variable), then it affects plant growth (dependent variable). Switching the variables makes no sense: Example : If plant growth rate changes, then it affects the color of light. Sometimes you don’t control either variable, like when you gather data to see if there is a relationship between two factors. This can make identifying the variables a bit trickier, but establishing a logical cause and effect relationship helps: Example : If you increase age (independent variable), then average salary increases (dependent variable). If you switch them, the statement doesn’t make sense: Example : If you increase salary, then age increases.

How to Graph Independent and Dependent Variables

Plot or graph independent and dependent variables using the standard method. The independent variable is the x-axis, while the dependent variable is the y-axis. Remember the acronym DRY MIX to keep the variables straight: D = Dependent variable R = Responding variable/ Y = Graph on the y-axis or vertical axis M = Manipulated variable I = Independent variable X = Graph on the x-axis or horizontal axis

• Babbie, Earl R. (2009). The Practice of Social Research (12th ed.) Wadsworth Publishing. ISBN 0-495-59841-0.
• di Francia, G. Toraldo (1981). The Investigation of the Physical World . Cambridge University Press. ISBN 978-0-521-29925-1.
• Gauch, Hugh G. Jr. (2003). Scientific Method in Practice . Cambridge University Press. ISBN 978-0-521-01708-4.
• Popper, Karl R. (2003). Conjectures and Refutations: The Growth of Scientific Knowledge . Routledge. ISBN 0-415-28594-1.

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What Are Dependent, Independent & Controlled Variables?

What Is a Responding Variable in Science Projects?

Say you're in lab, and your teacher asks you to design an experiment. The experiment must test how plants grow in response to different colored light. How would you begin? What are you changing? What are you keeping the same? What are you measuring?

These parameters of what you would change and what you would keep the same are called variables. Take a look at how all of these parameters in an experiment are defined, as independent, dependent and controlled variables.

What Is a Variable?

A variable is any quantity that you are able to measure in some way. This could be temperature, height, age, etc. Basically, a variable is anything that contributes to the outcome or result of your experiment in any way.

In an experiment there are multiple kinds of variables: independent, dependent and controlled variables.

What Is an Independent Variable?

An independent variable is the variable the experimenter controls. Basically, it is the component you choose to change in an experiment. This variable is not dependent on any other variables.

For example, in the plant growth experiment, the independent variable is the light color. The light color is not affected by anything. You will choose different light colors like green, red, yellow, etc. You are not measuring the light.

What Is a Dependent Variable?

A dependent variable is the measurement that changes in response to what you changed in the experiment. This variable is dependent on other variables; hence the name! For example, in the plant growth experiment, the dependent variable would be plant growth.

You could measure this by measuring how much the plant grows every two days. You could also measure it by measuring the rate of photosynthesis. Either of these measurements are dependent upon the kind of light you give the plant.

What Are Controlled Variables?

A control variable in science is any other parameter affecting your experiment that you try to keep the same across all conditions.

For example, one control variable in the plant growth experiment could be temperature. You would not want to have one plant growing in green light with a temperature of 20°C while another plant grows in red light with a temperature of 27°C.

You want to measure only the effect of light, not temperature. For this reason you would want to keep the temperature the same across all of your plants. In other words, you would want to control the temperature.

Another example is the amount of water you give the plant. If one plant receives twice the amount of water as another plant, there would be no way for you to know that the reason those plants grew the way they did is due only to the light color their received.

The observed effect could also be due in part to the amount of water they got. A control variable in science experiments is what allows you to compare other things that may be contributing to a result because you have kept other important things the same across all of your subjects.

Graphing Your Experiment

When graphing the results of your experiment, it is important to remember which variable goes on which axis.

The independent variable is graphed on the x-axis . The dependent variable , which changes in response to the independent variable, is graphed on the y-axis . Controlled variables are usually not graphed because they should not change. They could, however, be graphed as a verification that other conditions are not changing.

For example, after graphing the growth as compared to light, you could also look at how the temperature varied across different conditions. If you notice that it did vary quite a bit, you may need to go back and look at your experimental setup: How could you improve the experiment so that all plants are exposed to as similar an environment as possible (aside from the light color)?

How to Remember Which is Which

In order to try and remember which is the dependent variable and which is the independent variable, try putting them into a sentence which uses "causes a change in."

Here's an example. Saying, "light color causes a change in plant growth," is possible. This shows us that the independent variable affects the dependent variable. The inverse, however, is not true. "Plant growth causes a change in light color," is not possible. This way you know which is the independent variable and which is the dependent variable!

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Riti Gupta holds a Honors Bachelors degree in Biochemistry from the University of Oregon and a PhD in biology from Johns Hopkins University. She has an interest in astrobiology and manned spaceflight. She has over 10 years of biology research experience in academia. She currently teaches classes in biochemistry, biology, biophysics, astrobiology, as well as high school AP Biology and Chemistry test prep.

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Controlling Variables

by Anthony Carpi, Ph.D., Anne E. Egger, Ph.D.

This material is excerpted from a teaching module on the Visionlearning website, to view this material in context, please visit Research Methods: Experimentation.

Controlling variables is an important part of experimental design. Controlled variables refer to variables or contributing factors that are fixed or eliminated in order to clearly identify the relationship between an independent variable and a dependent variable . For example, in an experiment designed to quantify the effect of vitamin A dose on the metabolism of beta-carotene in humans, Shawna Lemke and colleagues had to precisely control the diet of their human volunteers (Lemke, Dueker et al. 2003). They asked their participants to limit their intake of foods rich in vitamin A and further asked that they maintain a precise log of all foods eaten for 1 week prior to their study. At the time of their study, they controlled their participants’ diet by feeding them all the same meals, described in the methods section of their research article in this way, “Meals were controlled for time and content on the dose administration day. Lunch was served at 5.5 h postdosing and consisted of a frozen dinner (Enchiladas, Amy's Kitchen, Petaluma, CA), a blueberry bagel with jelly, 1 apple and 1 banana, and a large chocolate chunk cookie (Pepperidge Farm). Dinner was served 10.5 h post dose and consisted of a frozen dinner (Chinese Stir Fry, Amy's Kitchen) plus the bagel and fruit taken for lunch.”

Controlling variables is important because slight variations in the experimental set-up could strongly affect the outcome being measured. For example, during the 1950s, a number of experiments were conducted to evaluate the toxicity in mammals of the metal molybdenum, using rats as experimental subjects . Unexpectedly, these experiments seemed to indicate that the type of cage the rats were housed in affected the toxicity of molybdenum. In response, G. Brinkman and Russell Miller set up an experiment to investigate this observation (Brinkman & Miller, 1961). Brinkman and Miller fed two groups of rats a normal diet that was supplemented with 200 parts per million (ppm) of molybdenum. One group of rats was housed in galvanized steel (steel coated with zinc to reduce corrosion) cages and the second group was housed in stainless steel cages. Rats housed in the galvanized steel cages suffered more from molybdenum toxicity than the other group: they had higher concentrations of molybdenum in their livers and lower blood hemoglobin levels. It was then shown that when the rats chewed on their cages, those housed in the galvanized metal cages absorbed zinc plated onto the metal bars and zinc is now known to affect the toxicity of molybdenum. In order to control for zinc exposure, then, stainless steel cages needed to be used for all rats.

While controlling variables is an important aspect of making an experiment manageable and informative, it is often not representative of the real world, in which many variables may change at once, including the foods you eat. Still, experimental research is an excellent way of determining relationships between variables that can be later validated in real world settings through descriptive or comparative studies.

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Control Variable: Simple Definition

Types of Variables > Control Variable

An experiment has several types of variables , including a control variable (sometimes called a controlled variable). Variables are just values that can change; a good experiment only has two changing variables: the independent variable and dependent variable . Let’s say you are testing to see how the amount of light received affects plant growth:

• The independent variable , in this case the amount of light, is changed by you, the researcher.
• As you change the independent variable, you watch what happens to the dependent variable . In this case you see how much the plants grow.
• A control variable is another factor in an experiment; it must be held constant. In the plant growth experiment, this may be factors like water and fertilizer levels.

The Control Variable and Experimental Design

Control Variables vs. Control Groups

In any experiment or research, it can be virtually impossible to account for all variables that may affect the outcome of your experiment. If it’s difficult to identify and control all potential confounding variables, it may be necessary to make a control group . A control group provides a baseline measurement for your experiment.

Independent, Dependent and Controlled Variables

This is part of the NSW HSC science curriculum part of the Working Scientifically skills.

What are Independent, Dependent, and Controlled Variables?

As a high school science student, you are likely to come across different types of variables in your experiments. Being able to recognise these variables is a skill which is included in the NSW Higher School Certificate (HSC) curriculum. These variables are essential to scientific inquiry as they help us understand how different factors affect the outcomes of experiments. There are three main types of variables in scientific investigations: independent, dependent, and controlled variables. We will explore each of these variables and their importance in scientific inquiry. 

Independent Variables

Independent variables are the variables that are manipulated or changed by the researcher in an experiment. They are also known as the input variables or the cause variables because they are the factors that cause changes in the dependent variable.

For example, if you were investigating the effect of temperature on the rate of photosynthesis in plants, temperature would be the independent variable. You would manipulate the temperature to see how it affects the rate of photosynthesis.

It is essential to note that an experiment should have only one independent variable. This is because if you change more than one variable, you will not know which variable caused the change in the dependent variable. Therefore, by controlling the independent variable, you can determine the effect of that variable on the dependent variable.

Dependent variables

Dependent variables are the variables that are affected by the independent variable in an experiment. They are also known as the outcome variables or the effect variables. The dependent variable is what you measure or observe to determine the effect of the independent variable.

For example, in the temperature and photosynthesis experiment, the dependent variable would be the rate of photosynthesis, which is affected by changes in temperature.

It is crucial to keep the dependent variable constant during an experiment to ensure that any changes observed are a result of changes in the independent variable. Additionally, the dependent variable should be measurable and quantitative, meaning that it can be expressed in numerical values.

Controlled variables

Controlled variables are the variables that are kept constant during an experiment to ensure that they do not affect the outcome. These variables are also known as constant variables or the controlled factors. The purpose of controlling these variables is to ensure that any changes observed in the dependent variable are due to changes in the independent variable and not due to other factors.

For example, in the temperature and photosynthesis experiment, the controlled variables would include factors such as the type of plant, the amount of light, and the amount of carbon dioxide. By keeping these variables constant, you can ensure that any changes in the rate of photosynthesis are due to changes in temperature and not due to other factors.

Identifying variables

Let's consider a scenario where we want to investigate the effect of different amounts of water on plant growth. In this case:

Independent variable: The independent variable in this experiment is the amount of water used to water the plants. We could use different amounts of water, such as 100 ml, 200 ml, or 300 ml.

Dependent variable: The dependent variable is still the growth of the plants, which we could measure by tracking the height, weight, or number of leaves of the plants.

Controlled variables: Some controlled variables in this experiment might include the type and species of plants used, the type and amount of soil used, the size and type of pots used, and the amount of sunlight and temperature that the plants are exposed to.

By identifying and controlling these variables, we can design a more controlled and rigorous experiment to investigate the effect of different amounts of water on plant growth.

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Experimental Design - Independent, Dependent, and Controlled Variables

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Scientific experiments are meant to show cause and effect of a phenomena (relationships in nature).  The “ variables ” are any factor, trait, or condition that can be changed in the experiment and that can have an effect on the outcome of the experiment.

An experiment can have three kinds of variables: i ndependent, dependent, and controlled .

• The independent variable is one single factor that is changed by the scientist followed by observation to watch for changes. It is important that there is just one independent variable, so that results are not confusing.
• The dependent variable is the factor that changes as a result of the change to the independent variable.
• The controlled variables (or constant variables) are factors that the scientist wants to remain constant if the experiment is to show accurate results. To be able to measure results, each of the variables must be able to be measured.

For example, let’s design an experiment with two plants sitting in the sun side by side. The controlled variables (or constants) are that at the beginning of the experiment, the plants are the same size, get the same amount of sunlight, experience the same ambient temperature and are in the same amount and consistency of soil (the weight of the soil and container should be measured before the plants are added). The independent variable is that one plant is getting watered (1 cup of water) every day and one plant is getting watered (1 cup of water) once a week. The dependent variables are the changes in the two plants that the scientist observes over time.

Can you describe the dependent variable that may result from this experiment? After four weeks, the dependent variable may be that one plant is taller, heavier and more developed than the other. These results can be recorded and graphed by measuring and comparing both plants’ height, weight (removing the weight of the soil and container recorded beforehand) and a comparison of observable foliage.

Using What You Learned: Design another experiment using the two plants, but change the independent variable. Can you describe the dependent variable that may result from this new experiment?

Think of another simple experiment and name the independent, dependent, and controlled variables. Use the graphic organizer included in the PDF below to organize your experiment's variables.

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Control Group vs Experimental Group

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In a controlled experiment , scientists compare a control group, and an experimental group is identical in all respects except for one difference – experimental manipulation.

Differences

Unlike the experimental group, the control group is not exposed to the independent variable under investigation. So, it provides a baseline against which any changes in the experimental group can be compared.

Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.

Almost all experimental studies are designed to include a control group and one or more experimental groups. In most cases, participants are randomly assigned to either a control or experimental group.

Because participants are randomly assigned to either group, we can assume that the groups are identical except for manipulating the independent variable in the experimental group.

It is important that every aspect of the experimental environment is the same and that the experimenters carry out the exact same procedures with both groups so researchers can confidently conclude that any differences between groups are actually due to the difference in treatments.

Control Group

A control group consists of participants who do not receive any experimental treatment. The control participants serve as a comparison group.

The control group is matched as closely as possible to the experimental group, including age, gender, social class, ethnicity, etc.

The difference between the control and experimental groups is that the control group is not exposed to the independent variable , which is thought to be the cause of the behavior being investigated.

Researchers will compare the individuals in the control group to those in the experimental group to isolate the independent variable and examine its impact.

The control group is important because it serves as a baseline, enabling researchers to see what impact changes to the independent variable produce and strengthening researchers’ ability to draw conclusions from a study.

Without the presence of a control group, a researcher cannot determine whether a particular treatment truly has an effect on an experimental group.

Control groups are critical to the scientific method as they help ensure the internal validity of a study.

Assume you want to test a new medication for ADHD . One group would receive the new medication, and the other group would receive a pill that looked exactly the same as the one that the others received, but it would be a placebo. The group that takes the placebo would be the control group.

Types of Control Groups

Positive control group.

• A positive control group is an experimental control that will produce a known response or the desired effect.
• A positive control is used to ensure a test’s success and confirm an experiment’s validity.
• For example, when testing for a new medication, an already commercially available medication could serve as the positive control.

Negative Control Group

• A negative control group is an experimental control that does not result in the desired outcome of the experiment.
• A negative control is used to ensure that there is no response to the treatment and help identify the influence of external factors on the test.
• An example of a negative control would be using a placebo when testing for a new medication.

Experimental Group

An experimental group consists of participants exposed to a particular manipulation of the independent variable. These are the participants who receive the treatment of interest.

Researchers will compare the responses of the experimental group to those of a control group to see if the independent variable impacted the participants.

An experiment must have at least one control group and one experimental group; however, a single experiment can include multiple experimental groups, which are all compared against the control group.

Having multiple experimental groups enables researchers to vary different levels of an experimental variable and compare the effects of these changes to the control group and among each other.

Assume you want to study to determine if listening to different types of music can help with focus while studying.

You randomly assign participants to one of three groups: one group that listens to music with lyrics, one group that listens to music without lyrics, and another group that listens to no music.

The group of participants listening to no music while studying is the control group, and the groups listening to music, whether with or without lyrics, are the two experimental groups.

Frequently Asked Questions

1. what is the difference between the control group and the experimental group in an experimental study.

Put simply; an experimental group is a group that receives the variable, or treatment, that the researchers are testing, whereas the control group does not. These two groups should be identical in all other aspects.

2. What is the purpose of a control group in an experiment

A control group is essential in experimental research because it:

Provides a baseline against which the effects of the manipulated variable (the independent variable) can be measured.

Helps to ensure that any changes observed in the experimental group are indeed due to the manipulation of the independent variable and not due to other extraneous or confounding factors.

Helps to account for the placebo effect, where participants’ beliefs about the treatment can influence their behavior or responses.

In essence, it increases the internal validity of the results and the confidence we can have in the conclusions.

3. Do experimental studies always need a control group?

Not all experiments require a control group, but a true “controlled experiment” does require at least one control group. For example, experiments that use a within-subjects design do not have a control group.

In  within-subjects designs , all participants experience every condition and are tested before and after being exposed to treatment.

These experimental designs tend to have weaker internal validity as it is more difficult for a researcher to be confident that the outcome was caused by the experimental treatment and not by a confounding variable.

4. Can a study include more than one control group?

Yes, studies can include multiple control groups. For example, if several distinct groups of subjects do not receive the treatment, these would be the control groups.

5. How is the control group treated differently from the experimental groups?

The control group and the experimental group(s) are treated identically except for one key difference: exposure to the independent variable, which is the factor being tested. The experimental group is subjected to the independent variable, whereas the control group is not.

This distinction allows researchers to measure the effect of the independent variable on the experimental group by comparing it to the control group, which serves as a baseline or standard.

Bailey, R. A. (2008). Design of Comparative Experiments. Cambridge University Press. ISBN 978-0-521-68357-9.

Hinkelmann, Klaus; Kempthorne, Oscar (2008). Design and Analysis of Experiments, Volume I: Introduction to Experimental Design (2nd ed.). Wiley. ISBN 978-0-471-72756-9.

What Is a Controlled Experiment?

Definition and Example

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A controlled experiment is one in which everything is held constant except for one variable . Usually, a set of data is taken to be a control group , which is commonly the normal or usual state, and one or more other groups are examined where all conditions are identical to the control group and to each other except for one variable.

Sometimes it's necessary to change more than one variable, but all of the other experimental conditions will be controlled so that only the variables being examined change. And what is measured is the variables' amount or the way in which they change.

Controlled Experiment

• A controlled experiment is simply an experiment in which all factors are held constant except for one: the independent variable.
• A common type of controlled experiment compares a control group against an experimental group. All variables are identical between the two groups except for the factor being tested.
• The advantage of a controlled experiment is that it is easier to eliminate uncertainty about the significance of the results.

Example of a Controlled Experiment

Let's say you want to know if the type of soil affects how long it takes a seed to germinate, and you decide to set up a controlled experiment to answer the question. You might take five identical pots, fill each with a different type of soil, plant identical bean seeds in each pot, place the pots in a sunny window, water them equally, and measure how long it takes for the seeds in each pot to sprout.

This is a controlled experiment because your goal is to keep every variable constant except the type of soil you use. You control these features.

Why Controlled Experiments Are Important

The big advantage of a controlled experiment is that you can eliminate much of the uncertainty about your results. If you couldn't control each variable, you might end up with a confusing outcome.

For example, if you planted different types of seeds in each of the pots, trying to determine if soil type affected germination, you might find some types of seeds germinate faster than others. You wouldn't be able to say, with any degree of certainty, that the rate of germination was due to the type of soil. It might as well have been due to the type of seeds.

Or, if you had placed some pots in a sunny window and some in the shade or watered some pots more than others, you could get mixed results. The value of a controlled experiment is that it yields a high degree of confidence in the outcome. You know which variable caused or did not cause a change.

Are All Experiments Controlled?

No, they are not. It's still possible to obtain useful data from uncontrolled experiments, but it's harder to draw conclusions based on the data.

An example of an area where controlled experiments are difficult is human testing. Say you want to know if a new diet pill helps with weight loss. You can collect a sample of people, give each of them the pill, and measure their weight. You can try to control as many variables as possible, such as how much exercise they get or how many calories they eat.

However, you will have several uncontrolled variables, which may include age, gender, genetic predisposition toward a high or low metabolism, how overweight they were before starting the test, whether they inadvertently eat something that interacts with the drug, etc.

Scientists try to record as much data as possible when conducting uncontrolled experiments, so they can see additional factors that may be affecting their results. Although it is harder to draw conclusions from uncontrolled experiments, new patterns often emerge that would not have been observable in a controlled experiment.

For example, you may notice the diet drug seems to work for female subjects, but not for male subjects, and this may lead to further experimentation and a possible breakthrough. If you had only been able to perform a controlled experiment, perhaps on male clones alone, you would have missed this connection.

• Box, George E. P., et al.  Statistics for Experimenters: Design, Innovation, and Discovery . Wiley-Interscience, a John Wiley & Soncs, Inc., Publication, 2005. 
• Creswell, John W.  Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research . Pearson/Merrill Prentice Hall, 2008.
• Pronzato, L. "Optimal experimental design and some related control problems". Automatica . 2008.
• Robbins, H. "Some Aspects of the Sequential Design of Experiments". Bulletin of the American Mathematical Society . 1952.
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Microbe Notes

Controlled Experiments: Definition, Steps, Results, Uses

Controlled experiments ensure valid and reliable results by minimizing biases and controlling variables effectively.

Rigorous planning, ethical considerations, and precise data analysis are vital for successful experiment execution and meaningful conclusions.

Real-world applications demonstrate the practical impact of controlled experiments, guiding informed decision-making in diverse domains.

Controlled experiments are the systematic research method where variables are intentionally manipulated and controlled to observe the effects of a particular phenomenon. It aims to isolate and measure the impact of specific variables, ensuring a more accurate causality assessment.

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Importance of controlled experiments in various fields

Controlled experiments are significant across diverse fields, including science, psychology, economics, healthcare, and technology.

They provide a systematic approach to test hypotheses, establish cause-and-effect relationships, and validate the effectiveness of interventions or solutions.

Why Controlled Experiments Matter? 

Validity and reliability of results.

Controlled experiments uphold the gold standard for scientific validity and reliability. By meticulously controlling variables and conditions, researchers can attribute observed outcomes accurately to the independent variable being tested. This precision ensures that the findings can be replicated and are trustworthy.

Minimizing Biases and Confounding Variables

One of the core benefits of controlled experiments lies in their ability to minimize biases and confounding variables. Extraneous factors that could distort results are mitigated through careful control and randomization. This enables researchers to isolate the effects of the independent variable, leading to a more accurate understanding of causality.

Achieving Causal Inference

Controlled experiments provide a strong foundation for establishing causal relationships between variables. Researchers can confidently infer causation by manipulating specific variables and observing resulting changes. The capability informs decision-making, policy formulation, and advancements across various fields.

Planning a Controlled Experiment

Formulating research questions and hypotheses.

Formulating clear research questions and hypotheses is paramount at the outset of a controlled experiment. These inquiries guide the direction of the study, defining the variables of interest and setting the stage for structured experimentation.

Well-defined questions and hypotheses contribute to focused research and facilitate meaningful data collection.

Identifying Variables and Control Groups

Identifying and defining independent, dependent, and control variables is fundamental to experimental planning. 

Precise identification ensures that the experiment is designed to isolate the effect of the independent variable while controlling for other influential factors. Establishing control groups allows for meaningful comparisons and robust analysis of the experimental outcomes.

Designing Experimental Procedures and Protocols

Careful design of experimental procedures and protocols is essential for a successful controlled experiment. The step involves outlining the methodology, data collection techniques, and the sequence of activities in the experiment. 

A well-designed experiment is structured to maintain consistency, control, and accuracy throughout the study, thereby enhancing the validity and credibility of the results.

Conducting a Controlled Experiment

Randomization and participant selection.

Randomization is a critical step in ensuring the fairness and validity of a controlled experiment. It involves assigning participants to different experimental conditions in a random and unbiased manner. 

The selection of participants should accurately represent the target population, enhancing the results’ generalizability.

Data Collection Methods and Instruments

Selecting appropriate data collection methods and instruments is pivotal in gathering accurate and relevant data. Researchers often employ surveys, observations, interviews, or specialized tools to record and measure the variables of interest. 

The chosen methods should align with the experiment’s objectives and provide reliable data for analysis.

Monitoring and Maintaining Experimental Conditions

Maintaining consistent and controlled experimental conditions throughout the study is essential. Regular monitoring helps ensure that variables remain constant and uncontaminated, reducing the risk of confounding factors. 

Rigorous monitoring protocols and timely adjustments are crucial for the accuracy and reliability of the experiment.

Analysing Results and Drawing Conclusions

Data analysis techniques.

Data analysis involves employing appropriate statistical and analytical techniques to process the collected data. This step helps derive meaningful insights, identify patterns, and draw valid conclusions. 

Common techniques include regression analysis, t-tests , ANOVA , and more, tailored to the research design and data type .

Interpretation of Results

Interpreting the results entails understanding the statistical outcomes and their implications for the research objectives. 

Researchers analyze patterns, trends, and relationships revealed by the data analysis to infer the experiment’s impact on the variables under study. Clear and accurate interpretation is crucial for deriving actionable insights.

Implications and Potential Applications

Identifying the broader implications and potential applications of the experiment’s results is fundamental. Researchers consider how the findings can inform decision-making, policy development, or further research. 

Understanding the practical implications helps bridge the gap between theoretical insights and real-world application.

Common Challenges and Solutions

Addressing ethical considerations.

Ethical challenges in controlled experiments include ensuring informed consent, protecting participants’ privacy, and minimizing harm. 

Solutions involve thorough ethics reviews, transparent communication with participants, and implementing safeguards to uphold ethical standards throughout the experiment.

Dealing with Sample Size and Statistical Power

The sample size is crucial for achieving statistically significant results. Adequate sample sizes enhance the experiment’s power to detect meaningful effects accurately. 

Statistical power analysis guides researchers in determining the optimal sample size for the experiment, minimizing the risk of type I and II errors .

Mitigating Unforeseen Variables

Unforeseen variables can introduce bias and affect the experiment’s validity. Researchers employ meticulous planning and robust control measures to minimize the impact of unforeseen variables. 

Pre-testing and pilot studies help identify potential confounders, allowing researchers to adapt the experiment accordingly.

A controlled experiment involves meticulous planning, precise execution, and insightful analysis. Adhering to ethical standards, optimizing sample size, and adapting to unforeseen variables are key challenges that require thoughtful solutions. 

Real-world applications showcase the transformative potential of controlled experiments across varied domains, emphasizing their indispensable role in evidence-based decision-making and progress.

• https://www.khanacademy.org/science/biology/intro-to-biology/science-of-biology/a/experiments-and-observations
• https://www.scribbr.com/methodology/controlled-experiment/
• https://link.springer.com/10.1007/978-1-4899-7687-1_891
• http://ai.stanford.edu/~ronnyk/GuideControlledExperiments.pdf
• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6776925/
• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4017459/
• https://www.merriam-webster.com/dictionary/controlled%20experiment

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Controlled Variables

Controlled variables are variables that is sometimes overlooked by researchers, but it is usually far more important than the dependent or independent variables.

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

A failure to isolate the controlled variables, in any experimental design , will seriously compromise the internal validity . This oversight may lead to confounding variables ruining the experiment , wasting time and resources, and damaging the researcher's reputation.

In any experimental design, a researcher will be manipulating one variable , the independent variable , and studying how that affects the dependent variables .

Most experimental designs measures only one or two variables at a time. Any other factor, which could potentially influence the results , must be correctly controlled. Its effect upon the results must be standardized, or eliminated, exerting the same influence upon the different sample groups .

For example, if you were comparing cleaning products, the brand of cleaning product would be the only independent variable measured. The level of dirt and soiling, the type of dirt or stain, the temperature of the water and the time of the cleaning cycle are just some of the variables that must be the same between experiments. Failure to standardize even one of these controlled variables could cause a confounding variable and invalidate the results.

Control Groups

In many fields of science, especially biology and behavioral sciences, it is very difficult to ensure complete control , as there is a lot of scope for small variations.

Biological processes are subject to natural fluctuations and chaotic rhythms. The key is to use established operationalization techniques, such as randomization and double blind experiments . These techniques will control and isolate these variables, as much as possible. If this proves difficult, a control group is used, which will give a baseline measurement for the unknown variables.

Sound statistical analysis will then eliminate these fluctuations from the results. Most statistical tests have a certain error margin built in, and repetition and large sample groups will eradicate the unknown variables.

There still needs to be constant monitoring and checks, but due diligence will ensure that the experiment is as accurate as is possible.

The Value of Consistency

It is important to ensure that all these possible variables are isolated, because a type III error may occur if an unknown factor influences the dependent variable . This is where the null hypothesis is correctly rejected, but for the wrong reason.

In addition, inadequate monitoring of controlled variables is one of the most common causes of researchers wrongly assuming that a correlation leads to causality .

Controlled variables are the road to failure in an experimental design , if not identified and eliminated. Designing the experiment with controls in mind is often more crucial than determining the independent variable .

Poor controls can lead to confounding variables , and will damage the internal validity of the experiment.

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Martyn Shuttleworth (Jun 2, 2008). Controlled Variables. Retrieved Aug 18, 2024 from Explorable.com: https://explorable.com/controlled-variables

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Improving the Investigation, Clearance Rates, and Victim Restoration of Robberies: A Randomized Controlled Experiment, Seattle, Washington, Rochester, New York, 2021-2023 (ICPSR 39101)

Version Date: Jul 30, 2024 View help for published

Lum, Cynthia, and Koper, Christopher S. Improving the Investigation, Clearance Rates, and Victim Restoration of Robberies: A Randomized Controlled Experiment, Seattle, Washington, Rochester, New York, 2021-2023. Inter-university Consortium for Political and Social Research [distributor], 2024-07-30. https://doi.org/10.3886/ICPSR39101.v1

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Principal Investigator(s): View help for Principal Investigator(s) Cynthia Lum , George Mason University; Christopher S. Koper , George Mason University

https://doi.org/10.3886/ICPSR39101.v1

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Clearance rates for most crimes have remained low and stable for decades (Braga et al., 2011), despite advances in police technologies (Koper et al., 2015). Many police agencies have developed triaging practices for criminal investigations using solvability factors to guess which cases are most likely to be solved and to allocate investigative resources to those crimes (Eck, 1983; 1992). This practice partially stems from a persistent belief that resolving crimes and the resulting clearance rates are due to circumstances of the crime and community context, and are beyond the control of police. However, a growing body of research has challenged this belief, demonstrating that enhanced investigative efforts can improve crime clearance rates beyond solvability factors (Braga and Dusseault, 2018; Lum and Wellford, 2023).

In this study, the research team sought to determine if investigative follow-ups could increase clearance rates for robbery and burglary cases (frequently occurring crime types with traditionally low clearance rates) and increase victim satisfaction with police services. Agencies selected for the study were the Seattle Police Department (SPD) in Seattle, Washington, and the Rochester Police Department (RPD) in Rochester, New York. Both agencies triaged a large proportion of robbery cases and would have a large enough sample size to successfully carry out an experiment.

The original study design was a randomized controlled trial. In both sites, robbery cases would be allocated to either the intervention condition--an investigative follow-up conducted by an officer during their daily patrol assignment--or the control condition with no follow-up. Challenges to personnel and agency funding from the COVID-19 pandemic, the murder of George Floyd, and other officer-involved deaths in 2020 led to difficulties implementing the study as initially designed. The experiment was not initiated in Rochester, and initiated but not completed in Seattle. Therefore, the team transitioned to a natural quasi-experiment design in Rochester and added a case analysis of robberies in Seattle.

This collection contains three datasets: victim satisfaction surveys from Seattle (DS1, n=39) and Rochester (DS2, n=37), and supplemental reports on follow-ups made during the Seattle experiment implementation (DS3, n=82).

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police beat

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Access to these data is restricted. Users interested in obtaining these data must complete a Restricted Data Use Agreement, specify the reason for the request, and obtain IRB approval or notice of exemption for their research.

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The data collected for this project were limited due to problems with program implementation and obtaining survey responses. As such, the principal investigators of this project strongly caution against further analysis or use of this data.

Methodology

Study purpose view help for study purpose.

The purpose of this study was to investigate whether assigning a patrol follow-up to uninvestigated robbery and/or burglary cases would increase case clearance rates and victim satisfaction with police services.

Study Design View help for Study Design

The originally proposed study design consisted of a randomized controlled trial at two police agencies: Seattle Police Department (SPD) in Seattle, Washington and Rochester Police Department (RPD) in Rochester, New York. Uninvestigated robbery cases would be randomly allocated to receive a simple patrol follow-up (treatment) or receive no follow-up (control). Agencies were selected for having lower-than-average clearance rates for robberies and tending to triage robbery cases based on solvability (i.e., no investigative effort assigned beyond the initial patrol investigation). Victim surveys would be sent to all treatment and control cases on a rolling basis.

Due to challenges stemming in 2020 from the COVID-19 pandemic, as well as social-political fallout from the murder of George Floyd and other officer-involved deaths, both agencies lost significant personnel and financial resources. Along with other challenges, such as low victim survey response rates, officer reassignments, and low officer morale, these events necessitated a study design revision for both locations.

In both study sites, the research team took over the implementation of the victim survey. Team members received uninvestigated robbery case information and victim information from SPD and RPD. Surveys were mailed with a prepaid, self-addressed return envelope and instructions to complete the survey online if preferred. Upon transitioning to burglary cases, the survey questionnaire was modified for burglaries.

A case analysis of robbery victimization within Seattle was added during the project. All robberies that took place in Seattle in 2021 that were not still under active investigation (total n=742) were coded for information about the incident, people involved, and officer's decision-making process. The cases included robberies that did not receive investigative resources (n=356) and those that had been investigated or assigned to a detective (n=386). Coding began in July 2022 and completed in August 2023. The research team pair-coded the robbery reports and met to reconcile any differences.

Sample View help for Sample

For Seattle, 117 robbery victim surveys were distributed based on the case information received (n=58 treatment, n=59 control); 6 surveys were returned (5% response rate). 204 burglary surveys were distributed, with 39 surveys returned (19% response rate).

For Rochester, 251 robbery victim surveys were distributed based on the case information received (n=116 investigated, n=135 not investigated). 37 surveys were returned (15% response rate).

Time Method View help for Time Method

Universe view help for universe.

• Victims of robbery/burglary crimes.
• Uninvestigated and investigated robbery and burglary cases in Seattle.

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Data type(s) view help for data type(s), mode of data collection view help for mode of data collection, description of variables view help for description of variables.

Victim satisfaction surveys: Items asked respondents to rate their satisfaction with how the police handled their case, how they were treated by the police, their level of perceived safety (current and following the case), level of trust in the police, and if they would encourage others to call the police for help. Administrative variables include treatment group status; description of offense; precinct, sector, and beat where incident occurred; and month and date of incident. Age is the sole demographic variable. The Rochester-specific survey also includes mode of follow-up communication, investigation status, and date of case status.

Follow-up supplemental reports: Items include number of times victim was contacted, mode of contact with victim, if the officer spoke to the victim, if a follow-up was able to be carried out, whether new information/leads were gathered from a follow-up, and if a detective/investigator was contacted.

Original Release Date View help for Original Release Date

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2024-07-30 ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection:

• Checked for undocumented or out-of-range codes.

The public-use data files in this collection are available for access by the general public. Access does not require affiliation with an ICPSR member institution.

One or more files in this data collection have special restrictions . Restricted data files are not available for direct download from the website; click on the Restricted Data button to learn more.

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Understanding the Milgram Experiment in Psychology

A closer look at Milgram's controversial studies of obedience

Isabelle Adam (CC BY-NC-ND 2.0) via Flickr

Factors That Influence Obedience

• Ethical Concerns
• Replications

How far do you think people would go to obey an authority figure? Would they refuse to obey if the order went against their values or social expectations? Those questions were at the heart of an infamous and controversial study known as the Milgram obedience experiments.

Yale University  psychologist   Stanley Milgram  conducted these experiments during the 1960s. They explored the effects of authority on obedience. In the experiments, an authority figure ordered participants to deliver what they believed were dangerous electrical shocks to another person. These results suggested that people are highly influenced by authority and highly obedient . More recent investigations cast doubt on some of the implications of Milgram's findings and even the results and procedures themselves. Despite its problems, the study has, without question, made a significant impact on psychology .

At a Glance

Milgram's experiments posed the question: Would people obey orders, even if they believed doing so would harm another person? Milgram's findings suggested the answer was yes, they would. The experiments have long been controversial, both because of the startling findings and the ethical problems with the research. More recently, experts have re-examined the studies, suggesting that participants were often coerced into obeying and that at least some participants recognized that the other person was just pretending to be shocked. Such findings call into question the study's validity and authenticity, but some replications suggest that people are surprisingly prone to obeying authority.

History of the Milgram Experiments

Milgram started his experiments in 1961, shortly after the trial of the World War II criminal Adolf Eichmann had begun. Eichmann’s defense that he was merely following instructions when he ordered the deaths of millions of Jews roused Milgram’s interest.

In his 1974 book "Obedience to Authority," Milgram posed the question, "Could it be that Eichmann and his million accomplices in the Holocaust were just following orders? Could we call them all accomplices?"

Procedure in the Milgram Experiment

The participants in the most famous variation of the Milgram experiment were 40 men recruited using newspaper ads. In exchange for their participation, each person was paid $4.50.

Milgram developed an intimidating shock generator, with shock levels starting at 15 volts and increasing in 15-volt increments all the way up to 450 volts. The many switches were labeled with terms including "slight shock," "moderate shock," and "danger: severe shock." The final three switches were labeled simply with an ominous "XXX."

Each participant took the role of a "teacher" who would then deliver a shock to the "student" in a neighboring room whenever an incorrect answer was given. While participants believed that they were delivering real shocks to the student, the “student” was a confederate in the experiment who was only pretending to be shocked.

As the experiment progressed, the participant would hear the learner plead to be released or even complain about a heart condition. Once they reached the 300-volt level, the learner would bang on the wall and demand to be released.

Beyond this point, the learner became completely silent and refused to answer any more questions. The experimenter then instructed the participant to treat this silence as an incorrect response and deliver a further shock.

Most participants asked the experimenter whether they should continue. The experimenter then responded with a series of commands to prod the participant along:

• "Please continue."
• "The experiment requires that you continue."
• "It is absolutely essential that you continue."
• "You have no other choice; you must go on."

Results of the Milgram Experiment

In the Milgram experiment, obedience was measured by the level of shock that the participant was willing to deliver. While many of the subjects became extremely agitated, distraught, and angry at the experimenter, they nevertheless continued to follow orders all the way to the end.

Milgram's results showed that 65% of the participants in the study delivered the maximum shocks. Of the 40 participants in the study, 26 delivered the maximum shocks, while 14 stopped before reaching the highest levels.

Why did so many of the participants in this experiment perform a seemingly brutal act when instructed by an authority figure? According to Milgram, there are some situational factors that can explain such high levels of obedience:

• The physical presence of an authority figure dramatically increased compliance .
• The fact that Yale (a trusted and authoritative academic institution) sponsored the study led many participants to believe that the experiment must be safe.
• The selection of teacher and learner status seemed random.
• Participants assumed that the experimenter was a competent expert.
• The shocks were said to be painful, not dangerous.

Later experiments conducted by Milgram indicated that the presence of rebellious peers dramatically reduced obedience levels. When other people refused to go along with the experimenter's orders, 36 out of 40 participants refused to deliver the maximum shocks.

More recent work by researchers suggests that while people do tend to obey authority figures, the process is not necessarily as cut-and-dried as Milgram depicted it.

In a 2012 essay published in PLoS Biology , researchers suggested that the degree to which people are willing to obey the questionable orders of an authority figure depends largely on two key factors:

• How much the individual agrees with the orders
• How much they identify with the person giving the orders

While it is clear that people are often far more susceptible to influence, persuasion , and obedience than they would often like to be, they are far from mindless machines just taking orders. 

Another study that analyzed Milgram's results concluded that eight factors influenced the likelihood that people would progress up to the 450-volt shock:

• The experimenter's directiveness
• Legitimacy and consistency
• Group pressure to disobey
• Indirectness of proximity
• Intimacy of the relation between the teacher and learner
• Distance between the teacher and learner

Ethical Concerns in the Milgram Experiment

Milgram's experiments have long been the source of considerable criticism and controversy. From the get-go, the ethics of his experiments were highly dubious. Participants were subjected to significant psychological and emotional distress.

Some of the major ethical issues in the experiment were related to:

• The use of deception
• The lack of protection for the participants who were involved
• Pressure from the experimenter to continue even after asking to stop, interfering with participants' right to withdraw

Due to concerns about the amount of anxiety experienced by many of the participants, everyone was supposedly debriefed at the end of the experiment. The researchers reported that they explained the procedures and the use of deception.

Critics of the study have argued that many of the participants were still confused about the exact nature of the experiment, and recent findings suggest that many participants were not debriefed at all.

Replications of the Milgram Experiment

While Milgram’s research raised serious ethical questions about the use of human subjects in psychology experiments , his results have also been consistently replicated in further experiments. One review further research on obedience and found that Milgram’s findings hold true in other experiments. In one study, researchers conducted a study designed to replicate Milgram's classic obedience experiment. The researchers made several alterations to Milgram's experiment.

• The maximum shock level was 150 volts as opposed to the original 450 volts.
• Participants were also carefully screened to eliminate those who might experience adverse reactions to the experiment.

The results of the new experiment revealed that participants obeyed at roughly the same rate that they did when Milgram conducted his original study more than 40 years ago.

Some psychologists suggested that in spite of the changes made in the replication, the study still had merit and could be used to further explore some of the situational factors that also influenced the results of Milgram's study. But other psychologists suggested that the replication was too dissimilar to Milgram's original study to draw any meaningful comparisons.

One study examined people's beliefs about how they would do compared to the participants in Milgram's experiments. They found that most people believed they would stop sooner than the average participants. These findings applied to both those who had never heard of Milgram's experiments and those who were familiar with them. In fact, those who knew about Milgram's experiments actually believed that they would stop even sooner than other people.

Another novel replication involved recruiting participants in pairs and having them take turns acting as either an 'agent' or 'victim.' Agents then received orders to shock the victim. The results suggest that only around 3.3% disobeyed the experimenter's orders.

Recent Criticisms and New Findings

Psychologist Gina Perry suggests that much of what we think we know about Milgram's famous experiments is only part of the story. While researching an article on the topic, she stumbled across hundreds of audiotapes found in Yale archives that documented numerous variations of Milgram's shock experiments.

Participants Were Often Coerced

While Milgram's reports of his process report methodical and uniform procedures, the audiotapes reveal something different. During the experimental sessions, the experimenters often went off-script and coerced the subjects into continuing the shocks.

"The slavish obedience to authority we have come to associate with Milgram’s experiments comes to sound much more like bullying and coercion when you listen to these recordings," Perry suggested in an article for Discover Magazine .

Few Participants Were Really Debriefed

Milgram suggested that the subjects were "de-hoaxed" after the experiments. He claimed he later surveyed the participants and found that 84% were glad to have participated, while only 1% regretted their involvement.

However, Perry's findings revealed that of the 700 or so people who took part in different variations of his studies between 1961 and 1962, very few were truly debriefed.

A true debriefing would have involved explaining that the shocks weren't real and that the other person was not injured. Instead, Milgram's sessions were mainly focused on calming the subjects down before sending them on their way.

Many participants left the experiment in a state of considerable distress. While the truth was revealed to some months or even years later, many were simply never told a thing.

Variations Led to Differing Results

Another problem is that the version of the study presented by Milgram and the one that's most often retold does not tell the whole story. The statistic that 65% of people obeyed orders applied only to one variation of the experiment, in which 26 out of 40 subjects obeyed.

In other variations, far fewer people were willing to follow the experimenters' orders, and in some versions of the study, not a single participant obeyed.

Participants Guessed the Learner Was Faking

Perry even tracked down some of the people who took part in the experiments, as well as Milgram's research assistants. What she discovered is that many of his subjects had deduced what Milgram's intent was and knew that the "learner" was merely pretending.

Such findings cast Milgram's results in a new light. It suggests that not only did Milgram intentionally engage in some hefty misdirection to obtain the results he wanted but that many of his participants were simply playing along.

An analysis of an unpublished study by Milgram's assistant, Taketo Murata, found that participants who believed they were really delivering a shock were less likely to obey, while those who did not believe they were actually inflicting pain were more willing to obey. In other words, the perception of pain increased defiance, while skepticism of pain increased obedience.

A review of Milgram's research materials suggests that the experiments exerted more pressure to obey than the original results suggested. Other variations of the experiment revealed much lower rates of obedience, and many of the participants actually altered their behavior when they guessed the true nature of the experiment.

Impact of the Milgram Experiment

Since there is no way to truly replicate the experiment due to its serious ethical and moral problems, determining whether Milgram's experiment really tells us anything about the power of obedience is impossible to determine.

So why does Milgram's experiment maintain such a powerful hold on our imaginations, even decades after the fact? Perry believes that despite all its ethical issues and the problem of never truly being able to replicate Milgram's procedures, the study has taken on the role of what she calls a "powerful parable."

Milgram's work might not hold the answers to what makes people obey or even the degree to which they truly obey. It has, however, inspired other researchers to explore what makes people follow orders and, perhaps more importantly, what leads them to question authority.

Recent findings undermine the scientific validity of the study. Milgram's work is also not truly replicable due to its ethical problems. However, the study has led to additional research on how situational factors can affect obedience to authority.

Milgram’s experiment has become a classic in psychology , demonstrating the dangers of obedience. The research suggests that situational variables have a stronger sway than personality factors in determining whether people will obey an authority figure. However, other psychologists argue that both external and internal factors heavily influence obedience, such as personal beliefs and overall temperament.

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Russell N, Gregory R. The Milgram-Holocaust linkage: challenging the present consensus . State Crim J. 2015;4(2):128-153.

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Haslam SA, Reicher SD. Contesting the "nature" of conformity: What Milgram and Zimbardo's studies really show . PLoS Biol. 2012;10(11):e1001426. doi:10.1371/journal.pbio.1001426

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Haslam N, Loughnan S, Perry G. Meta-Milgram: an empirical synthesis of the obedience experiments .  PLoS One . 2014;9(4):e93927. doi:10.1371/journal.pone.0093927

Perry G. Deception and illusion in Milgram's accounts of the obedience experiments . Theory Appl Ethics . 2013;2(2):79-92.

Blass T. The Milgram paradigm after 35 years: some things we now know about obedience to authority . J Appl Soc Psychol. 1999;29(5):955-978. doi:10.1111/j.1559-1816.1999.tb00134.x

Burger J. Replicating Milgram: Would people still obey today? . Am Psychol . 2009;64(1):1-11. doi:10.1037/a0010932

Elms AC. Obedience lite . American Psychologist . 2009;64(1):32-36. doi:10.1037/a0014473

Miller AG. Reflections on “replicating Milgram” (Burger, 2009) . American Psychologist . 2009;64(1):20-27. doi:10.1037/a0014407

Grzyb T, Dolinski D. Beliefs about obedience levels in studies conducted within the Milgram paradigm: Better than average effect and comparisons of typical behaviors by residents of various nations .  Front Psychol . 2017;8:1632. doi:10.3389/fpsyg.2017.01632

Caspar EA. A novel experimental approach to study disobedience to authority .  Sci Rep . 2021;11(1):22927. doi:10.1038/s41598-021-02334-8

Haslam SA, Reicher SD, Millard K, McDonald R. ‘Happy to have been of service’: The Yale archive as a window into the engaged followership of participants in Milgram’s ‘obedience’ experiments . Br J Soc Psychol . 2015;54:55-83. doi:10.1111/bjso.12074

Perry G, Brannigan A, Wanner RA, Stam H. Credibility and incredulity in Milgram’s obedience experiments: A reanalysis of an unpublished test . Soc Psychol Q . 2020;83(1):88-106. doi:10.1177/0190272519861952

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

Title Page Setup

A title page is required for all APA Style papers. There are both student and professional versions of the title page. Students should use the student version of the title page unless their instructor or institution has requested they use the professional version. APA provides a student title page guide (PDF, 199KB) to assist students in creating their title pages.

Student title page

The student title page includes the paper title, author names (the byline), author affiliation, course number and name for which the paper is being submitted, instructor name, assignment due date, and page number, as shown in this example.

Title page setup is covered in the seventh edition APA Style manuals in the Publication Manual Section 2.3 and the Concise Guide Section 1.6

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