math definition for controlled experiment

• Science Notes Posts
• Contact Science Notes
• Todd Helmenstine Biography
• Anne Helmenstine Biography
• Free Printable Periodic Tables (PDF and PNG)
• Periodic Table Wallpapers
• Interactive Periodic Table
• Periodic Table Posters
• Science Experiments for Kids
• How to Grow Crystals
• Chemistry Projects
• Fire and Flames Projects
• Holiday Science
• Chemistry Problems With Answers
• Physics Problems
• Unit Conversion Example Problems
• Chemistry Worksheets
• Biology Worksheets
• Periodic Table Worksheets
• Physical Science Worksheets
• Science Lab Worksheets
• My Amazon Books

What Is a Control Variable? Definition and Examples

A control variable is any factor that is controlled or held constant during an experiment . For this reason, it’s also known as a controlled variable or a constant variable. A single experiment may contain many control variables . Unlike the independent and dependent variables , control variables aren’t a part of the experiment, but they are important because they could affect the outcome. Take a look at the difference between a control variable and control group and see examples of control variables.

Importance of Control Variables

Remember, the independent variable is the one you change, the dependent variable is the one you measure in response to this change, and the control variables are any other factors you control or hold constant so that they can’t influence the experiment. Control variables are important because:

• They make it easier to reproduce the experiment.
• The increase confidence in the outcome of the experiment.

For example, if you conducted an experiment examining the effect of the color of light on plant growth, but you didn’t control temperature, it might affect the outcome. One light source might be hotter than the other, affecting plant growth. This could lead you to incorrectly accept or reject your hypothesis. As another example, say you did control the temperature. If you did not report this temperature in your “methods” section, another researcher might have trouble reproducing your results. What if you conducted your experiment at 15 °C. Would you expect the same results at 5 °C or 35 5 °C? Sometimes the potential effect of a control variable can lead to a new experiment!

Sometimes you think you have controlled everything except the independent variable, but still get strange results. This could be due to what is called a “ confounding variable .” Examples of confounding variables could be humidity, magnetism, and vibration. Sometimes you can identify a confounding variable and turn it into a control variable. Other times, confounding variables cannot be detected or controlled.

Control Variable vs Control Group

A control group is different from a control variable. You expose a control group to all the same conditions as the experimental group, except you change the independent variable in the experimental group. Both the control group and experimental group should have the same control variables.

Control Variable Examples

Anything you can measure or control that is not the independent variable or dependent variable has potential to be a control variable. Examples of common control variables include:

• Duration of the experiment
• Size and composition of containers
• Temperature
• Sample volume
• Experimental technique
• Chemical purity or manufacturer
• Species (in biological experiments)

For example, consider an experiment testing whether a certain supplement affects cattle weight gain. The independent variable is the supplement, while the dependent variable is cattle weight. A typical control group would consist of cattle not given the supplement, while the cattle in the experimental group would receive the supplement. Examples of control variables in this experiment could include the age of the cattle, their breed, whether they are male or female, the amount of supplement, the way the supplement is administered, how often the supplement is administered, the type of feed given to the cattle, the temperature, the water supply, the time of year, and the method used to record weight. There may be other control variables, too. Sometimes you can’t actually control a control variable, but conditions should be the same for both the control and experimental groups. For example, if the cattle are free-range, weather might change from day to day, but both groups have the same experience. When you take data, be sure to record control variables along with the independent and dependent variable.

• Box, George E.P.; Hunter, William G.; Hunter, J. Stuart (1978). Statistics for Experimenters : An Introduction to Design, Data Analysis, and Model Building . New York: Wiley. ISBN 978-0-471-09315-2.
• Giri, Narayan C.; Das, M. N. (1979). Design and Analysis of Experiments . New York, N.Y: Wiley. ISBN 9780852269145.
• Stigler, Stephen M. (November 1992). “A Historical View of Statistical Concepts in Psychology and Educational Research”. American Journal of Education . 101 (1): 60–70. doi: 10.1086/444032

Related Posts

• Science, Tech, Math ›
• Social Sciences ›
• Sociology ›
• Research, Samples, and Statistics ›

What are Controlled Experiments?

Determining Cause and Effect

skynesher / Getty Images

• Research, Samples, and Statistics
• Key Concepts
• Major Sociologists
• News & Issues
• Recommended Reading
• Archaeology

A controlled experiment is a highly focused way of collecting data and is especially useful for determining patterns of cause and effect. This type of experiment is used in a wide variety of fields, including medical, psychological, and sociological research. Below, we’ll define what controlled experiments are and provide some examples.

Key Takeaways: Controlled Experiments

• A controlled experiment is a research study in which participants are randomly assigned to experimental and control groups.
• A controlled experiment allows researchers to determine cause and effect between variables.
• One drawback of controlled experiments is that they lack external validity (which means their results may not generalize to real-world settings).

Experimental and Control Groups

To conduct a controlled experiment , two groups are needed: an experimental group and a control group . The experimental group is a group of individuals that are exposed to the factor being examined. The control group, on the other hand, is not exposed to the factor. It is imperative that all other external influences are held constant . That is, every other factor or influence in the situation needs to remain exactly the same between the experimental group and the control group. The only thing that is different between the two groups is the factor being researched.

For example, if you were studying the effects of taking naps on test performance, you could assign participants to two groups: participants in one group would be asked to take a nap before their test, and those in the other group would be asked to stay awake. You would want to ensure that everything else about the groups (the demeanor of the study staff, the environment of the testing room, etc.) would be equivalent for each group. Researchers can also develop more complex study designs with more than two groups. For example, they might compare test performance among participants who had a 2-hour nap, participants who had a 20-minute nap, and participants who didn’t nap.

Assigning Participants to Groups

In controlled experiments, researchers use  random assignment (i.e. participants are randomly assigned to be in the experimental group or the control group) in order to minimize potential confounding variables in the study. For example, imagine a study of a new drug in which all of the female participants were assigned to the experimental group and all of the male participants were assigned to the control group. In this case, the researchers couldn’t be sure if the study results were due to the drug being effective or due to gender—in this case, gender would be a confounding variable.

Random assignment is done in order to ensure that participants are not assigned to experimental groups in a way that could bias the study results. A study that compares two groups but does not randomly assign participants to the groups is referred to as quasi-experimental, rather than a true experiment.

Blind and Double-Blind Studies

In a blind experiment, participants don’t know whether they are in the experimental or control group. For example, in a study of a new experimental drug, participants in the control group may be given a pill (known as a placebo ) that has no active ingredients but looks just like the experimental drug. In a double-blind study , neither the participants nor the experimenter knows which group the participant is in (instead, someone else on the research staff is responsible for keeping track of group assignments). Double-blind studies prevent the researcher from inadvertently introducing sources of bias into the data collected.

Example of a Controlled Experiment

If you were interested in studying whether or not violent television programming causes aggressive behavior in children, you could conduct a controlled experiment to investigate. In such a study, the dependent variable would be the children’s behavior, while the independent variable would be exposure to violent programming. To conduct the experiment, you would expose an experimental group of children to a movie containing a lot of violence, such as martial arts or gun fighting. The control group, on the other hand, would watch a movie that contained no violence.

To test the aggressiveness of the children, you would take two measurements : one pre-test measurement made before the movies are shown, and one post-test measurement made after the movies are watched. Pre-test and post-test measurements should be taken of both the control group and the experimental group. You would then use statistical techniques to determine whether the experimental group showed a significantly greater increase in aggression, compared to participants in the control group.

Studies of this sort have been done many times and they usually find that children who watch a violent movie are more aggressive afterward than those who watch a movie containing no violence.

Strengths and Weaknesses

Controlled experiments have both strengths and weaknesses. Among the strengths is the fact that results can establish causation. That is, they can determine cause and effect between variables. In the above example, one could conclude that being exposed to representations of violence causes an increase in aggressive behavior. This kind of experiment can also zero-in on a single independent variable, since all other factors in the experiment are held constant.

On the downside, controlled experiments can be artificial. That is, they are done, for the most part, in a manufactured laboratory setting and therefore tend to eliminate many real-life effects. As a result, analysis of a controlled experiment must include judgments about how much the artificial setting has affected the results. Results from the example given might be different if, say, the children studied had a conversation about the violence they watched with a respected adult authority figure, like a parent or teacher, before their behavior was measured. Because of this, controlled experiments can sometimes have lower external validity (that is, their results might not generalize to real-world settings).

Updated  by Nicki Lisa Cole, Ph.D.

• An Overview of Qualitative Research Methods
• Using Ethnomethodology to Understand Social Order
• Pros and Cons of Secondary Data Analysis
• Immersion Definition: Cultural, Language, and Virtual
• Sociology Explains Why Some People Cheat on Their Spouses
• What Is Participant Observation Research?
• The Differences Between Indexes and Scales
• Definition and Overview of Grounded Theory
• Deductive Versus Inductive Reasoning
• The Study of Cultural Artifacts via Content Analysis
• Units of Analysis as Related to Sociology
• Data Sources For Sociological Research
• Full-Text Sociology Journals Online
• How Race and Gender Biases Impact Students in Higher Ed
• The Racial Wealth Gap
• A Review of Software Tools for Quantitative Data Analysis

All Subjects

History of Mathematics

Study guides for every class, that actually explain what's on your next test, controlled experiments, from class:.

Controlled experiments are scientific tests where all variables are kept constant except for one, which is manipulated to observe its effect. This design allows researchers to determine cause-and-effect relationships by isolating the influence of the independent variable while controlling for external factors that could skew results.

congrats on reading the definition of Controlled Experiments . now let's actually learn it.

5 Must Know Facts For Your Next Test

• Controlled experiments are essential for establishing causal relationships by ensuring that only one variable is tested at a time.
• In a controlled experiment, researchers use control groups and experimental groups to compare outcomes effectively.
• The use of random assignment helps eliminate bias, ensuring that each participant has an equal chance of being placed in any group.
• Controlled experiments can be conducted in various fields, including psychology, biology, and physics, highlighting their versatility.
• A well-designed controlled experiment often includes replication, allowing results to be verified and ensuring reliability in scientific findings.

Review Questions

• Controlled experiments help establish cause-and-effect relationships by isolating one independent variable while keeping all other variables constant. This design allows researchers to observe how changes in the independent variable affect the dependent variable without interference from other factors. As a result, it provides clear evidence of whether a specific change directly causes an effect, making it a critical method in scientific inquiry.
• Random assignment plays a crucial role in ensuring the validity of controlled experiments by minimizing bias and ensuring that any differences between groups are due to chance rather than systematic differences. By randomly assigning participants to control and experimental groups, researchers can confidently attribute any observed effects on the dependent variable to the manipulation of the independent variable. This practice enhances the reliability of conclusions drawn from the experiment.
• Replication is essential in controlled experiments because it verifies the reliability and validity of experimental findings. When other researchers replicate an experiment and obtain similar results, it strengthens confidence in those findings and contributes to a more robust body of scientific knowledge. Replication helps identify any potential errors or biases in initial studies and ensures that conclusions drawn are not just anomalies but reflect true effects. This process ultimately advances understanding in various fields by building a consensus based on repeated evidence.

Related terms

Independent Variable : The variable that is changed or controlled in a scientific experiment to test its effects on the dependent variable.

Dependent Variable : The variable that is measured and affected in an experiment as a result of changes to the independent variable.

Random Assignment : A technique used in experiments where participants are randomly assigned to different groups to ensure that each group is similar at the start of the experiment.

" Controlled Experiments " also found in:

Subjects ( 18 ).

• Art Conservation and Restoration
• Bioremediation
• Cognitive Bias in Business Decision Making
• College Biology
• Critical Thinking
• Game Theory
• Haptic Interfaces and Telerobotics
• Introduction to Archaeology
• Introduction to Biostatistics
• Introduction to Environmental Science
• Introduction to the Study of Language
• Microeconomic Analysis for Business Decisions
• Philosophy of Science
• Preparatory Statistics
• Principles & Techniques of Data Science
• Psychology of Economic Decision-Making
• Wireless Sensor Networks

© 2024 Fiveable Inc. All rights reserved.

Ap® and sat® are trademarks registered by the college board, which is not affiliated with, and does not endorse this website..

Controlled Experiment

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

This is when a hypothesis is scientifically tested.

In a controlled experiment, an independent variable (the cause) is systematically manipulated, and the dependent variable (the effect) is measured; any extraneous variables are controlled.

The researcher can operationalize (i.e., define) the studied variables so they can be objectively measured. The quantitative data can be analyzed to see if there is a difference between the experimental and control groups.

What is the control group?

In experiments scientists compare a control group and an experimental group that are identical in all respects, except for one difference – experimental manipulation.

Unlike the experimental group, the control group is not exposed to the independent variable under investigation and so 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.

Randomly allocating participants to independent variable groups means that all participants should have an equal chance of participating in each condition.

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

What are extraneous variables?

The researcher wants to ensure that the manipulation of the independent variable has changed the changes in the dependent variable.

Hence, all the other variables that could affect the dependent variable to change must be controlled. These other variables are called extraneous or confounding variables.

Extraneous variables should be controlled were possible, as they might be important enough to provide alternative explanations for the effects.

In practice, it would be difficult to control all the variables in a child’s educational achievement. For example, it would be difficult to control variables that have happened in the past.

A researcher can only control the current environment of participants, such as time of day and noise levels.

Why conduct controlled experiments?

Scientists use controlled experiments because they allow for precise control of extraneous and independent variables. This allows a cause-and-effect relationship to be established.

Controlled experiments also follow a standardized step-by-step procedure. This makes it easy for another researcher to replicate the study.

Key Terminology

Experimental group.

The group being treated or otherwise manipulated for the sake of the experiment.

Control Group

They receive no treatment and are used as a comparison group.

Ecological validity

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

Experimenter effects

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

Demand characteristics

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

Independent variable (IV)

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

Dependent variable (DV)

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

Extraneous variables (EV)

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

Confounding variables

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

Random Allocation

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

Order effects

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

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

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

What is the control in an experiment?

In an experiment , the control is a standard or baseline group not exposed to the experimental treatment or manipulation. It serves as a comparison group to the experimental group, which does receive the treatment or manipulation.

The control group helps to account for other variables that might influence the outcome, allowing researchers to attribute differences in results more confidently to the experimental treatment.

Establishing a cause-and-effect relationship between the manipulated variable (independent variable) and the outcome (dependent variable) is critical in establishing a cause-and-effect relationship between the manipulated variable.

What is the purpose of controlling the environment when testing a hypothesis?

Controlling the environment when testing a hypothesis aims to eliminate or minimize the influence of extraneous variables. These variables other than the independent variable might affect the dependent variable, potentially confounding the results.

By controlling the environment, researchers can ensure that any observed changes in the dependent variable are likely due to the manipulation of the independent variable, not other factors.

This enhances the experiment’s validity, allowing for more accurate conclusions about cause-and-effect relationships.

It also improves the experiment’s replicability, meaning other researchers can repeat the experiment under the same conditions to verify the results.

Why are hypotheses important to controlled experiments?

Hypotheses are crucial to controlled experiments because they provide a clear focus and direction for the research. A hypothesis is a testable prediction about the relationship between variables.

It guides the design of the experiment, including what variables to manipulate (independent variables) and what outcomes to measure (dependent variables).

The experiment is then conducted to test the validity of the hypothesis. If the results align with the hypothesis, they provide evidence supporting it.

The hypothesis may be revised or rejected if the results do not align. Thus, hypotheses are central to the scientific method, driving the iterative inquiry, experimentation, and knowledge advancement process.

What is the experimental method?

The experimental method is a systematic approach in scientific research where an independent variable is manipulated to observe its effect on a dependent variable, under controlled conditions.

In probability and statistics, an experiment typically refers to a study in which the experimenter is trying to determine whether there is a relationship between two or more variables. In an experiment, the subjects are randomly assigned to either a treatment group or a control group (there can be more than one of either group).

Generally, the control group in an experiment receives a placebo (substance that has no effect) or no treatment at all. The treatment group receives the experimental treatment. The goal of the experiment is to determine whether or not the treatment has the desired/any effect that differs from the control group to a degree that the difference can be attributed to the treatment rather than to random chance or variability. Well-designed experiments can yield informative and unambiguous conclusions about cause and effect relationships.

As an example, if a scientist wants to test whether a new medication they developed has any effect, they would select subjects from a common population and randomly assign them to either a treatment group or a control group. They would then administer the treatment to the treatment group, and either a placebo or no treatment to the control group, and study the effects of each using statistical measures to determine whether the medication had any effect beyond chance or variability.

Note that an experiment does not necessarily need to have a physical treatment. The term "treatment" is used fairly loosely. Another experiment could look at the effects of getting advice from a college counselor on admission rates compared to not getting advice from a college counselor. In this case, the "treatment" would be getting advice from a college counselor. The control group would get no advice from a college counselor.

Importance of experimental design

Like survey methodology , experimental design is essential to the validity of the results of the experiment. A poorly designed experiment can result in false or incorrect conclusions. Proper statistical experiment design generally involves the following:

• Identification of the explanatory variable, also referred to as the independent variable . The explanatory variable is the "treatment," or the thing that causes the change, and can be anything that causes a change in the response variable.
• Identification of the response variable, also referred to as the dependent variable . It is the variable that may be affected by the explanatory/independent variable.
• Defining the population of interest and taking a random sample from the population. Generally the larger the random sample, the less potential for sample error, since the larger sample will likely be more representative of the population.
• Random assignment of the subjects in the sample to either the treatment group or the control group.
• Administration of the treatment to the treatment group, and placebo (or nothing) to the control group), possibly using a blind experiment (the subject doesn't know whether they are receiving the treatment or the placebo) or double blind experiment (neither experimenter nor subject knows which treatment they are getting).
• Measurement of the response over a chosen period of time.
• Statistical analysis of the supposed response to determine whether there is an actual response, or the response can be attributed to chance, to determine whether there is a causal relationship between the treatment and the response.
• Replication of the experiment by peers, assuming there is a causal relationship between the treatment and the response.

Experiments vs surveys

Experiments and surveys are both techniques used as part of inferential statistics . A survey involves the use of a random sample of the population, rather than the whole, with the goal that all subjects in the population have an equal chance of being selected. The random sample of the population is then used to draw conclusions or make inferences about the population as a whole.

In contrast, an experiment typically involves the use of random assignment such that all subjects have an equal chance of being assigned to the groups (treatment and control) in the study, which minimizes potential biases, as well as allows the experimenters to evaluate the role of variability in the experiment. This in turn allows them to determine whether any observed differences between the groups merit further study or not based on whether or not the differences can be attributed to variability or chance.