control in experiment definition

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.

controlled experiment extraneous variables

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.

What is a control in a science experiment?

What is a Control in a Science Experiment?

A well-designed experiment is the backbone of scientific research, and an essential component of every experiment is the control. In this article, we will delve into the world of controls, exploring what they are, why they are crucial, and how to implement them effectively.

A control in a science experiment is a sample or treatment that is identical to the experimental group, except for the specific variable being tested. In other words, a control is a "standard" or " baseline" condition against which the effects of the experimental variable can be measured and compared. The control serves as a reference point to help scientists understand the results of the experiment and draw meaningful conclusions.

Why is a Control Necessary?

Imagine conducting an experiment without a control: what would you compare your results to? A control provides a stable, predictable outcome against which the effects of the experimental variable can be measured. Without a control, it is impossible to determine whether changes observed in the experimental group are due to the variable being tested or some other factor.

Types of Controls

There are several types of controls used in science experiments, each serving a specific purpose:

Positive Control : A positive control is a sample that has been treated with the specific variable being tested. This control ensures that the variable is indeed having the expected effect.
Negative Control : A negative control is a sample that has not been exposed to the variable. This control helps to ensure that any changes observed are not due to extraneous factors.
Double-Blind Control : A double-blind control is used when researchers are unaware of which samples are which, adding an extra layer of objectivity to the experiment.
Split-Plot Design Control : In a split-plot design, a control is used to evaluate the effect of a factor on a subset of the overall experimental design.

How to Implement a Control

Implementing a control is crucial to the success of an experiment. Here are some best practices to keep in mind:

Identify the variable : Clearly define the variable being tested and ensure that the control is identical in all aspects except for this variable.
Select a suitable control : Choose a control that is representative of the experimental group, but different in the specific variable being tested.
Maintain consistency : Ensure that all samples, including the control, are treated with the same care and handling throughout the experiment.
Monitor and record : Regularly monitor and record the control’s data to detect any unexpected changes or anomalies.

Case Study: The Importance of Controls

To illustrate the significance of controls, let’s consider a classic experiment conducted in the 1920s by Armaul, which aimed to investigate the effect of sugar on the growth of radish sprouts. The experiment involved three groups: a control group with no sugar, a group with sugar, and a group with inhibitors to prevent growth.

TABLE 1: Results of Armaul’s Experiment

As shown in Table 1 , the results indicate that the addition of sugar significantly increased leaf growth and stem length, while the inhibitor treatment had the opposite effect. Without a control, it would be impossible to determine whether the changes were due to the sugar or other factors.

In conclusion, a control is an essential component of a science experiment, providing a benchmark against which the effects of the experimental variable can be measured and compared. By understanding the different types of controls and implementing them effectively, scientists can increase the accuracy and reliability of their results, drawing meaningful conclusions from their research. Remember, a control is not just a necessary evil, but a vital tool to help us uncover the secrets of the universe.