Why is it important to control or eliminate different variables when conducting an experiment?

By Dr. Saul McLeod, published 2019

What is a controlled experiment?

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 variables being studied so they can be objectivity measured. The quantitative data can be analysed to see if there is a difference between the experimental group and control group.

What is the control group in an experiment?

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 base line 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 taking part in each condition.

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

What are extraneous variables in an experiment?

The researcher wants to make sure that it is the manipulation of the independent variable that 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 on 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 do scientists 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 standardised step by step procedure. This makes it easy 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 that lead the participants to think they know what the researcher is looking for (e.g. experimenter’s body language).

Independent variable (IV)

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

Dependent variable (DV)

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

Extraneous variables (EV)

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

Confounding variables

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

Random Allocation

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

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

Order effects

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

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

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

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How to reference this article:

McLeod, S. A. (2019, Aug 12). Controlled Experiment Simply psychology: https://www.simplypsychology.org/controlled-experiment.html

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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.