What Is a Control in an Experiment? (With Definition and Guide) | Indeed.com

What Is a Control in an Experiment? (With Definition and Guide)

By Indeed Editorial Team

June 9, 2021

Many careers in medicine, science and analysis involve conducting experiments to gather data. Understanding the role of a control, also known as a “control variable” or “control group,” can help you conduct efficient experiments that meet scientific method standards. In this article, we discuss what a control is, how to develop one and which careers are most likely to use them.

Related: The Scientific Method Steps (With an Example)

What is a control in an experiment?

When conducting an experiment, a control is an element that remains unchanged or unaffected by other variables. It is used as a benchmark or a point of comparison against which other test results are measured. Controls are typically used in science experiments, business research, cosmetic testing and medication testings.

For example, when a new type of medicine is tested, the group that receives the medication is called the “experimented” group. The control group, however, receives no medicine or a placebo.

By comparing the impact on those who take the medicine to those who don't, scientists can observe and measure the effects the new medication.

Variables in experiments

A controlled variable is one of three types of variables usually found in experiments. A variable is any factor, trait, or condition that can exist in differing amounts or types. The other two are independent and dependent variables.

Controlled variables

Controlled variables are quantities that a scientist wants to remain constant. If they were altered, it would greatly affect the experiment’s results. Most experiments have more than one controlled variable. For example, if you are testing a new cold medicine, the controlled variable might be that the patient has a cold and a fever. If you tested someone without those two controls, your results would be inaccurate and possibly misleading.

Independent variables

These are the variables being tested, such as the new cold medication. Only one independent variable is typically tested at a time. In simple terms, the independent variable is the potential cause of an observed effect. This is the variable most likely to change from one experiment to the next, such as changing the amount of medicine given when trying to determine the correct dosage.

Dependent variables

Scientists observe and monitor these variables to see if they are changed, or “dependent,” on the independent variables. For example, doses of the new cold may cause headaches which effect the patient’s health.

Related: 10 Types of Variables in Research and Statistics

How to develop a control in an experiment

Developing a control in an experiment depends on the independent variables being tested. When testing new medication, the control group doesn't receive it. If testing the effect of sunlight on the growth of a flower, the control group of flowers might be grown inside and away from the sun.

Here are the steps to take when performing an experiment with a control group:

1. Ask a question based on observation

Your experiment should begin with a question that needs an answer. Perhaps you've noticed an effect and are curious about its cause. This is your hypothesis, the integral starting point for figuring out what your control is going to be.

Related: Hypothesis: Definition and Examples

2. Make observations

Once you've settled on the question you hope to answer, begin making observations on the topic you hope to study. If you're a medical professional trying to determine what effects a particular exercise regimen has on arthritic patients, note any patients doing similar exercises.

Record any observations you make about their type of arthritis, what their regimen is and what effects it seems to have. This helps you decide which independent variables you wish to test and which groups are most likely to display the effects these variables may have.

3. Refine your hypothesis

With a question that needs answering and some observation-based data, choose a more specific hypothesis. Doing so will help you figure out the exact independent variable to use during your study. For example, if a psychologist observed that their patients benefit from spending time outside their house, the specific hypothesis becomes that periodically enjoying time away from the home has a positive effect on their health and recovery.

4. Select a specific variable to test

For example, there may be several exercise regimens that aid arthritis patients' mobility. However, since the scientific method only works by testing one variable at a time, you must only select one. This way, you can trace all data gathered back to one specific cause.

Consider picking one exercise for all patients. Make sure they perform the same actions in the same way for the same amount of time. This eliminates the possibility of other variables affecting the outcome of your data. Assign this variable to an experimental group of patients.

5. Pick a control group

Choose patients with the same condition as your experimental group but who either receive no treatment or the usual treatment for their condition. This is your baseline and is one of the most important aspects of your experiment. Record the effects your control group exhibits and compare it to your experimental group. Since they have not experienced the variable you are testing, any effect observed in both groups cannot be attributed to your independent variable. For example, if both groups have improved mobility, it is not due to the tested exercise regime.

When selecting the control group, make sure they are as similar as possible to your experimental group. Whether they are patients, plants or any other subject you wish to study, selecting those similar to your test group ensures that other variables have no or little effect on your experiment.

6. Conduct your tests

After selecting your experimental and control groups, you can begin testing your experiment. If you're careful in your group selections, your experiment can meet scientific method standards, which is why making precise choices when deciding who or what to include in your control is so important.

For example, if your hypothesis is that time spent outside has positive effects on a patient's recovery, it's helpful if both your experimental and control groups have similar symptoms, such as low enthusiasm for socializing and spend most of their time inside. Your experiment likely includes your experimental group spending time outside while your control group stays indoors.

Then, you can find statistics such as:

  • The way your experimental group felt both before and after the experiment

  • The way your control group felt during those periods

  • The comparison between the two groups' feelings before and after

7. Continue your tests

After your first test, you might find that there isn't a measurable change in their responses to social situations. Whether you prove your hypothesis or not, consider analyzing your test for any possible variables previously unaccounted for and then trying the experiment again.

Testing with a controlled experiment involves doing the test several times until the same experiment with similar groups seems to end in similar measurable results when comparing your findings from your experimental group against what you learn from the control group.

Related: Designing an Experiment: A How-To Guide

What careers benefit from using controls in experiments?

Experimentation and the use of control groups isn't confined to the medical field. Most scientific and mathematical studies benefit from controls as does any area of study that requires the development of new methods and the observation and testing of their efficacy. Careers that may use controlled experiments include:

  • Scientist

  • Laboratory technician

  • Software engineer

  • Mathematician

  • Biologist

  • Psychologist

  • Chemist

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