Scientific experiments are a core part of learning new information, supporting hypotheses, and understanding the effectiveness of current methodologies as you find new techniques to use. Variables and control groups are essential here as they help scientists draw valid conclusions that, in turn, help in finding the accuracy of results. This article discusses control groups, how different experiments require different groups, frequently asked questions (FAQs), and the importance of having such groups.
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Control Group – In a Nutshell
Control groups are essential in driving any experiment when you use the design effectively. However, if you haven’t accounted for all the differences between the control and treatment groups, it leads to confounding instead of independent variables. You can minimize these risks by using:
- The experimental design to account for any potential confounding variable.
- Randomly placed subjects in control and treatment groups.
- Double-blinding to prevent members in each group from adjusting their behaviors based on the group you placed them.
Definition: Control Group
A control group is a group of factors that aren’t subject to change during an experiment. In a scientific experiment, an independent variable remains constant in the control group and changes in the treatment group. These groups help establish a cause-and-effect relationship between the dependent and the independent variable.
If the dependent variable shows any changes, you can attribute it to the independent variable. For example, such groups in medical experiments receive placebo pills instead of standard medications.
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The control group in experiments
Control groups are crucial for any experimental design to work. In an experiment to find new treatments, researchers usually divide the participants into at least two groups:
- Treatment or Experimental Group: The participants who received the medication that’s being tested
- Control Group: The participants received placebo, standard treatment with already known effects, or no treatment.
The treatment used in these experiments are variables manipulated by the researchers and usually depends on the research performed. For example, medical experiments may test new therapies or drugs, while a public policy study may have a new social policy.
To conduct a well-designed experiment, ensure to keep all variables constant between the two groups except the treatment. This way, you can easily measure the extent of the treatment without interference from any confounding variable.
Example
Suppose you’re interested in knowing whether students perform better when paid. In that case, you could assemble several students and divide them into control and experimental groups, as follows:
Students in the experimental group receive money for getting high grades while,
Students in the control group don’t receive any payment.
Comparing records from the two groups lets you understand if monetary incentives motivate students to improve school performance.
Furthermore, some studies may involve more than one control or treatment group. This happens when researchers want to compare a treatment to several alternatives or study multiple treatments simultaneously.
Example
Suppose you’ve created a medication for treating addictions. In that case, you could run this experiment using two control and treatment groups as follows:
- You give the new pill to the treatment group
- Group 1 receives a placebo
- Group 2 gets medication already approved for treating addictions
When comparing the results, you could attribute any changes between the groups to the pill they used, as shown below:
- The control and treatment group 1 show the pill’s effectiveness compared to no treatment.
- The control and treatment group 2 reveal whether there’s a difference between the new medication and the existing options on the market.
The control group in non-experimental research
These groups are also helpful in non-experimental research, as described below:
Control groups in quasi-experimental design
In contrast to regular experiments, the quasi-experimental design uses a different criterion other than randomization to place people. Often, these assignments are pre-existing groups who receive different treatments—for example, studying a teaching method applied to one class within a school and not others or public policy implemented in one city and not the neighboring cities.
In such cases, the classes devoid of the new teaching method and the neighboring cities are the control groups.
Control groups in matching design
A matching design uses correlational research where matching is a potential alternate option. In the study of the matching design, you match people who received the treatment (independent variable) to the others who didn’t (control group). Hence, every member in the experimental group has a counterpart in the control group.
The correlational research ensures that all the counterparts are identical in every way outside of the treatment. Therefore, the treatment is the only factor that may bring differences between the two groups.
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Importance of having a control group
The primary purpose of these groups is to provide a platform where you can compare the experimental results and draw valuable conclusions. These groups assure you of the research’s internal validity. However, it’s challenging to spot if the dependent variable has changed in the treatment group if you don’t have a control group.
Additionally, changes can occur in the dependent variables if you initially used identical control and experimental groups. Because the treatment is the only difference between the two groups, you can easily attribute the changes to this treatment.
Control group example
The following example shows a cosmetics company testing their newly invented lip color product using this group design:
Example
The Jane Doe cosmetics company has recently developed a long-lasting lip color product and wants to test it before its release. Testing the product first enables them to make improvements and substantiate their claims. They get study participants with the same lip color preferences and divide them into control and experimental groups.
The company assigns the experimental group the new product to use for a set time and asks them to record how long it takes for results to be visible. They also ask the control group to record how long the lip color lasts. The ones in the controlled group get a similar lip color, but they’re not using a product from the same formula.
From these records, the company can make the necessary changes.
FAQs
No. The group doesn’t receive any treatment in an experiment, but the experimental group does.
In a negative controlled group, the experiment conditions result in a negative outcome.
Experimental design is planning a set of processes to study a relationship between variables.
The independent variable changes in the experimental group but remains constant in the control group.