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control group , the standard to which comparisons are made in an experiment. Many experiments are designed to include a control group and one or more experimental groups; in fact, some scholars reserve the term experiment for study designs that include a control group. Ideally, the control group and the experimental groups are identical in every way except that the experimental groups are subjected to treatments or interventions believed to have an effect on the outcome of interest while the control group is not. Inclusion of a control group greatly strengthens researchers’ ability to draw conclusions from a study. Indeed, only in the presence of a control group can a researcher determine whether a treatment under investigation truly has a significant effect on an experimental group, and the possibility of making an erroneous conclusion is reduced. See also scientific method .
A typical use of a control group is in an experiment in which the effect of a treatment is unknown and comparisons between the control group and the experimental group are used to measure the effect of the treatment. For instance, in a pharmaceutical study to determine the effectiveness of a new drug on the treatment of migraines , the experimental group will be administered the new drug and the control group will be administered a placebo (a drug that is inert, or assumed to have no effect). Each group is then given the same questionnaire and asked to rate the effectiveness of the drug in relieving symptoms . If the new drug is effective, the experimental group is expected to have a significantly better response to it than the control group. Another possible design is to include several experimental groups, each of which is given a different dosage of the new drug, plus one control group. In this design, the analyst will compare results from each of the experimental groups to the control group. This type of experiment allows the researcher to determine not only if the drug is effective but also the effectiveness of different dosages. In the absence of a control group, the researcher’s ability to draw conclusions about the new drug is greatly weakened, due to the placebo effect and other threats to validity. Comparisons between the experimental groups with different dosages can be made without including a control group, but there is no way to know if any of the dosages of the new drug are more or less effective than the placebo.
It is important that every aspect of the experimental environment be as alike as possible for all subjects in the experiment. If conditions are different for the experimental and control groups, it is impossible to know whether differences between groups are actually due to the difference in treatments or to the difference in environment. For example, in the new migraine drug study, it would be a poor study design to administer the questionnaire to the experimental group in a hospital setting while asking the control group to complete it at home. Such a study could lead to a misleading conclusion, because differences in responses between the experimental and control groups could have been due to the effect of the drug or could have been due to the conditions under which the data were collected. For instance, perhaps the experimental group received better instructions or was more motivated by being in the hospital setting to give accurate responses than the control group.
In non-laboratory and nonclinical experiments, such as field experiments in ecology or economics , even well-designed experiments are subject to numerous and complex variables that cannot always be managed across the control group and experimental groups. Randomization, in which individuals or groups of individuals are randomly assigned to the treatment and control groups, is an important tool to eliminate selection bias and can aid in disentangling the effects of the experimental treatment from other confounding factors. Appropriate sample sizes are also important.
A control group study can be managed in two different ways. In a single-blind study, the researcher will know whether a particular subject is in the control group, but the subject will not know. In a double-blind study , neither the subject nor the researcher will know which treatment the subject is receiving. In many cases, a double-blind study is preferable to a single-blind study, since the researcher cannot inadvertently affect the results or their interpretation by treating a control subject differently from an experimental subject.
In a randomized and controlled psychology experiment , the researchers are examining the impact of an experimental condition on a group of participants (does the independent variable 'X' cause a change in the dependent variable 'Y'?). To determine cause and effect, there must be at least two groups to compare, the experimental group and the control group.
The participants who are in the experimental condition are those who receive the treatment or intervention of interest. The data from their outcomes are collected and compared to the data from a group that did not receive the experimental treatment. The control group may have received no treatment at all, or they may have received a placebo treatment or the standard treatment in current practice.
Comparing the experimental group to the control group allows researchers to see how much of an impact the intervention had on the participants.
Imagine that you want to do an experiment to determine if listening to music while working out can lead to greater weight loss. After getting together a group of participants, you randomly assign them to one of three groups. One group listens to upbeat music while working out, one group listens to relaxing music, and the third group listens to no music at all. All of the participants work out for the same amount of time and the same number of days each week.
In this experiment, the group of participants listening to no music while working out is the control group. They serve as a baseline with which to compare the performance of the other two groups. The other two groups in the experiment are the experimental groups. They each receive some level of the independent variable, which in this case is listening to music while working out.
In this experiment, you find that the participants who listened to upbeat music experienced the greatest weight loss result, largely because those who listened to this type of music exercised with greater intensity than those in the other two groups. By comparing the results from your experimental groups with the results of the control group, you can more clearly see the impact of the independent variable.
When it comes to using experimental groups in a psychology experiment, there are a few important things to know:
Experiments play an important role in the research process and allow psychologists to investigate cause-and-effect relationships between different variables. Having one or more experimental groups allows researchers to vary different levels or types of the experimental variable and then compare the effects of these changes against a control group. The goal of this experimental manipulation is to gain a better understanding of the different factors that may have an impact on how people think, feel, and act.
Byrd-Bredbenner C, Wu F, Spaccarotella K, Quick V, Martin-Biggers J, Zhang Y. Systematic review of control groups in nutrition education intervention research . Int J Behav Nutr Phys Act. 2017;14(1):91. doi:10.1186/s12966-017-0546-3
Steingrimsdottir HS, Arntzen E. On the utility of within-participant research design when working with patients with neurocognitive disorders . Clin Interv Aging. 2015;10:1189-1200. doi:10.2147/CIA.S81868
Oberste M, Hartig P, Bloch W, et al. Control group paradigms in studies investigating acute effects of exercise on cognitive performance—An experiment on expectation-driven placebo effects . Front Hum Neurosci. 2017;11:600. doi:10.3389/fnhum.2017.00600
Kim H. Statistical notes for clinical researchers: Analysis of covariance (ANCOVA) . Restor Dent Endod . 2018;43(4):e43. doi:10.5395/rde.2018.43.e43
Bate S, Karp NA. A common control group — Optimising the experiment design to maximise sensitivity . PLoS ONE. 2014;9(12):e114872. doi:10.1371/journal.pone.0114872
Myers A, Hansen C. Experimental Psychology . 7th Ed. Cengage Learning; 2012.
By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."
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What are control groups in research, examples of control groups in research, control group vs. experimental group, types of control groups, control groups in non-experimental research.
A control group is typically thought of as the baseline in an experiment. In an experiment, clinical trial, or other sort of controlled study, there are at least two groups whose results are compared against each other.
The experimental group receives some sort of treatment, and their results are compared against those of the control group, which is not given the treatment. This is important to determine whether there is an identifiable causal relationship between the treatment and the resulting effects.
As intuitive as this may sound, there is an entire methodology that is useful to understanding the role of the control group in experimental research and as part of a broader concept in research. This article will examine the particulars of that methodology so you can design your research more rigorously .
Suppose that a friend or colleague of yours has a headache. You give them some over-the-counter medicine to relieve some of the pain. Shortly after they take the medicine, the pain is gone and they feel better. In casual settings, we can assume that it must be the medicine that was the cause of their headache going away.
In scientific research, however, we don't really know if the medicine made a difference or if the headache would have gone away on its own. Maybe in the time it took for the headache to go away, they ate or drank something that might have had an effect. Perhaps they had a quick nap that helped relieve the tension from the headache. Without rigorously exploring this phenomenon , any number of confounding factors exist that can make us question the actual efficacy of any particular treatment.
Experimental research relies on observing differences between the two groups by "controlling" the independent variable , or in the case of our example above, the medicine that is given or not given depending on the group. The dependent variable in this case is the change in how the person suffering the headache feels, and the difference between taking and not taking the medicine is evidence (or lack thereof) that the treatment is effective.
The catch is that, between the control group and other groups (typically called experimental groups), it's important to ensure that all other factors are the same or at least as similar as possible. Things such as age, fitness level, and even occupation can affect the likelihood someone has a headache and whether a certain medication is effective.
Faced with this dynamic, researchers try to make sure that participants in their control group and experimental group are as similar as possible to each other, with the only difference being the treatment they receive.
Experimental research is often associated with scientists in lab coats holding beakers containing liquids with funny colors. Clinical trials that deal with medical treatments rely primarily, if not exclusively, on experimental research designs involving comparisons between control and experimental groups.
However, many studies in the social sciences also employ some sort of experimental design which calls for the use of control groups. This type of research is useful when researchers are trying to confirm or challenge an existing notion or measure the difference in effects.
How might a company know if an employee training program is effective? They may decide to pilot the program to a small group of their employees before they implement the training to their entire workforce.
If they adopt an experimental design, they could compare results between an experimental group of workers who participate in the training program against a control group who continues as per usual without any additional training.
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Music certainly has profound effects on psychology, but what kind of music would be most effective for concentration? Here, a researcher might be interested in having participants in a control group perform a series of tasks in an environment with no background music, and participants in multiple experimental groups perform those same tasks with background music of different genres. The subsequent analysis could determine how well people perform with classical music, jazz music, or no music at all in the background.
Suppose that you want to improve reading ability among elementary school students, and there is research on a particular teaching method that is associated with facilitating reading comprehension. How do you measure the effects of that teaching method?
A study could be conducted on two groups of otherwise equally proficient students to measure the difference in test scores. The teacher delivers the same instruction to the control group as they have to previous students, but they teach the experimental group using the new technique. A reading test after a certain amount of instruction could determine the extent of effectiveness of the new teaching method.
As you can see from the three examples above, experimental groups are the counterbalance to control groups. A control group offers an essential point of comparison. For an experimental study to be considered credible, it must establish a baseline against which novel research is conducted.
Researchers can determine the makeup of their experimental and control groups from their literature review . Remember that the objective of a review is to establish what is known about the object of inquiry and what is not known. Where experimental groups explore the unknown aspects of scientific knowledge, a control group is a sort of simulation of what would happen if the treatment or intervention was not administered. As a result, it will benefit researchers to have a foundational knowledge of the existing research to create a credible control group against which experimental results are compared, especially in terms of remaining sensitive to relevant participant characteristics that could confound the effects of your treatment or intervention so that you can appropriately distribute participants between the experimental and control groups.
There are multiple control groups to consider depending on the study you are looking to conduct. All of them are variations of the basic control group used to establish a baseline for experimental conditions.
This kind of control group is common when trying to establish the effects of an experimental treatment against the absence of treatment. This is arguably the most straightforward approach to an experimental design as it aims to directly demonstrate how a certain change in conditions produces an effect.
In this case, the control group receives some sort of treatment under the exact same procedures as those in the experimental group. The only difference in this case is that the treatment in the placebo control group has already been judged to be ineffective, except that the research participants don't know that it is ineffective.
Placebo control groups (or negative control groups) are useful for allowing researchers to account for any psychological or affective factors that might impact the outcomes. The negative control group exists to explicitly eliminate factors other than changes in the independent variable conditions as causes of the effects experienced in the experimental group.
Contrasted with a no-treatment control group, a positive control group employs a treatment against which the treatment in the experimental group is compared. However, unlike in a placebo group, participants in a positive control group receive treatment that is known to have an effect.
If we were to use our first example of headache medicine, a researcher could compare results between medication that is commonly known as effective against the newer medication that the researcher thinks is more effective. Positive control groups are useful for validating experimental results when compared against familiar results.
Rather than study participants in control group conditions, researchers may employ existing data to create historical control groups. This form of control group is useful for examining changing conditions over time, particularly when incorporating past conditions that can't be replicated in the analysis.
Qualitative research more often relies on non-experimental research such as observations and interviews to examine phenomena in their natural environments. This sort of research is more suited for inductive and exploratory inquiries, not confirmatory studies meant to test or measure a phenomenon.
That said, the broader concept of a control group is still present in observational and interview research in the form of a comparison group. Comparison groups are used in qualitative research designs to show differences between phenomena, with the exception being that there is no baseline against which data is analyzed.
Comparison groups are useful when an experimental environment cannot produce results that would be applicable to real-world conditions. Research inquiries examining the social world face challenges of having too many variables to control, making observations and interviews across comparable groups more appropriate for data collection than clinical or sterile environments.
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As someone who is deeply interested in the field of research, you may have heard the terms control group and experimental group thrown around a lot. If you’re not very familiar with these terms, it can be daunting to determine the role they play in research and why they are so important. In layman’s terms, a control group is a group that does not receive any experimental treatment and is used as a benchmark for the group that does receive the treatment. Meanwhile, the experimental group is a group that receives the treatment and is compared to the control group that does not receive the treatment. To put it simply, the main difference between a control group and an experimental group is whether or not they receive the experimental treatment.
Table of Contents
A control group is a group in an experiment that does not receive the experimental treatment and is used as a comparison for the group that does receive the treatment. It is a critical aspect of experimental research to determine whether the treatment caused the outcome rather than another factor. The control group ensures that any observed effects can be attributed to the treatment and not a result of other variables. The quality of the control group can affect the validity of the experiment. Therefore, researchers must carefully design and select participants for the control group to ensure that it accurately represents the population and provides meaningful results. Overall, control groups are essential to gain accurate and reliable results in experimental research.
Key differences between control group and experimental group, control group vs. experimental group similarities.
The control group and experimental group are two essential components of any research study. The main similarity between these groups is that they are both used to assess the effects of a treatment or intervention. The control group is intended to provide a baseline measurement of the outcomes that are expected in the absence of the intervention. In contrast, the experimental group is exposed to the intervention or treatment and is observed for any changes or improvements in outcomes. In summary, both groups serve as comparisons for one another, and their use increases the credibility and validity of research findings.
Control group pros & cons, control group pros, control group cons, experimental group pros & cons, experimental group pros.
The Experimental Group, in scientific studies and experimentation, is a group that receives the experimental treatment and is compared to a control group that does not receive the treatment. There are several advantages or pros of this group. First, the experimental group allows researchers to determine the effectiveness of a new treatment or procedure. Second, it helps in identifying side effects of the treatment on the subjects. Third, it provides clear evidence regarding the cause and effect relationships between variables. Additionally, the experimental group enables researchers to validate their findings and test the hypothesis. These benefits make the Experimental Group essential in accurately assessing the effectiveness of new treatments or procedures.
Comparison table: 5 key differences between control group and experimental group.
Purpose | Used as a comparison to the experimental group | Receives the intervention being tested |
Treatment | Receives no intervention or a placebo | Receives the treatment being tested |
Randomization | Randomly selected from the population being studied | Randomly selected from the population being studied |
Sample Size | Large enough to provide statistical power | Large enough to provide statistical power |
Analysis | Statistical analysis is performed to compare outcomes | Statistical analysis is performed to compare outcomes |
Comparison video, conclusion: what is the difference between control group and experimental group.
In conclusion, understanding the difference between a control group and an experimental group is crucial in designing and conducting reliable experiments. The control group serves as a baseline, allowing researchers to compare the effects of the experimental treatment. Without a control group, it is difficult to determine whether any observed effects are due to the treatment or to other factors. By contrast, the experimental group receives the treatment and is used to evaluate the effects of the intervention. By carefully controlling for different factors, scientists can use these groups to test hypotheses and draw meaningful conclusions about the impact of different treatments on the outcomes of interest.
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Statistics By Jim
Making statistics intuitive
By Jim Frost 3 Comments
A control group in an experiment does not receive the treatment. Instead, it serves as a comparison group for the treatments. Researchers compare the results of a treatment group to the control group to determine the effect size, also known as the treatment effect.
Imagine that a treatment group receives a vaccine and it has an infection rate of 10%. By itself, you don’t know if that’s an improvement. However, if you also have an unvaccinated control group with an infection rate of 20%, you know the vaccine improved the outcome by 10 percentage points.
By serving as a basis for comparison, the control group reveals the treatment’s effect.
Related post : Effect Sizes in Statistics
Most experiments include a control group and at least one treatment group. In an ideal experiment, the subjects in all groups start with the same overall characteristics except that those in the treatment groups receive a treatment. When the groups are otherwise equivalent before treatment begins, you can attribute differences after the experiment to the treatments.
Randomized controlled trials (RCTs) assign subjects to the treatment and control groups randomly. This process helps ensure the groups are comparable when treatment begins. Consequently, treatment effects are the most likely cause for differences between groups at the end of the study. Statisticians consider RCTs to be the gold standard. To learn more about this process, read my post, Random Assignment in Experiments .
Observational studies either can’t use randomized groups or don’t use them because they’re too costly or problematic. In these studies, the characteristics of the control group might be different from the treatment groups at the start of the study, making it difficult to estimate the treatment effect accurately at the end. Case-Control studies are a specific type of observational study that uses a control group.
For these types of studies, analytical methods and design choices, such as regression analysis and matching, can help statistically mitigate confounding variables. Matching involves selecting participants with similar characteristics. For each participant in the treatment group, the researchers find a subject with comparable traits to include in the control group. To learn more about this type of study and matching, read my post, Observational Studies Explained .
Control groups are key way to increase the internal validity of an experiment. To learn more, read my post about internal and external validity .
Randomized versus non-randomized control groups are just several of the different types you can have. We’ll look at more kinds later!
Related posts : When to Use Regression Analysis
Suppose we want to determine whether regular vitamin consumption affects the risk of dying. Our experiment has the following two experimental groups:
In this experiment, we randomly assign subjects to the two groups. Because we use random assignment, the two groups start with similar characteristics, including healthy habits, physical attributes, medical conditions, and other factors affecting the outcome. The intentional introduction of vitamin supplements in the treatment group is the only systematic difference between the groups.
After the experiment is complete, we compare the death risk between the treatment and control groups. Because the groups started roughly equal, we can reasonably attribute differences in death risk at the end of the study to vitamin consumption. By having the control group as the basis of comparison, the effect of vitamin consumption becomes clear!
Researchers can use different types of control groups in their experiments. Earlier, you learned about the random versus non-random kinds, but there are other variations. You can use various types depending on your research goals, constraints, and ethical issues, among other things.
The group introduces a condition that the researchers expect won’t have an effect. This group typically receives no treatment. These experiments compare the effectiveness of the experimental treatment to no treatment. For example, in a vaccine study, a negative control group does not get the vaccine.
Positive control groups typically receive a standard treatment that science has already proven effective. These groups serve as a benchmark for the performance of a conventional treatment. In this vein, experiments with positive control groups compare the effectiveness of a new treatment to a standard one.
For example, an old blood pressure medicine can be the treatment in a positive control group, while the treatment group receives the new, experimental blood pressure medicine. The researchers want to determine whether the new treatment is better than the previous treatment.
In these studies, subjects can still take the standard medication for their condition, a potentially critical ethics issue.
Placebo control groups introduce a treatment lookalike that will not affect the outcome. Standard examples of placebos are sugar pills and saline solution injections instead of genuine medicine. The key is that the placebo looks like the actual treatment. Researchers use this approach when the recipients’ belief that they’re receiving the treatment might influence their outcomes. By using placebos, the experiment controls for these psychological benefits. The researchers want to determine whether the treatment performs better than the placebo effect.
Learn more about the Placebo Effect .
If the subject’s awareness of their group assignment might affect their outcomes, the researchers can use a blinded experimental design that does not tell participants their group membership. Typically, blinded control groups will receive placebos, as described above. In a double-blinded control group, both subjects and researchers don’t know group assignments.
When there is a waitlist to receive a new treatment, those on the waitlist can serve as a control group until they receive treatment. This type of design avoids ethical concerns about withholding a better treatment until the study finishes. This design can be a variation of a positive control group because the subjects might be using conventional medicines while on the waitlist.
When historical data for a comparison group exists, it can serve as a control group for an experiment. The group doesn’t exist in the study, but the researchers compare the treatment group to the existing data. For example, the researchers might have infection rate data for unvaccinated individuals to compare to the infection rate among the vaccinated participants in their study. This approach allows everyone in the experiment to receive the new treatment. However, differences in place, time, and other circumstances can reduce the value of these comparisons. In other words, other factors might account for the apparent effects.
December 19, 2021 at 9:17 am
Thank you very much Jim for your quick and comprehensive feedback. Extremely helpful!! Regards, Arthur
December 17, 2021 at 4:46 pm
Thank you very much Jim, very interesting article.
Can I select a control group at the end of intervention/experiment? Currently I am managing a project in rural Cambodia in five villages, however I did not select any comparison/control site at the beginning. Since I know there are other villages which have not been exposed to any type of intervention, can i select them as a control site during my end-line data collection or it will not be a legitimate control? Thank you very much, Arthur
December 18, 2021 at 1:51 am
You might be able to use that approach, but it’s not ideal. The ideal is to have control groups defined at the beginning of the study. You can use the untreated villages as a type of historical control groups that I talk about in this article. Or, if they’re awaiting to receive the intervention, it might be akin to a waitlist control group.
If you go that route, you’ll need to consider whether there was some systematic reason why these villages have not received any intervention. For example, are the villages in question more remote? And, if there is a systematic reason, would that affect your outcome variable? More generally, are they systematically different? How well do the untreated villages represent your target population?
If you had selected control villages at the beginning, you’d have been better able to ensure there weren’t any systematic differences between the villages receiving interventions and those that didn’t.
If the villages that didn’t receive any interventions are systematically different, you’ll need to incorporate that into your interpretation of the results. Are they different in ways that affect the outcomes you’re measuring? Can those differences account for the difference in outcomes between the treated and untreated villages? Hopefully, you’d be able to measure those differences between untreated/treated villages.
So, yes, you can use that approach. It’s not perfect and there will potentially be more things for you to consider and factor into your conclusions. Despite these drawbacks, it’s possible that using a pseudo control group like that is better than not doing that because at least you can make comparisons to something. Otherwise, you won’t know whether the outcomes in the intervention villages represent an improvement! Just be aware of the extra considerations!
Best of luck with your research!
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Methodology
Published on April 19, 2021 by Pritha Bhandari . Revised on June 22, 2023.
In experiments , researchers manipulate independent variables to test their effects on dependent variables. In a controlled experiment , all variables other than the independent variable are controlled or held constant so they don’t influence the dependent variable.
Controlling variables can involve:
Why does control matter in experiments, methods of control, problems with controlled experiments, other interesting articles, frequently asked questions about controlled experiments.
Control in experiments is critical for internal validity , which allows you to establish a cause-and-effect relationship between variables. Strong validity also helps you avoid research biases , particularly ones related to issues with generalizability (like sampling bias and selection bias .)
Extraneous variables are factors that you’re not interested in studying, but that can still influence the dependent variable. For strong internal validity, you need to remove their effects from your experiment.
You can control some variables by standardizing your data collection procedures. All participants should be tested in the same environment with identical materials. Only the independent variable (e.g., ad color) should be systematically changed between groups.
Other extraneous variables can be controlled through your sampling procedures . Ideally, you’ll select a sample that’s representative of your target population by using relevant inclusion and exclusion criteria (e.g., including participants from a specific income bracket, and not including participants with color blindness).
By measuring extraneous participant variables (e.g., age or gender) that may affect your experimental results, you can also include them in later analyses.
After gathering your participants, you’ll need to place them into groups to test different independent variable treatments. The types of groups and method of assigning participants to groups will help you implement control in your experiment.
Controlled experiments require control groups . Control groups allow you to test a comparable treatment, no treatment, or a fake treatment (e.g., a placebo to control for a placebo effect ), and compare the outcome with your experimental treatment.
You can assess whether it’s your treatment specifically that caused the outcomes, or whether time or any other treatment might have resulted in the same effects.
To test the effect of colors in advertising, each participant is placed in one of two groups:
To avoid systematic differences and selection bias between the participants in your control and treatment groups, you should use random assignment .
This helps ensure that any extraneous participant variables are evenly distributed, allowing for a valid comparison between groups .
Random assignment is a hallmark of a “true experiment”—it differentiates true experiments from quasi-experiments .
Masking in experiments means hiding condition assignment from participants or researchers—or, in a double-blind study , from both. It’s often used in clinical studies that test new treatments or drugs and is critical for avoiding several types of research bias .
Sometimes, researchers may unintentionally encourage participants to behave in ways that support their hypotheses , leading to observer bias . In other cases, cues in the study environment may signal the goal of the experiment to participants and influence their responses. These are called demand characteristics . If participants behave a particular way due to awareness of being observed (called a Hawthorne effect ), your results could be invalidated.
Using masking means that participants don’t know whether they’re in the control group or the experimental group. This helps you control biases from participants or researchers that could influence your study results.
You use an online survey form to present the advertisements to participants, and you leave the room while each participant completes the survey on the computer so that you can’t tell which condition each participant was in.
Although controlled experiments are the strongest way to test causal relationships, they also involve some challenges.
Especially in research with human participants, it’s impossible to hold all extraneous variables constant, because every individual has different experiences that may influence their perception, attitudes, or behaviors.
But measuring or restricting extraneous variables allows you to limit their influence or statistically control for them in your study.
Controlled experiments have disadvantages when it comes to external validity —the extent to which your results can be generalized to broad populations and settings.
The more controlled your experiment is, the less it resembles real world contexts. That makes it harder to apply your findings outside of a controlled setting.
There’s always a tradeoff between internal and external validity . It’s important to consider your research aims when deciding whether to prioritize control or generalizability in your experiment.
If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.
Research bias
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In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:
Depending on your study topic, there are various other methods of controlling variables .
An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.
Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:
When designing the experiment, you decide:
Experimental design is essential to the internal and external validity of your experiment.
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In a comparative experiment, the experimental group (aka the treatment group) is the group being tested for a reaction to a change in the variable. There may be experimental groups in a study, each testing a different level or amount of the variable. The other type of group, the control group , can show the effects of the variable by having a set amount, or none, of the variable. The experimental groups vary in the level of variable they are exposed to, which shows the effects of various levels of a variable on similar organisms.
In biological experiments, the subjects being studied are often living organisms. In such cases, it is desirable that all the subjects be closely related, in order to reduce the amount of genetic variation present in the experiment. The complicated interactions between genetics and the environment can cause very peculiar results when exposed to the same variable. If the organisms being tested are not related, the results could be the effects of the genetics and not the variable. This is why new human drugs must be rigorously tested in a variety of animals before they can be tested on humans. These different experimental groups allow researchers to see the effects of their drug on different genetics. By using animals that are closer and closer in their relation to humans, eventually human trials can take place without severe risks for the first people to try the drug.
A simple experiment.
A student is conducting an experiment on the effects music has on growing plants. The student wants to know if music can help plants grow and, if so, which type of music the plants prefer. The students divide a group of plants in to two main groups, the control group and the experimental group. The control group will be kept in a room with no music, while the experimental group will be further divided into smaller experimental groups. Each of the experimental groups is placed in a separate room, with a different type of music.
Ideally, each room would have many plants in it, and all the plants used in the experiment would be clones of the same plant. Even more ideally, the plant would breed true, or would be homozygous for all genes. This would introduce the smallest amount of genetic variation into the experiment. By limiting all other variables, such as the temperature and humidity, the experiment can determine with validity that the effects produced in each room are attributable to the music, and nothing else.
To study the effects of variable on many organisms at once, scientist sometimes study ecosystems as a whole. The productivity of these ecosystems is often determined by the amount of oxygen they produce, which is an indication of how much algae is present. Ecologists sometimes study the interactions of organisms on these environments by excluding or adding organisms to an experimental group of ecosystems, and test the effects of their variable against ecosystems with no tampering. This method can sometimes show the drastic effects that various organisms have on an ecosystem.
Many experiments of this kind take place, and a common theme is to separate a single ecosystem into parts, with artificial divisions. Thus, a river could be separated by netting it into areas with and without bugs. The area with no nets allows bugs into the water. The bugs not only eat algae, but die and provide nutrients for the algae to grow. Without the bugs, various effects can be seen on the experimental portion of the river, covered by netting. The levels of oxygen in the water in each system can be measured, as well as other indicators of water quality. By comparing these groups, ecologists can begin to discern the complex relationships between populations of organisms in the environment.
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In experiments, controls are factors that you hold constant or don't expose to the condition you are testing. By creating a control, you make it possible to determine whether the variables alone are responsible for an outcome. Although control variables and the control group serve the same purpose, the terms refer to two different types of controls which are used for different kinds of experiments.
A student places a seedling in a dark closet, and the seedling dies. The student now knows what happened to the seedling, but he doesn't know why. Perhaps the seedling died from lack of light, but it might also have died because it was already sickly, or because of a chemical kept in the closet, or for any number of other reasons.
In order to determine why the seedling died, it is necessary to compare that seedling's outcomes to another identical seedling outside the closet. If the closeted seedling died while the seedling kept in sunshine stayed alive, it's reasonable to hypothesize that darkness killed the closeted seedling.
Even if the closeted seedling died while the seedling placed in sunshine lived, the student would still have unresolved questions about her experiment. Might there be something about the particular seedlings that caused the results she saw? For example, might one seedling have been healthier than the other to start with?
To answer all of her questions, the student might choose to put several identical seedlings in a closet and several in the sunshine. If at the end of a week, all of the closeted seedlings are dead while all of the seedlings kept in the sunshine are alive, it is reasonable to conclude that the darkness killed the seedlings.
A control variable is any factor you control or hold constant during an experiment. A control variable is also called a controlled variable or constant variable.
If you are studying the effect of the amount of water on seed germination, control variables might include temperature, light, and type of seed. In contrast, there may be variables you can't easily control, such as humidity, noise, vibration, and magnetic fields.
Ideally, a researcher wants to control every variable, but this isn't always possible. It's a good idea to note all recognizable variables in a lab notebook for reference.
A control group is a set of experimental samples or subjects that are kept separate and aren't exposed to the independent variable .
In an experiment to determine whether zinc helps people recover faster from a cold, the experimental group would be people taking zinc, while the control group would be people taking a placebo (not exposed to extra zinc, the independent variable).
A controlled experiment is one in which every parameter is held constant except for the experimental (independent) variable. Usually, controlled experiments have control groups. Sometimes a controlled experiment compares a variable against a standard.
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Almost all experimental studies are designed to include a control group and one or more experimental groups, each serving a different purpose. In most cases, participants are randomly assigned to either a control or experimental group.
Experimental groups are usually manipulated to try and change the out come of the experiment. Control groups are usually kept as natural or unchanged to provide a normal outcome for comparison in the experiment. Read the article to learn more about the two.
The experimental group, is the group of subjects or participants that receives the experimental treatment, intervention or condition being studied. In other words, it is a group of items, animals or people being tested, which have one variable or condition changed from the other groups in the experiment. The variable is usually stated in the hypothesis and is the main focus of the experiment.
Experimental group is exposed to changes in the independent variable being tested. The values of the independent variable and the impact on the dependent variable are recorded. An experiment may include multiple experimental groups at one time.
Researchers will compare the responses of the experimental group to those of a control group to see if the independent variable impacted the participants.
An experiment must have at least one control group and one experimental group; however, a single experiment can include multiple experimental groups, which are all compared against the control group.
Having multiple experimental groups enables researchers to vary different levels of an experimental variable and compare the effects of these changes to the control group and among each other.
An example of an experimental group would be if someone wanted to see if music helps people sleep longer. The experimental population could be divided into two groups. One group would track the length of time they sleep each night without music playing. The other group would track the length of time they sleep each night when listening to music. This group would be your experimental group because something has been changed in this group. Listening to music while they sleep. This group is being “experimented” on.
A control group is a fundamental component of experimental research design, and its primary purpose is to serve as a baseline or reference group against which the experimental group is compared. In other words, it is is a collection of factors that remain constant throughout an experiment.
The control group allows researchers to assess the natural course or behavior of the subjects in the absence of the experimental intervention. This baseline comparison helps determine whether any observed changes in the experimental group can be attributed to the treatment or are simply a result of the normal variation or other factors.
While all experiments have an experimental group, not all experiments require a control group. Controls are extremely useful where the experimental conditions are complex and difficult to isolate. Experiments that use control groups are called controlled experiments.
Unlike the experimental group, the control group is not exposed to the independent variable under investigation. So, it provides a baseline against which any changes in the experimental group can be compared.
In comparative experiments, members of a control group receive a standard treatment, a placebo, or no treatment at all. There may be more than one treatment group, more than one control group, or both.
A simple example of a controlled experiment may be used to determine whether or not plants need to be watered to live. The control group would be plants that are not watered. The experimental group would consist of plants that receive water. A clever scientist would wonder whether too much watering might kill the plants and would set up several experimental groups, each receiving a different amount of water.
Positive and negative controls are two other types of control groups:
Aspect | Control Group | Experimental Group |
---|---|---|
Purpose | Serves as a baseline or reference group. | Receives the experimental treatment or condition. |
Treatment | Does not receive the experimental treatment. | Receives the experimental treatment or condition. |
Randomization | Subjects may be randomly assigned to this group. | Subjects are randomly assigned to this group. |
Blinding | Can be single-blind or double-blind. | Can be single-blind or double-blind. |
Data Collection | Provides baseline data for comparison. | Data collected to assess the treatment’s effects. |
Psychology experiments | Control groups might be exposed to a neutral condition or a placebo. | Experimental group is exposed to the variable being studied. |
Randomization
Outcome Measurement
Hypothesis Testing
Statistical Analysis
https://doi.org/10.1136/bmjebm-2024-113088
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As a heart surgeon, you regularly perform aortic valve replacement, using a median sternotomy technique. However, you are aware that less-invasive options are available, and you wonder whether these may be as effective and safe as your current practice. After searching the Cochrane Library, you find a systematic review, ‘Limited versus full sternotomy for aortic valve replacement 1 ’. The authors of the review conclude that ‘upper hemi‐sternotomy may have little to no effect on mortality versus full median sternotomy (risk ratio (RR) 0.93, 95% CI 0.45 to 1.94)’. What does this statistic mean in practice?
It is important to remember that the risk ratio is a relative effect measure and does not tell us about the actual numbers of people who may experience an event. In the example of the RCT above, if only five people in the experimental group and two people in the control group experienced an adverse event, the risk ratio would still be 2.50 (0.05/0.02). Understanding this may result in a different clinical decision.
The risk ratio …
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Contributors RR drafted the article. AK and KD contributed to writing and editing the article. All three authors approved the final version.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests RR and AK are employees of Cochrane. KD is a former employee of Cochrane.
Provenance and peer review Not commissioned; internally peer reviewed.
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Tracking ovine pulmonary adenocarcinoma development using an experimental jaagsiekte sheep retrovirus infection model.
2. materials and methods, 2.1. animals, 2.2. jsrv production and in vitro quantification, 2.3. general anaesthesia, 2.4. trans-thoracic ultrasound scanning, 2.5. computed tomography scanning, 2.6. bronchoscopy, 2.7. post mortem examination, 2.8. histopathology and immunohistochemistry, 2.9. ct image analysis, 2.10. statistical analysis and calculation of tumour volumes, 3.1. local bronchosopic instillation resulted in high overall jsrv infection and opa development rates, 3.2. all sheep gained weight and remained subclinical during the study period, 3.3. ultrasound and ct are able to identify changes consistent with opa diagnosis, 3.4. opa tumour growth rates are variable but can be rapid, 3.5. ultrasound and ct are highly correlated in their ability to track tumour development over time, 4. discussion, 5. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.
Click here to enlarge figure
Phase | Drug | Manufacturer | Dose (mg/kg) | Route |
---|---|---|---|---|
Sedation | Medetomidine | ‘Medetor’; Virbac Ltd., Suffolk, UK | 0.005 | i.v. |
in combination with | ||||
Ketamine | ‘Ketamidor’; Chanelle Vet Ltd., Hungerford, UK | 0.5 | i.v. | |
Induction | Propofol | ‘Propofol’; Fresenius Kabi, Cheshire, UK | To effect * (e.g., 2–5) | i.v. |
Maintenance | Isoflurane | ‘Isofane’; Piramal Critical Care Ltd., West Drayton, UK | Inhaled | |
Recovery | Atipamezole | ‘Atipam’; Dechra, Eurovet Animal Health BV, Bladel, The Netherlands | 0.025 | i.m. |
Group | JSRV Low Dose | JSRV Intermediate Dose | JSRV High Dose | Control | ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Sheep No. | 14 | 18 | 17 | 13 | 16 | 15 | 24 | 20 | 23 | 21 | 22 | 19 | 25 | 26 | 27 | 28 | 29 | 30 | 1–6 | |
Sex | M | F | F | M | F | M | M | F | M | F | M | F | M | M | M | F | F | F | M&F | |
Months post-instil | 4 | 6 | 6 | 9 | 9 | 9 | 7 | 7 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | |
IHC Sample Site | L1 | ++ | ++ | + | - | - | - | ++ | - | - | - | - | - | - | - | - | - | - | - | - |
L2 | ++ | ++ | - | - | - | - | ++ | - | - | - | - | - | - | - | - | - | - | - | - | |
L3 | ++ | ++ | ++ | + | + | - | ++ | ++ | + | ++ | + | - | ++ | + | + | + | + | + | - | |
L4 | ++ | ++ | - | - | - | - | ++ | - | - | - | - | - | - | - | - | - | - | - | - | |
L5 | ns | - | - | - | - | - | - | ++ | - | - | - | - | - | - | - | - | - | - | - | |
L6 | ns | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | |
L7 | ns | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | |
L8 | ns | - | - | - | - | - | ++ | - | - | - | - | - | - | - | - | - | - | - | - | |
L9 | + | ++ | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | |
L10 | - | ++ | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | |
Advanced lesions | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||||||||
Early lesions | ✓ | ✓ | ||||||||||||||||||
No gross lesions | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||||
Negative | ✓ | ✓ | ✓ |
Sheep No. | Tumour Volume Doubling Time (Days) |
---|---|
14 | 7.39 |
17 | 15.37 |
18 | 19.56 |
20 | 17.20 |
24 | 14.59 |
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
Cousens, C.; Meehan, J.; Collie, D.; Wright, S.; Chang, Z.; Todd, H.; Moore, J.; Grant, L.; Daniel, C.R.; Tennant, P.; et al. Tracking Ovine Pulmonary Adenocarcinoma Development Using an Experimental Jaagsiekte Sheep Retrovirus Infection Model. Genes 2024 , 15 , 1019. https://doi.org/10.3390/genes15081019
Cousens C, Meehan J, Collie D, Wright S, Chang Z, Todd H, Moore J, Grant L, Daniel CR, Tennant P, et al. Tracking Ovine Pulmonary Adenocarcinoma Development Using an Experimental Jaagsiekte Sheep Retrovirus Infection Model. Genes . 2024; 15(8):1019. https://doi.org/10.3390/genes15081019
Cousens, Chris, James Meehan, David Collie, Steven Wright, Ziyuan Chang, Helen Todd, Jo Moore, Lynn Grant, Carola R. Daniel, Peter Tennant, and et al. 2024. "Tracking Ovine Pulmonary Adenocarcinoma Development Using an Experimental Jaagsiekte Sheep Retrovirus Infection Model" Genes 15, no. 8: 1019. https://doi.org/10.3390/genes15081019
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IMAGES
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COMMENTS
In research, the control group is the one not exposed to the variable of interest (the independent variable) and provides a baseline for comparison. The experimental group, on the other hand, is exposed to the independent variable. Comparing results between these groups helps determine if the independent variable has a significant effect on the outcome (the dependent variable).
The control group and experimental group are compared against each other in an experiment. The only difference between the two groups is that the independent variable is changed in the experimental group. The independent variable is "controlled", or held constant, in the control group. A single experiment may include multiple experimental ...
A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn't receive the experimental treatment.. However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group's outcomes before and after a treatment (instead of comparing outcomes between different groups).
Three types of experimental designs are commonly used: 1. Independent Measures. Independent measures design, also known as between-groups, is an experimental design where different participants are used in each condition of the independent variable. This means that each condition of the experiment includes a different group of participants.
A control group is not the same thing as a control variable. A control variable or controlled variable is any factor that is held constant during an experiment. Examples of common control variables include temperature, duration, and sample size. The control variables are the same for both the control and experimental groups.
In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.
In experimental research, random assignment is a way of placing participants from your sample into different groups using randomisation. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.
Table of Contents control group, the standard to which comparisons are made in an experiment.Many experiments are designed to include a control group and one or more experimental groups; in fact, some scholars reserve the term experiment for study designs that include a control group. Ideally, the control group and the experimental groups are identical in every way except that the experimental ...
Positive control groups: In this case, researchers already know that a treatment is effective but want to learn more about the impact of variations of the treatment.In this case, the control group receives the treatment that is known to work, while the experimental group receives the variation so that researchers can learn more about how it performs and compares to the control.
Experiments play an important role in the research process and allow psychologists to investigate cause-and-effect relationships between different variables. Having one or more experimental groups allows researchers to vary different levels or types of the experimental variable and then compare the effects of these changes against a control group.
The alterations made to this group are deliberate and strategic, aiming to explore the effects of specific changes or treatments. Comparing the outcomes from the experimental group with those of the control group allows researchers to deduce the impact of the variable being tested, thereby, providing a framework for interpreting the results.
A control group is typically thought of as the baseline in an experiment. In an experiment, clinical trial, or other sort of controlled study, there are at least two groups whose results are compared against each other. The experimental group receives some sort of treatment, and their results are compared against those of the control group ...
The key differences between control group and experimental group are that the control group serves as a baseline, while the experimental group allows researchers to evaluate the effects of an experimental intervention. Additionally, experimental results are more reliable and valid when compared to the control group, demonstrating the importance ...
There are two groups in the experiment, and they are identical except that one receives a treatment (water) while the other does not. The group that receives the treatment in an experiment (here, the watered pot) is called the experimental group, while the group that does not receive the treatment (here, the dry pot) is called the control group.The control group provides a baseline that lets ...
Experimental and control groups are the two main groups found in an experiment, each serving a slightly different purpose. Experimental groups are being manipulated to try and change the out come ...
A control group in an experiment does not receive the treatment. Instead, it serves as a comparison group for the treatments. Researchers compare the results of a treatment group to the control group to determine the effect size, also known as the treatment effect.. A control group is important because it is a benchmark that allows scientists to draw conclusions about the treatment's ...
A control group in a scientific experiment is a group separated from the rest of the experiment, where the independent variable being tested cannot influence the results. This isolates the independent variable's effects on the experiment and can help rule out alternative explanations of the experimental results. Control groups can also be separated into two other types: positive or negative.
A control group that's presented with red advertisements for a fast food meal. An experimental group that's presented with green advertisements for the same fast food meal. Only the color of the ad is different between groups, and all other aspects of the design are the same. Random assignment
Experimental Group Definition. In a comparative experiment, the experimental group (aka the treatment group) is the group being tested for a reaction to a change in the variable. There may be experimental groups in a study, each testing a different level or amount of the variable. The other type of group, the control group, can show the effects ...
Treatment and control groups. For a Linux kernel feature governing computing resources, sometimes known as a hypothesis groups, see cgroups. In the design of experiments, hypotheses are applied to experimental units in a treatment group. [1] In comparative experiments, members of a control group receive a standard treatment, a placebo, or no ...
A control group is a set of experimental samples or subjects that are kept separate and aren't exposed to the independent variable . In an experiment to determine whether zinc helps people recover faster from a cold, the experimental group would be people taking zinc, while the control group would be people taking a placebo (not exposed to ...
Almost all experimental studies are designed to include a control group and one or more experimental groups, each serving a different purpose. In most cases, participants are randomly assigned to either a control or experimental group. Experimental groups are usually manipulated to try and change the out come of the experiment.
What is a Control Group? Separated from where Independent Variable can be increased. What is a Experimental Group? Group that receives the Variable. What is the difference between the two? One group is exposed to the experiment while the other is not. Study with Quizlet and memorize flashcards containing terms like What is a Control Group ...
The SFT analysis of the experimental group and the control group found that Int-2, Int-1, Con-1, and Con-2 were significantly different between males and females. The EAEBI scale analysis found that the male experimental group had the largest difference in awareness effect ( F = 9.47, p < .05), and the female experimental group had the largest ...
In the control group, 20 out of 100 people who received a placebo experienced at least one side effect: the risk of the event was 20/100, or 0.2. The risk ratio was therefore 0.5/0.2, or 2.50. This means that the probability of experiencing at least one side effect was more than double in the experimental group compared with the control group.
Ovine pulmonary adenocarcinoma (OPA) is an infectious, neoplastic lung disease of sheep that causes significant animal welfare and economic issues throughout the world. Understanding OPA pathogenesis is key to developing tools to control its impact. Central to this need is the availability of model systems that can monitor and track events after Jaagsiekte sheep retrovirus (JSRV) infection ...