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What Does Experimental Group Mean In Biology

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Select A Specific Variable To Test

Experimental Design: Variables, Groups, and Controls

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.

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What Is A Control For An Experiment

A scientific control is an experiment or observation designed to minimize the effects of variables other than the independent variable . This increases the reliability of the results often through a comparison between control measurements and the other measurements. See also how does migration help animals survive

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:

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What Is Difference Between Experimental Group And Control Group

Frequently asked questions about control groups. What is the difference between a control group and an experimental group? 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.

What Is A Control In An Experiment

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The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed’s data and insights to deliver useful tips to help guide your career journey.

Many careers in medicine, science and analysis involve experiments that gather data. Understanding the role of a control, also known as the control variable or the control group, in an experiment can help you to 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.

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How Do You Explain An Experiment

An experiment is a procedure designed to test a hypothesis as part of the scientific method. The two key variables in any experiment are the independent and dependent variables. The independent variable is controlled or changed to test its effects on the dependent variable. See also what begins with a haploid cell?

Control Groups In Experiments

Control groups are essential to experimental design. When researchers are interested in the impact of a new treatment, they randomly divide their study participants into at least two groups:

  • The treatment group receives the treatment whose effect the researcher is interested in.
  • The control group receives either no treatment, a standard treatment whose effect is already known, or a placebo .

The treatment is any independent variable manipulated by the experimenters, and its exact form depends on the type of research being performed. In a medical trial, it might be a new drug or therapy. In public policy studies, it could be a new social policy that some receive and not others.

In a well-designed experiment, all variables apart from the treatment should be kept constant between the two groups. This means researchers can correctly measure the entire effect of the treatment without interference from confounding variables.

  • You pay the students in the treatment group for achieving high grades.
  • Students in the control group do not receive any money.

Studies can also include more than one treatment or control group. Researchers might want to examine the impact of multiple treatments at once, or compare a new treatment to several alternatives currently available.

  • The treatment group gets the new pill.
  • Control group 1 gets an identical-looking sugar pill
  • Control group 2 gets a pill already approved to treat high blood pressure

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Box 1 How We Chose The Studies

We searched PubMed for animal studies where experimental interventions were applied at one level but effects were examined at another level . We focused on studies with this design because regulatory authorities provide clear guidelines on replication . The following search term was used: “prenatal exposure AND offspring AND NOT review “, restricting the search to non-review articles between January 2011 and September 2016. Five-hundred abstracts were returned, and from this list we randomly selected 200 abstracts to examine further. The inclusion criteria were: English language paper multiparous mammals used in the experiments and paternal or maternal animals assigned to treatment conditions and the treatment applied to them, but the outcomes measured in the offspring. These criteria excluded studies on humans, nonhuman primates, and nonmammalian species. Papers deemed irrelevant were discarded and another randomly selected paper was included until we reached 200 papers that met the inclusion criteria. We chose 200 papers as that gives a target 95% CI width of approximately 10%. Based on a previous study we expected that 80%90% of papers would mistake pseudoreplication for genuine replication, which gives a 95% CI width of between 9% and 12% .

Data Entry And Analysis

Introduction to experimental design | High school biology | Khan Academy

The researcher has the ultimate responsibility for collecting, entering, and analyzing the data correctly. When dealing with large volumes of data, it is especially easy for data entry errors to occur . Quality assurance procedures to identify data entry errors should be developed and incorporated into the experimental design before data analysis. This process can be accomplished by directly comparing raw data for individual animals with the data entered into the computer or with compiled data for the group as a whole . The analysis of the data varies depending on the type of project and the statistics required to evaluate it. Because this topic is beyond the scope of this article, we refer the reader to the many outstanding books and articles on statistical analysis .

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Identification Of Potential Collaborators

The procedures required to carry out the experiments will determine what, if any, additional expertise is needed. It is important to identify and consult with potential collaborators at the beginning of project development to determine who will be working on the project and in what capacity . Collaborator input into the logistics and design of the experiments and proper sample acquisition are critical to ensure the validity of the data generated. Core facilities at larger research institutions provide many services that involve highly technical procedures or require expensive equipment. Identification of existing core facilities can often lead to the development of a list of potential intramural collaborators.

Analysis Of Hierarchical Data

The analysis of hierarchical data is a large topic and beyond the scope of this paper, but a simple solution to the problem of pseudoreplication is to reduce the data to a single value for each EU. Reducing the data by calculating the mean of multiple measurements on each EU will often be appropriate, but other numeric summaries such as the slope or area under the curve may capture a feature of interest better . These summary-measure or derived-variable analyses allow standard statistical tests to be used, and will often give the same answer as more complex hierarchical models .

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Control Groups In Quasi

While true experiments rely on random assignment to the treatment or control groups, quasi-experimental design uses some criterion other than randomization to assign people.

Often, these assignments are not controlled by researchers, but are pre-existing groups that have received different treatments. For example, researchers could study the effects of a new teaching method that was applied in some classes in a school but not others, or study the impact of a new policy that is implemented in one state but not in the neighboring state.

In these cases, the classes that did not use the new teaching method, or the state that did not implement the new policy, is the control group.

Control Groups In Matching Design


In correlational research, matching represents a potential alternate option when you cannot use either true or quasi-experimental designs.

In matching designs, the researcher matches individuals who received the âtreatmentâ, or independent variable under study, to others who did notâthe control group.

Each member of the treatment group thus has a counterpart in the control group identical in every way possible outside of the treatment. This ensures that the treatment is the only source of potential differences in outcomes between the two groups.

Instead, you can create a control group by matching individuals who do not smoke with those who do on age, gender, diet, level of exercise, and so on, ensuring that the only difference between the two groupsâand thus the only variable that could cause differences in their rates of lung cancerâis their use of e-cigarettes.

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What Is Control Group In A Research

The control group is composed of participants who do not receive the experimental treatment. They also closely resemble the participants who are in the experimental group or the individuals who receive the treatment. While they do not receive the treatment they do play a vital role in the research process.

A Definition Of Experiments

An experiment involves the creation of a contrived situation in order that the researcher can manipulate one or more variables whilst controlling all of the others and measuring the resultant effects. For instance, when United Fruits were considering replacing their Gros Michel variety of banana with the Valery variety, a simple experiment was first carried out. In selected retail outlets, the two varieties were switched on different days of the week and sales data examined to determine what effect the variety had on sales volumes. That is, the variety was being manipulated whilst all other variables were held constant. United Fruits found that the switch back and forth between Gros Michel and Valery had no effect upon sales. United Fruit were therefore able to replace Gros Michel with Valery.

Boyd and Westfall1 have defined experimentation as:

“…that research process in which one or more variables are manipulated under conditions which permit the collection of data which show the effects, if any, in unconfused fashion.”

When experiments are conducted within a natural setting then they are termed field experiments. The variety test carried out by United Fruits on their Gros Michel and Valery bananas is an example of a field experiment. The researcher obviously has less control over variables likely to have an effect upon the experimental variable but will strive to exert whatever control is possible.

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Basic Concepts In Experimentation

Dependency: Experiments allow marketing researchers to study the effects of an independent variable on a dependent variable. The researcher is able to manipulate the independent variable and observe what effect, if any, this has upon the value of the dependent variable. Put another way, an independent variable is one which can be manipulated independently of other variables. Independent variables are selected for inclusion in an experiment on the basis of an assumption that they are in some way related to the dependent variable being studied. It is for this reason that independent variables are on occasion referred to as explanatory variables. The dependent variable is the one under study. The researcher begins from the premise that changes in the value of the dependent variable are at least in part caused by changes in the independent variable. The experiment is designed to determine whether or not this cause and effect relationship actually exists.

Causality: A causal relationship is said to exist where the value of one variable is known to determine or influence the value of another. Green et al.3 draw a distinction between two types of causation: deterministic and probabilistic.

Where the independent variable wholly explains changes in the value of the dependent variable and the researcher is able to establish the functional relationship between the two variables then this can be expressed as follows:

y = f

Changes in demand for sugar = f

y = f.

The Difference Between Control Group And Experimental Group

Variables in Science: Independent, Dependent and Controlled!

By , published Feb 22, 2022

In a controlled experiment 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 theonly difference between the experimental and control groups, we can be sure that any differences betweenthe two are due to experimental manipulation rather than chance.

Almost all experimental studies are designed to include a control group and one or more experimental groups. In most cases, participants are randomly assigned to either a control group or an experimental group.

Because participants are randomly assigned to either group, we can assume that the groups are essentially identical with the exception of the manipulation of the independent variable in the experimental group.

It is important that every aspect of the experimental environment is the same and that the experimenters carry out the exact same procedures with both groups so researchers can confidently conclude that any differences between groups are actually due to the difference in treatments.

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A Simple Example Of A Controlled Experiment

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.

Sometimes setting up a controlled experiment can be confusing. For example, a scientist may wonder whether or not a species of bacteria needs oxygen in order to live. To test this, cultures of bacteria may be left in the air, while other cultures are placed in a sealed container of nitrogen or deoxygenated air . Which container is the control? Which is the experimental group?

Practical Aspects Of Experimental Design In Animal Research

Paula D. Johnson, D.V.M., M.S., is Executive Director, Southwest Association for Education in Biomedical Research, University of Arizona, Tucson David G. Besselsen, D.V.M., Ph.D., is Veterinary Specialist and Chief, Pathology Services, University Animal Care, University of Arizona, Tucson.

Paula D. Johnson, D.V.M., M.S., is Executive Director, Southwest Association for Education in Biomedical Research, University of Arizona, Tucson David G. Besselsen, D.V.M., Ph.D., is Veterinary Specialist and Chief, Pathology Services, University Animal Care, University of Arizona, Tucson.

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How To Develop A Control For An Experiment

Developing a control for 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:

What Is An Experimental Control Example

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Experimental controls are used in scientific experiments to prevent factors other than those being studied from affecting the outcome. For example suppose a researcher feeds an experimental artificial sweetener to thirty laboratory rats and observes that eight of them subsequently die of dehydration.

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Requirements For Genuine Replication

To ensure that the results can be extrapolated to the population of interest, there must also be replication relevant to the hypothesis being tested. For this, it helps to define the scientific or BU of interest, which is the entity that we would like to test a hypothesis or draw a conclusion about. For example, to conclude that a drug is better than a placebo, a large number of patients are required because the hypothesis is about patients. We cannot give Jim the drug and Bob the placebo, take a daily measurement for several weeks, and then make general conclusions about the drug’s efficacy. At times, the BU may not be so apparent. Suppose we hypothesise that outbred mice are smarter than inbred mice. We cannot test this hypothesis with only 1 strain of outbred and 1 strain of inbred mice, even if we have many mice from each strain. There may be intelligence differences between these 2 strains that have nothing to do with their inbred/outbred status, much like there are differences between Bob and Jim that cannot be disentangled from the drug effect. The BU is the strain and therefore we need multiple strains of both outbred and inbred mice.

Example Of An Experimental Group

Assume you want to study to determine if listening to different types of music can help with focus while studying.

You randomly assign participants to one of three groups: one group that listens to music with lyrics, one group who listens to music without lyrics, and another group that listens to no music.

The group of participants listening to no music while studying is the control group and the groups listening to music, whether with or without lyrics, are the two experimental groups.

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