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If all parts of an experiment are conducted at the same temperature, then temperature is a controlled variable. Level1studio / Getty Images Updated on January 30, 2020 A controlled variable is one which the researcher holds constant (controls) during an experiment. It is also known as a constant variable or simply as a "control." The control variable is not part of an experiment itself—it is neither the independent nor dependent variable—but it is important because it can have an effect on the results. It is not the same as a control group. Any given experiment has numerous control variables, and it's important for a scientist to try to hold all variables constant except for the independent variable. If a control variable changes during an experiment, it may invalidate the correlation between the dependent and independent variables. When possible, control variables should be identified, measured, and recorded. Examples of Controlled VariablesTemperature is a common type of controlled variable. If a temperature is held constant during an experiment, it is controlled. Other examples of controlled variables could be an amount of light, using the same type of glassware, constant humidity, or duration of an experiment. Importance of Controlled VariablesAlthough control variables may not be measured (though they are often recorded), they can have a significant effect on the outcome of an experiment. Lack of awareness of control variables can lead to faulty results or what are called "confounding variables." Additionally, noting control variables makes it easier to reproduce an experiment and establish the relationship between the independent and dependent variables. For example, say you are trying to determine whether a particular fertilizer has an effect on plant growth. The independent variable is the presence or absence of the fertilizer, while the dependent variable is the height of the plant or rate of growth. If you don't control the amount of light (e.g., you perform part of the experiment in the summer and part during the winter), you may skew your results. Discover 21 more articles on this topic A failure to isolate the controlled variables, in any experimental design, will seriously compromise the internal validity. This oversight may lead to confounding variables ruining the experiment, wasting time and resources, and damaging the researcher's reputation. In any experimental design, a researcher will be manipulating one variable, the independent variable, and studying how that affects the dependent variables. A failure to isolate the controlled variables will compromise the internal validity. Most experimental designs measures only one or two variables at a time. Any other factor, which could potentially influence the results, must be correctly controlled. Its effect upon the results must be standardized, or eliminated, exerting the same influence upon the different sample groups. For example, if you were comparing cleaning products, the brand of cleaning product would be the only independent variable measured. The level of dirt and soiling, the type of dirt or stain, the temperature of the water and the time of the cleaning cycle are just some of the variables that must be the same between experiments. Failure to standardize even one of these controlled variables could cause a confounding variable and invalidate the results.  Control GroupsIn many fields of science, especially biology and behavioral sciences, it is very difficult to ensure complete control, as there is a lot of scope for small variations. Biological processes are subject to natural fluctuations and chaotic rhythms. The key is to use established operationalization techniques, such as randomization and double blind experiments. These techniques will control and isolate these variables, as much as possible. If this proves difficult, a control group is used, which will give a baseline measurement for the unknown variables. Sound statistical analysis will then eliminate these fluctuations from the results. Most statistical tests have a certain error margin built in, and repetition and large sample groups will eradicate the unknown variables. There still needs to be constant monitoring and checks, but due diligence will ensure that the experiment is as accurate as is possible. The Value of ConsistencyControlled variables are often referred to as constants, or constant variables. It is important to ensure that all these possible variables are isolated, because a type III error may occur if an unknown factor influences the dependent variable. This is where the null hypothesis is correctly rejected, but for the wrong reason. In addition, inadequate monitoring of controlled variables is one of the most common causes of researchers wrongly assuming that a correlation leads to causality. Controlled variables are the road to failure in an experimental design, if not identified and eliminated. Designing the experiment with controls in mind is often more crucial than determining the independent variable. Poor controls can lead to confounding variables, and will damage the internal validity of the experiment. Why is it important that all variables in an experiment except for the independent variable are controlled?It's important for a scientist to try to hold all the variables constant except for the independent variable. If a control variable changes during the experiment, it may invalidate the correlation between the dependent and independent variables.
Why is it important to have control over all other variables other than independent and dependent?Why do control variables matter? Control variables enhance the internal validity of a study by limiting the influence of confounding and other extraneous variables. This helps you establish a correlational or causal relationship between your variables of interest and helps avoid research bias.
Why is it important to control all of the variables except one in an experiment?Any given experiment has numerous control variables, and it's important for a scientist to try to hold all variables constant except for the independent variable. If a control variable changes during an experiment, it may invalidate the correlation between the dependent and independent variables.
Why is it so important to control the variables What would happen if we did not control them?If control variables aren't kept constant, they could ruin your experiment. For example, you may conclude that plants grow optimally at 4 hours of light a day. However, if your plants are receiving different fertilizer levels, your experiment becomes invalid.
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Why is it important to control all variables in an experiment except for the independent variable and the dependent variable?
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