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When an experiment is conducted for the purpose of determining the effect of a single variable of interest on a particular system, a scientific control is used to minimize the unintended influence of other variables on the same system. Such extraneous variables include researcher bias, environmental changes, and biological variation. Scientific controls ensure that data are valid, and are a vital part of the scientific method.


Controlled experiments

An experiment which uses controls is called a controlled experiment, and usually separates research subjects into two groups: A treatment group and a control group. The control group is practically identical to the treatment group, except for the single variable of interest whose effect is being tested, which is only applied to the treatment group. A good example would be a drug trial. The sample or group receiving the drug would be the treatment group, and the one receiving the placebo would be the control group.[citation needed]

Examples of controls

In testing a drug, it is important to carefully verify that the supposed effects of the drug are produced only by the drug itself. Physicians achieve this with a double-blind study in a clinical trial: two (statistically) identical groups of patients are compared, one of which receives the drug and one of which receives a placebo. Neither the patients nor the doctor know which group receives the real drug, which serves both to curb bias and to isolate the effects of the drug.

In experiments involving a surgical procedure, a sham-operated group is used to ensure that the data reflect the effects of the experiment itself, and are not a consequence of the surgery.

In experiments where crop yield is affected (e.g. soil fertility), the experiment can be controlled by assigning the treatments to randomly selected plots of land. This mitigates the effect of different soil composition on the yield.

Positive and negative control

Many experiments are designed to include a negative control and a positive control, which are the simplest forms of controls.[1]

Positive controls confirm that the procedure is competent in observing the effect (therefore minimizing false negatives). Negative controls confirm that the procedure is not observing an unrelated effect (therefore minimizing false positives). A positive control is a procedure that is very similar to the actual experimental test, but which is known from previous experience to give a result that is hypothesized to occur in the treatment group (positive result). A negative control is known to give a negative result. The positive control confirms that the basic conditions of the experiment were able to produce a positive result, even if none of the actual experimental samples produce a positive result. The negative control demonstrates the base-line result obtained when a test does not produce a measurable positive result; often the value of the negative control is treated as a "background" value to be subtracted from the test sample results, or be used as the "100%" value against which the test sample results are weighed.

For example, in an enzyme assay to measure the amount of an enzyme in a set of extracts, a positive control would be an assay where you add some of the purified enzyme, and a negative control would be where you do not add any extract. The positive control should give a large amount of enzyme activity, while the negative control should give very low to no activity.

If both the treatment group and the negative control produce the result, it can be inferred that another variable acted on the experiment and the data is discarded. Similarly, if the positive control fails, we know there was something wrong with our procedure so we discard any results and begin again. If both controls behave correctly, we can confidently accept the results of the experiment as the effect of the desired variable.

Necessity of controls

Controls are needed to eliminate alternate explanations of experimental results. For example, suppose a researcher feeds an experimental artificial sweetener to sixty laboratory rats and observes that ten of them subsequently die. The underlying cause of death could be the sweetener itself or something unrelated. Perhaps the rats were simply not supplied with enough food or water; or the water was contaminated and undrinkable; or the rats were under some psychological or physiological stress, or any other number of variables that may interfere with the experimental design many of which may not be readily obvious. Eliminating each of these possible explanations individually would be time-consuming and difficult. Instead, the researcher can use an experimental control, separating the rats into two groups: one group that receives the sweetener and one that does not. The two groups are kept in otherwise identical conditions, and both groups are observed in the same ways. Now, any difference in morbidity between the two groups can be ascribed to the sweetener itself—and no other factor—with much greater confidence.

In other cases, an experimental control is used to prevent the effects of one variable from being drowned out by the known, greater effects of other variables. For example, suppose a program that gives out free books to children in subway stations wants to measure the effect of the program on standardized test scores. However, the researchers understand that many other factors probably have a much greater effect on standardized test scores than the free books: household income, for example, and the extent of parents' education. In scientific parlance, these are called confounding variables. In this case, the researchers can either use a control group or use statistical techniques to control for the other variables.

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