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A scientific control augments integrity in experiments by isolating variables as dictated by the scientific method in order to make a conclusion about such variables. In a controlled experiment, two virtually identical experiments are conducted. In one of them, the treatment, the factor being tested is applied. In the other, the control, the factor being tested is not applied. For example, in testing a drug, it is important to carefully verify that the supposed effects of the drug are produced only by the drug itself. Doctors 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 researchers' bias and to isolate the effects of the drug.

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 of dehydration and vomit. The underlying cause of death could be the sweetener itself or something unrelated. Perhaps the rats were simply not supplied with enough water; or the water was contaminated and undrinkable; or the rats were under some psychological or physiological stress that caused them not to drink enough; or a disease dehydrated them; or their cage was kept too hot. 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 doesn't. 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

• Controlling for a variable

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