Controls are needed to eliminate alternate explanations of experimental results. For example, suppose a researcher feeds an experimental artificial sweetener to thirty laboratory rats and observes that eight of them subsequently die of dehydration. 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. Variables such as independent and dependent.

You want to know the effect of Miracle Gro on plants. Your control group gets no Miracle Gro. Your test group gets the Miracle Gro.

You always need a control group to rule out other effects (maybe the city added something to your water that you don’t know about that makes plants grow)