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Scientific control
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Scientific controls are a vital part of the scientific method, since they can eliminate or minimise unintended influences such as researcher bias, environmental changes and biological variation. Controlled experiments are used to investigate the effect of a variable on a particular system. In a controlled one set of samples have been (or is believed to be) modified and the other set of samples are either expected to show no change (negative control) or expected to show a definite change (positive control). [1][2]
Contents
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* 1 Examples of controls
* 2 Positive and negative control
* 3 Necessity of controls
* 4 See also
* 5 References

[edit] 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. 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 bias and to isolate the effects of the drug.

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.

[edit] Positive and negative control

The simplest forms of controls are positive and negative controls.[3]

Positive controls confirm that the procedure is effective in observing the effect (therefore minimising false negatives). Negative controls confirm that the procedure is not observing a unrelated effect (therefore minimising 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 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.

[edit] 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.