A scientific control is an experiment or observation designed to minimize the effects of variables other than the single independent variable. This increases the reliability of the results, often through a comparison between control measurements and the other measurements. Scientific controls are a part of the scientific method.
Controls eliminate alternate explanations of experimental results, especially experimental errors and experimenter bias.
The simplest types of control are negative and positive controls, and both are found in many different types of experiments. These two controls, when both are successful, are usually sufficient to eliminate most potential confounding variables: it means that the experiment produces a negative result when a negative result is expected, and a positive result when a positive result is expected.
Negative controls are groups where no phenomenon is expected. They ensure that there is no effect when there should be no effect. To continue with the example of drug testing, a negative control is a group that has not been administered the drug of interest. This group receives either no preparation at all or a shampreparation (that is, a placebo), either an excipient-only (also called vehicle-only) preparation or the proverbial "sugar pill." We would say that the control group should show a negative or null effect.
Positive controls are groups where a phenomenon is expected. That is, they ensure that there is an effect when there should be an effect, by using an experimental treatment that is already known to produce that effect (and then comparing this to the treatment that is being investigated in the experiment).
Positive controls are often used to assess test validity. For example, to assess a new test's ability to detect a disease (its sensitivity), then we can compare it against a different test that is already known to work. The well-established test is the positive control, since we already know that the answer to the question (whether the test works) is yes.
In randomization, the groups that receive different experimental treatments are determined randomly. While this does not ensure that there are no differences between the groups, it ensures that the differences are distributed equally, thus correcting for systematic errors.
For example, 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 variations in soil composition on the yield.
In blind experiments, at least some information is withheld from participants in the experiments (but not the experimenter). For example, to evaluate the success of a medical treatment, an outside expert might be asked to examine blood samples from each of the patients without knowing which patients received the treatment and which did not. If the expert's conclusions as to which samples represent the best outcome correlates with the patients who received the treatment, this allows the experimenter to have much higher confidence that the treatment is effective.
In double-blind experiments, at least some participants and some experimenters do not possess full information while the experiment is being carried out. Double-blind experiments are most often used inclinical trials of medical treatments, to verify that the supposed effects of the treatment are produced only by the treatment itself. Trials are typically randomized and double-blinded, with two (statistically) identical groups of patients being compared. The treatment group receives the treatment, and the control group receives a placebo such as a sugar pill. The placebo is the "first" blind, and controls for the patient expectations that come with taking a pill, which can have an effect on patient outcomes. The "second" blind, of the experimenters, controls for the effects on patient expectations due to unintentional differences in the experimenters' behavior. Since the experimenters do not know which patients are in which group, they cannot unconsciously influence the patients. After the experiment is over, they then "unblind" themselves and analyse the results.
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