What Is a Control?
A scientific control is an 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.
For example, during drug testing, scientists will try to control two groups to keep them as identical as possible, then allow one group to try the drug. Another example might be testing plant fertilizer by giving it to only half the plants in a garden: the plants that receive no fertilizer are the control group, because they establish the baseline level of growth that the fertilizer-treated plants will be compared against. Without a control group, the experiment cannot determine whether the fertilizer-treated plants grow more than they would have if untreated.
Ideally, all variables in an experiment will be controlled (accounted for by the control measurements) and none will be uncontrolled. In such an experiment, if all the controls work as expected, it is possible to conclude that the experiment is working as intended and that the results of the experiment are due to the effect of the variable being tested. That is, scientific controls allow an investigator to make a claim like "Two situations were identical until factor X occurred. Since factor X is the only difference between the two situations, the new outcome was caused by factor X. "
Controlled Experiments
Controlled experiments can be performed when it is difficult to exactly control all the conditions in an experiment. In this case, the experiment begins by creating two or more sample groups that are probabilistically equivalent, which means that measurements of traits should be similar among the groups and that the groups should respond in the same manner if given the same treatment. This equivalency is determined by statistical methods that take into account the amount of variation between individuals and the number of individuals in each group. In fields such as microbiology and chemistry, where there is very little variation between individuals and the group size is easily in the millions, these statistical methods are often bypassed and simply splitting a solution into equal parts is assumed to produce identical sample groups.
Types of Controls
The simplest types of control are negative and positive controls. These two controls, when both are successful, are usually sufficient to eliminate most potential confounding variables. This 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
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. We would say that the control group should show a negative or null effect.
If the treatment group and the negative control both produce a negative result, it can be inferred that the treatment had no effect. If the treatment group and the negative control both produce a positive result, it can be inferred that a confounding variable acted on the experiment, and the positive results are likely not due to the treatment.
Positive Controls
Positive controls are groups where a phenomenon is expected. That is, they ensure that there is an effect when there should be an effect. This is accomplished 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, 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.
For difficult or complicated experiments, the result from the positive control can also help in comparison to previous experimental results. For example, if the well-established disease test was determined to have the same effectiveness as found by previous experimenters, this indicates that the experiment is being performed in the same way that the previous experimenters did.
When possible, multiple positive controls may be used. For example, if there is more than one disease test that is known to be effective, more than one might be tested. Multiple positive controls also allow finer comparisons of the results (calibration or standardization) if the expected results from the positive controls have different sizes.
Controlled Experiments
An all-female crew of scientific experimenters began a five-day exercise on December 16, 1974. They conducted 11 selected experiments in materials science to determine their practical application for Spacelab missions and to identify integration and operational problems that might occur on actual missions. Air circulation, temperature, humidity and other factors were carefully controlled.