Scientific control

Scientific control refers to a concept that allows for comparison as a part of the scientific method. It is often used in discussion of natural experiments. For instance, during drug testing, scientists will try to control two groups to keep them as identical and normal as possible, then allow one group to try the drug. This allows science to isolate the effects of the drug.

Scientific control needs not be experimental, and experimentation can sometimes be impossible (as in astronomy). The important thing is to try and control variables and attributes in the data so that the conclusions drawn are valid. Controls are used because it can be difficult to avoid confounding variables.

Controlled experiments

There are many forms of controlled or designed experiments. A relatively simple one separates research subjects or specimen into two groups: an experimental group and a control group. The control group is practically identical to the experimental group, although the experimental group is changed according to some key variable of interest, while the control group remains constant during the experiment. Each field develops their own specific, important controls.

Many of the ideas behind scientific control can be illustrated by the field of medicine. In drug testing, it is important to carefully verify that the supposed effects of the drug are produced only by the drug itself. Physicians achieve such control in many ways. They often use Blinded experiments (particularly double-blind studies), where some of the experimenters are ignorant of certain details in order to avoid unconsciously affecting the experiment's results. 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. Placebos have become especially crucial in light of research demonstrating the power of the mind (e.g. the control group will be given a sugar pill, to make sure that the effects of the experiment are not from the actions and expectations that come with taking any pill). The group receiving the placebo would be the control group, while the group receiving the actual drug would be the treatment group. Again, in a double-blind experiment, neither the patients nor the doctor know which group receives the real drug until the end; this serves both to curb bias and to isolate the effects of the drug.

In experiments involving a surgical procedure, a sham operated group is used to ensure that the data reflect the effects of the experiment itself, and are not a consequence of the surgery.

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.

Types of variable

Many experiments are designed to include a negative control and a positive control, which are the simplest forms of controls.^[1]

Negative

Negative controls are groups where the theory expects no phenomenon. This is similar to the idea of falsifiability: one way to test a theory is to make sure there is no effect when there should be no effect. To continue with the example of drug testing, a negative control is simply a group that has not been administered the drug. We would say that the control group should show a negative or null effect.

If both the treatment group and the negative control produce the result, it can be inferred that a confounding variable acted on the experiment.

Positive

Positive controls are where a phenomenon is expected, but this situation can be used as the "normal" to test another phenomenon anyway. A control group for drug testing becomes a 'positive' control if they agree to a sinus spray that provokes a flu. This new, 'positive control' flu group could then be used in the standard way: divided into two groups, where one is given a treatment and the other is given a placebo.

Positive controls can also be used to assess test validity. If we want to assess a new test's ability to detect the flu, then we can use positive controls (which often involve another different, and more established test) to make sure that the new test works. That is, the well-established experiment delivers the 'positive result' we expect, and can then be used for other experiments.

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 containing some of the purified enzyme, and a negative control would contain no extract. The positive control should give a large amount of enzyme activity, while the negative control should give very low to no activity. If the positive control does not produce the expected result, there may be something wrong with the experiment procedure and the experiment is repeated. If both controls behave correctly, the results of the experiment are often concluded to be the effect of the desired variable.

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. Other variables, many of which may not be readily obvious, may interfere with the experimental design. For instance, 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, etc. 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.

References

^ Johnson PD, Besselsen DG (2002). "Practical aspects of experimental design in animal research" (PDF). ILAR J. 43 (4): 202–6. PMID 12391395.

[pmid12391395-1] Johnson PD, Besselsen DG (2002). "Practical aspects of experimental design in animal research" (PDF). ILAR J. 43 (4): 202–6. PMID 12391395.

[1]