A scientific control group is an essential part of most research designs, allowing researchers to eliminate and isolate variables.
A researcher must only measure one variable at a time, and using a scientific control group gives reliable baseline data to compare their results with.
For example, a medical study will use two groups, giving one set of patients the real medicine and the other a placebo, in order to rule out the placebo effect. In this particular type of research, the experiment is double blind.
Neither the doctors nor the patients are aware of which pill they are receiving, curbing potential research bias.
For example, the placebo effect for medication is well documented, and the Hawthorne Effect is another influence where, if people know that they are the subjects of an experiment, they automatically change their behavior.
There are two main types of control, positive and negative, both providing researchers with ways of increasing the statistical validity of their data.
Positive scientific control groups are where the control group is expected to have a positive result, and allows the researcher to show that the set-up was capable of producing results.
Generally, a researcher will use a positive control procedure, which is similar to the actual design with a factor that is known to work.
For example, a researcher testing the effect of new antibiotics upon Petri dishes of bacteria, may use an established antibiotic that is known to work. If all of the samples fail, except that one, it is likely that the tested antibiotics are ineffective.
However, if the control fails too, there is something wrong with the design. Positive scientific control groups reduce the chances of false negatives.
Negative Scientific Control is the process of using the control group to make sure that no confounding variable has affected the results, or to factor in any likely sources of bias. It uses a sample that is not expected to work.
In the antibiotic example, the negative control group would be a Petri dish with no antibiotic, allowing the researcher to prove that the results are valid and that there are no confounding variables.
If all of the new medications worked, but the negative control group also showed inhibition of bacterial growth, then some other variable may have had an effect, invalidating the results.
A negative control can also be a way of setting a baseline.
A researcher testing the radioactivity levels of various samples with a Geiger counter would also sample the background level, allowing them to adjust the results accordingly.
Establishing strong scientific control groups is arguably a more important part of any scientific design than the actual samples.
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