12.7:
Controls in Experiments
Controls in an experiment are elements that are held constant and not affected by independent variables. Controls are essential for unbiased and accurate measurement of the dependent variables in response to the treatment.
For example, patients reporting in a hospital with high-grade fever, breathing difficulty, cough, cold, and severe body pain are suspected of COVID infection. But it is also possible that other respiratory infection causes the same symptoms. So, the doctor recommends a COVID test.
The patient's nasal swabs are collected, and the COVID test is performed. In addition, a control sample is maintained that does not have COVID viral RNA. This type of control is also called negative control. It helps to prevent false positive reports in patients' samples.
A positive control is another commonly used type of control in an experiment. Unlike the negative control, the positive control contains an actual sample – the viral RNA. This helps to match the presence of viral RNA in the test samples, and it validates the procedure and accuracy of the test.
12.7:
Controls in Experiments
When conducting an experiment, it is crucial to have control to reduce bias and accurately measure the dependent variables. It also marks the results more reliable. Controls are elements in an experiment that have the same characteristics as the treatment groups but are not affected by the independent variable. By sorting these data into control and experimental conditions, the relationship between the dependent and independent variables can be drawn. A randomized experiment always includes a control group that receives an inactive treatment but is otherwise managed exactly as the other groups. The control group helps researchers balance the effects of being in an experiment with the effects of the active treatments.
In clinical or diagnostic procedures, positive controls are included to validate the test results. The positive controls would show the expected result if the test had worked as expected. A negative control does not contain the main ingredient or treatment but includes everything else. For example, in a COVID RT-PCR test, a negative sample does not include the viral DNA. Experiments often use positive and negative controls to prevent or avoid false positives and false negative reports. In
This text is adapted from Openstax, Introductory Statistics, Section 1.4, Experimental Design and Ethics