A control in a scientific experiment is a standard of comparison. It is the part of the experiment where the factor being tested, known as the independent variable, is either absent or kept at a baseline level. By setting up this comparison, scientists can determine if the changes they observe in the experimental group are truly caused by the variable they introduced.
Why Controls Are Essential for Valid Results
Incorporating a control helps isolate the effect of the single factor under investigation. In a well-designed experiment, only one variable is intentionally changed between the experimental and control groups. The control group serves as the baseline, showing what happens when the independent variable is not applied. If a change is observed in the experimental group, but not in the control group, scientists can confidently attribute the change to the factor being tested. Without this comparison, any observed outcome could be due to the natural progression of the system, a flaw in the setup, or an unmeasured environmental factor, making the conclusions meaningless.
Control Group vs. Controlled Variables
The terms control group and controlled variables refer to two distinct components of experimental design. A control group is a comparison set of subjects or samples that is exposed to all the same conditions as the experimental group, except for the treatment being investigated. For example, in a drug trial, the control group receives a placebo, which is an inactive substance, while the experimental group receives the actual medication. Controlled variables, by contrast, are all the other factors that must be kept identical across both the control group and the experimental group. Maintaining these external factors at a constant level ensures that the only difference between the groups is the presence or absence of the fertilizer, thereby isolating the true effect of the independent variable.
The Two Main Types of Control Groups
Rigorous scientific studies often utilize two specialized types of controls.
Positive Control
A positive control is a group or sample where a known effect is expected to occur, essentially serving as a check that the entire experimental system is working correctly. For example, in a test for a certain pathogen, a positive control would involve testing a sample that is already known to contain the pathogen. If the test fails to detect it, the researcher knows the testing materials or procedure are flawed. The expected positive result confirms the methodology is sensitive enough to detect the phenomenon.
Negative Control
A negative control is a group or sample where no effect is expected to occur, which helps to rule out contamination or non-specific reactions. This group is treated exactly like the experimental sample but lacks the component that should produce the effect. A positive result in this sample would indicate contamination of the reagents or a false-positive reaction. Using both positive and negative controls allows scientists to confidently interpret their data.
Controls in Everyday Scientific Inquiry
The application of control groups is widely seen in various fields, underpinning the results used in daily life. Pharmaceutical research relies heavily on controlled experiments, where a portion of the participants in a clinical trial receives an inactive placebo instead of the actual drug being tested. This placebo group acts as the control, allowing researchers to measure the drug’s effect against the natural rate of recovery and the powerful psychological impact of receiving a treatment. Similarly, in agricultural science, controls are established by leaving specific plots of land untreated with a new pesticide or fertilizer. These untreated plots provide an accurate baseline measurement of crop yield or pest infestation under natural conditions.