Short answer: scientists use control groups to compare what happens with an intervention against what happens without it, or against a standard alternative. Without a control group, it is much harder to know whether the intervention caused the result.
Imagine testing a new study technique. Students who use it improve their marks. That sounds promising, but it is not enough. Perhaps they improved because the next test was easier. Perhaps they studied more because they knew they were being observed. Perhaps they were already stronger students. A control group helps separate the effect of the technique from other explanations.
What control groups do
A control group gives researchers a baseline. In medicine, one group may receive a new treatment while another receives a placebo or existing treatment. In education, one group may use a new curriculum while another continues with the usual curriculum. The comparison is what makes the result interpretable.
Good control groups should be as similar as possible to the treatment group except for the thing being tested. Random assignment helps because it spreads known and unknown differences across groups. Blinding can help prevent expectations from shaping behaviour or measurement.
Why they matter
Many changes happen naturally. Symptoms improve. Skills develop. Markets shift. People adapt. If researchers only measure before and after an intervention, they may mistake ordinary change for a treatment effect. Control groups reduce that risk.
Control groups are not always possible or ethical. Researchers cannot randomly expose people to serious harm just to create a clean comparison. In those cases, scientists use observational methods, natural experiments, matching, modelling, or other designs. But whenever a controlled comparison is possible, it is one of the strongest tools for testing cause and effect.