IBM Data Science Practice Test

In the context of A/B testing, the control group is indeed defined as the group that does not receive the treatment and is utilized for comparison against the treatment group, which is exposed to the new variable or change being tested. This distinction is critical because the primary objective of A/B testing is to determine the impact of a specific intervention or treatment by measuring the differences in outcomes between the control group and the treatment group. The control group serves as a baseline, allowing researchers to isolate the effects of the treatment by comparing the responses of those who experienced the intervention to those who did not. This comparison helps in assessing whether any observed effects are likely due to the treatment itself rather than other external factors or random fluctuations. By having a well-defined control group, the validity of the conclusions drawn from the A/B test is strengthened, making it a crucial component of experimental design in data science.