Answer (a) — Incorrect: Confounding is a problem in all observational study designs. Remember, confounding is a "mixing of effects" between an exposure, outcome, and a third variable. Confounding results from the fact that risk factors are generally not evenly distributed between comparison populations (i.e., exposed and unexposed groups) in observational studies. In large experimental studies, randomization usually produces comparison populations that have nearly the same distribution of characteristics, thus eliminating (or minimizing) confounding.

Answer (b) — Correct: Confounding is a problem for all observational study designs. Because epidemiology research concerns human populations, we must always consider that certain characteristics (e.g., age, sex, education, income, etc.) may be unevenly distributed in our study populations.

Answer (c) — Incorrect: Confounding can be just as large in a cohort study as it is in a case-control study. Regardless of design, it is important to consider potential confounders in your work, both in the design and analysis stages of your study.