Define selection bias and give an example in a community health study.

• A. Selection bias arises from random sampling.
• B. Selection bias occurs when measurement tools are inaccurate.
• C. Selection bias is a systematic difference in how participants are selected; example: healthier individuals more likely to enroll in a survey.
• D. Selection bias arises when confounding is controlled.

Answer: (C)

Selection bias happens when the way people are chosen for a study creates a systematic difference between those included and the overall population the study aims to describe. That means the sample isn’t truly representative, which can skew results. In a community health study, if healthier individuals are more likely to enroll, the enrolled group will differ from the broader population in health status. This can lead to underestimating how common a health problem is or to biased estimates of how risk factors relate to outcomes, because the sicker or more at-risk individuals are underrepresented.

The other ideas miss the core point: random sampling is a method used to reduce bias rather than cause it, so selection bias doesn’t arise from random sampling. Measurement tool inaccuracy produces information bias, not selection bias. And bias from confounding relates to unmeasured factors affecting associations, not to how participants are selected into the study.