Define bias and confounding; provide an example of each in public health research.

• A. Bias is random error; confounding is random error from sampling.
• B. Bias occurs when a study systematically over- or underestimates; confounding is when a third variable distorts an association.
• C. Bias is a mixing of effects by an extraneous variable; confounding is selection bias due to nonrandomization.
• D. Bias is systematic error in study design or conduct; confounding is when the observed association is distorted by a competing exposure related to both exposure and outcome.

Answer: (D)

Bias and confounding are about factors that can distort study results and mislead conclusions.

Bias is a systematic error that comes from the way a study is designed, conducted, or analyzed, causing the effect estimate to consistently skew in one direction. For example, if participants are selected in a way that makes the sample unrepresentative, or if information is collected differently for groups (such as recall bias in a case–control study where cases remember past exposures more or less accurately than controls), the measured association can be biased.

Confounding happens when a third variable is related to both the exposure and the outcome and thus distorts the observed relationship between them. A classic public health example is age or smoking status acting as a confounder in studies of an exposure and heart disease: if older people are more likely to smoke and also more likely to have heart disease, the association between smoking and heart disease may be partly due to age unless you adjust for it.

The statement that reflects these ideas states that bias involves systematic error in how the study is designed or conducted, and confounding occurs when the observed association is distorted by a competing exposure related to both exposure and outcome.