Name two common biases in epidemiology.

• A. Selection bias — systematic differences in how participants are selected; Information bias — misclassification of exposure or outcome
• B. Selection bias — random sampling error; Information bias — sampling bias
• C. Selection bias — follow-up loss; Information bias — misclassification due to measurement error
• D. Selection bias — non-response; Information bias — misclassification of exposure or outcome

Answer: (A)

Two common biases in epidemiology are selection bias and information bias. Selection bias happens when there are systematic differences in how participants are chosen for the study or how many are retained, so the group you study isn't representative of the target population. This can skew the observed association between exposure and outcome. Information bias arises when there is misclassification or measurement error in the exposure or the outcome, leading to incorrect data about who is exposed or who has the disease, which also distorts results. The described definitions match these two well-known biases: selection bias as systematic differences in selection, and information bias as misclassification of exposure or outcome.

Other options mix terms in ways that aren’t as standard. Random sampling error is random, not a systematic bias, so it isn’t best described as a bias. While follow-up loss or non-response can contribute to selection bias, the most general and commonly taught descriptions focus on systematic differences in selection and on misclassification due to measurement error.