What correctly describes selection bias and information bias in epidemiologic research?

• A. Selection bias arises when participants are not representative; information bias arises from misclassification or measurement error.
• B. Selection bias arises from misclassification; information bias from nonrandom dropouts.
• C. Both are types of sampling bias; selection bias is random; information bias is systematic.
• D. Selection bias occurs after data collection; information bias occurs during analysis.

Answer: (A)

Understanding bias types in epidemiology involves two main ideas: how the study sample is chosen and how the data are measured. Selection bias happens when the participants in a study are not representative of the target population because of how they were selected or who remains in the study, which can skew results. Information bias occurs when there are errors in measuring exposure or outcome, such as misclassification or measurement error, leading to distorted associations.

The best description says that selection bias arises from participants not being representative, while information bias comes from misclassification or measurement error. This captures the distinct sources: who is in the study versus how the data are collected.

Other options mix up these concepts: misclassification is a hallmark of information bias, not selection bias; nonrandom dropouts are a form of selection bias, not information bias; treating both as sampling biases or claiming one is random and the other systematic mischaracterizes their nature; and selection bias is a problem at the design/selection stage, whereas information bias concerns data collection, not analysis.