What is selection bias and how can it be minimized in study design?

• A. Systematic differences between those selected and those not selected.
• B. A random error due to data collection mistakes.
• C. Selection bias is the systematic difference between those selected and those not selected; minimization via random sampling, proper inclusion criteria, and ensuring follow-up.
• D. Bias arising from reporting only severe cases.

Answer: (C)

Selection bias happens when the participants included in a study differ systematically from those who are not included, so the groups you compare aren’t truly comparable. This can distort findings because the observed associations may reflect how people were chosen rather than true relationships in the population.

Minimizing it starts with designing the study to be representative: use random sampling so everyone in the population has a known, nonzero chance of selection; apply clear, appropriate inclusion and exclusion criteria to keep the sample aligned with the target population; and maximize follow-up or limit loss to follow-up so dropouts don’t differ in meaningful ways from those who stay. These steps help ensure that differences between groups are due to the factors being studied, not who happened to be included.

Other choices describe different issues: a random error from data collection mistakes isn’t about who is selected, and reporting only severe cases relates to ascertainment or reporting bias rather than the selection process itself.