What is selection bias?

• A. Systematic error from non-random sampling causing over- or under-representation
• B. Random error due to small sample size
• C. Measurement error due to instrument calibration
• D. Confounding due to lack of randomization

Answer: (A)

Selection bias is a systematic error that happens when the way participants are selected or who ends up in the study makes the sample unrepresentative of the population. Because certain groups are more likely to be included or excluded, the study’s findings can be distorted, with some outcomes appearing more common or less common than they truly are. For example, in nursing research, a postoperative pain study that only includes patients who return for follow-up may miss those who have ongoing pain and those who recover quickly, leading to an inaccurate picture of typical pain levels or recovery patterns. This non-random sampling distorts estimates and associations.

This differs from random error due to a small sample size (which is variability that tends to decrease with more participants), measurement error from instrument calibration (which affects the accuracy of data collection), and confounding from lack of randomization (where an outside factor influences both exposure and outcome).