Selectiebias

Selectiebias, also known as selection bias, is a systematic distortion in the statistical analysis that arises when the sample used in a study is not representative of the target population. This can lead to incorrect conclusions about prevalence, associations, or causal effects because certain groups are overrepresented or underrepresented relative to the population of interest.

Causes of selectiebias include nonrandom sampling, self-selection, nonresponse, and loss to follow-up. Using convenience or hospital-based samples, strict inclusion or exclusion criteria, or data collected through a limited channel (such as online surveys) can all create biases. In observational research, biases may also emerge when participants who drop out differ from those who remain, or when data sources systematically exclude relevant subgroups.

Consequences of selectiebias are reduced external validity and biased estimates. The results may not generalize beyond the studied sample, and associations observed in the data may not reflect true relationships in the broader population. This can mislead policy decisions, clinical recommendations, or scientific understanding.

Mitigation strategies focus on designing the study to favor representativeness and using analytical adjustments. Examples include random or stratified sampling, minimizing loss to follow-up, broad and inclusive recruitment, and weighting or imputation to correct for underrepresented groups. Sensitivity analyses can assess how robust results are to potential biases. In data science and machine learning, ensuring diverse and representative training data, monitoring for biased performance across subgroups, and applying bias correction techniques can help reduce selection bias in models.