Reading the evidence
Selection bias arises when inclusion in a sample depends on factors related to the outcome, making the observed group unrepresentative of the intended population.
Make it click
Surveying only people who choose to enter a specialist shop will not describe the whole town. The doorway has already filtered the responses.
Now put that into markets
Market datasets can select liquid, reported or successful events and omit difficult cases. The resulting rate may reflect admission rules.
Self-selection, missingness and post-outcome filtering are common forms. Weighting helps only when selection probabilities and relevant variables are understood.
Define target population, sampling frame and exclusion reasons. Compare included and excluded records using information available before outcomes.
A large selected sample remains biased, and statistical adjustment cannot recover unmeasured missing groups with certainty.
Takeaway
