Selection bias occurs when inclusion in the analyzed sample depends on variables related to the exposure and outcome, making the observed association differ from its target-population counterpart.
Survivorship bias occurs when analysis conditions on remaining observable or event-free long enough to enter a sample, thereby excluding earlier failures and overrepresenting longer survivors.
Collider bias arises when analysis conditions on a common effect of two variables, thereby creating a statistical association between its causes.
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Selection bias refers to a type of error that occurs when the sample taken for a study or analysis is not representative of the larger population from which it is drawn. This can lead to skewed or invalid conclusions because the characteristics of the sample may differ significantly from those of the overall population.