Glossary
Survivorship bias
A distortion that occurs when analysis focuses only on entities that "survived" a filter, ignoring those that failed or dropped out.
Survivorship bias occurs when a dataset only contains the individuals, companies, or accounts that made it through some filter, while everything that failed or dropped out along the way is missing entirely, and conclusions are then drawn as if the full picture were visible. The classic illustration is studying only currently successful companies to find "what makes companies succeed," when the failed companies that shared the same traits are invisible to the analysis.
It is a specific form of selection bias, caused not by a flawed sampling method but by the underlying process itself removing certain cases before analysis ever begins, churned users vanish from an "active users" table, failed products get discontinued and drop out of a sales database, and closed funds disappear from a returns dataset.
Survivorship bias matters in analytics whenever churn rate or attrition is part of the picture: analyzing only currently retained customers to learn what a "successful" customer looks like will systematically miss the traits that predict who leaves. A proper cohort analysis deliberately tracks a full starting group, including those who later drop out, specifically to avoid this trap. The pitfall to watch for is any "successful cases only" dataset presented as if it represented the full population that started out.
Last reviewed September 22, 2026