Survivorship Bias

Survivorship bias (or survivor bias) is a logical error where one focuses on the people or things that “survived” a process and inadvertently overlooks those that did not because of their lack of visibility. This can lead to false conclusions in various fields, including finance, research, and history.

Core Details

• Mechanism: Concentrating on entities that passed a selection process while overlooking those that did not, leading to incomplete data.
• Impact:
  • Over-optimism: Multiple failures are overlooked (e.g., successful fund managers are studied while failed ones are ignored).
  • False Causality: Believing that successes have a special property rather than being a result of coincidence.
• Key Example (WWII Aircraft):
  • Statistician Abraham Wald analysed damage to returning planes.
  • Instead of reinforcing the areas with holes, he recommended reinforcing the areas without holes, as those were the spots where hits were fatal (preventing the plane from returning).

Practical Examples / Applications

Software Engineering & Business

• Startup Success: Studying only unicorns (like Facebook or Google) ignores the thousands of failed startups that did the exact same things but failed due to timing or luck.
• Feature Usage: Analysing only active users’ feedback might lead to ignoring why non-users or churned users left the platform.

Finance

• Mutual Funds: Performance indices often exclude failed funds, skewing the average returns higher.

Related Concepts

• Confirmation Bias (Mental Model)
• Selection Bias (Statistical Bias)

Citations

[1] Wikipedia: Survivorship Bias
[2] Wald, Abraham. (1943). A Method of Estimating Plane Vulnerability Based on Damage of Survivors.