Survivorship Bias

Overview

Survivorship bias is drawing conclusions from a sample pre-filtered by survival — treating survivor traits as the cause of survival when non-survivors (absent from data by definition) may have had identical traits and still failed.

Canon: Wald (1943) reversed the Navy's bomber-armor recommendation — returning planes showed damage where hits were survivable; the missing planes (shot down) were hit where returning planes showed no damage. Armor the gaps, not the hits.

Composes with bayesian-reasoning (prior = population, not survivors), critical-thinking (what would non-survivors say?), first-principles (population is bedrock), and abductive-reasoning ("winners have trait Y" is one hypothesis; randomness is another).

When to Use

• Someone draws lessons from "what successful X did"
• Investment returns / fund performance / backtested strategies are cited
• A business strategy is justified by pointing to companies that used it
• Medical / treatment success rates are reported without dropout data
• Career advice comes from what top performers did
• Odds of building an AI startup are inferred from the visible AI winners (funded unicorns, "wrapper" success stories) amid the AI-bubble / AI-capex debate