── ── Cognitive bias
Survivorship Bias
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.
How it works
Step 1 — State the claim: What is being concluded, from what sample, from what source?
Step 2 — Identify the survival filter: What process produced this sample? What was the population before the filter? What fraction was removed? What did the filter select for/against?
Step 3 — Construct the non-survivor hypothesis: What did non-survivors likely have? Did they share the trait attributed to success? Would the claim hold if we could see them?
When to use it
- user says 'look at what winners/billionaires/champions did,' investment returns or fund performance are being cited, a strategy is justified by pointing to companies that succeeded, historical data is treated as representative of all cases, or someone says 'this works because X did it
When not to use it
When the decision is routine and reversible, applying a formal method costs more than it returns.
Worked example
Abraham Wald and the Statistical Research Group, 1943
The canonical demonstration of survivorship bias is Abraham Wald's wartime work at the Statistical Research Group (SRG) at Columbia University, 1942-1945. The SRG was the secret mathematical-statistics arm of the wartime US government, comparable in caliber to the Manhattan Project for statistics: it included Wald, Allen Wallis, Milton Friedman, Frederick Mosteller, and Jacob Wolfowitz, among others.
Install this skill (free, MIT)
npx skills add deciqAI/knowledge-skills