── ── 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.
Run Survivorship Bias on a real problem
Bring something you're actually deciding — free, in the browser.
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-skillsUseful? Star the repo — stars help other builders find it.
FAQ
What is the classic example of survivorship bias?
WWII statistician Abraham Wald's bomber analysis: the military wanted to armor where returning planes showed bullet holes. Wald pointed out those planes survived those hits — the armor belonged where the returners were unscathed, because planes hit there never came back.
How does survivorship bias mislead founders?
Startup advice is drawn almost entirely from companies that survived. Copying the habits of winners — dropping out, ignoring competitors, betting everything — ignores the far larger population that did the same things and died. The failures hold the real information, and they're missing from the sample.
How do I correct for survivorship bias?
Ask what sample the evidence comes from and who was filtered out before you saw it. Actively seek the non-survivors — churned customers, failed startups in your category, rejected applicants — because a conclusion drawn only from survivors tells you about survival, not about cause.
Related mental models
The voltage effect is John A. List's name for what happens to most promising ideas when they scale: they lose voltage — they fail, shrink, or…
Wu wei (无为) — Daoist principle (Daodejing, ~4th century BCE): act without forcing, not without acting.
Zero-sum means total value is fixed — one player's gain is another's exact loss.
We seek and weight evidence that confirms what we already believe. Hunt for what would prove you wrong.