TB: Survivorship Bias

The logical error of focusing on the entities that passed a selection process while overlooking those that did not. Because failures are invisible (they drop out of the sample), the surviving population appears more successful, robust, or talented than the full population actually is.

Survivorship bias distorts every field where failures disappear from view. Business advice from successful founders omits the majority who failed doing the same thing. The safest-looking investment strategies are often the ones that happened to work for a specific historical window. The cure is to actively seek out the missing data — ask "what happened to everyone who tried this and isn't here to tell us?" The visible data is a biased sample; the invisible data is often the more informative one.

• The Black Swan (Taleb): the "silent graveyard" — history is written by the winners; the cemetery of failed strategies contains more information than the hall of fame; Taleb's core critique of naive empiricism depends on understanding survivorship
• Fooled by Randomness (Taleb): the successful trader vs. the many failed traders following the same strategy; randomness produces a few spectacular winners from a large population; the winner believes in skill, the observer sees only the survivor

• Second-Order Thinking — the invisible failures are the second-order consequence of selection
• Inversion — "who tried this and failed?" is the survivorship-corrected version of the question