Index

Cognitive Biases

Survivorship Bias

A bias that occurs when conclusions are drawn from successful outcomes while ignoring non-survivors.

Survivorship bias distorts learning by overfocusing on success stories and underweighting failed paths.

Core question

Whose data is missing, and how would those failures change this conclusion?

Example

Studying only successful startups can make risky strategies seem safer than they are because failed companies are absent from the sample.

How to apply

  1. 1.Identify who or what is excluded from the dataset.
  2. 2.Estimate failure rates and distributions.
  3. 3.Compare survivor narratives with broader base rates.
  4. 4.Design plans resilient to average outcomes, not only winner outcomes.

Pitfalls

  • ·Copying tactics from outliers without context.
  • ·Confusing post-hoc storytelling with causal insight.
  • ·Underestimating the role of luck.

Questions

What is a classic survivorship bias example?

Only analyzing profitable funds that still exist can overstate expected returns because failed funds were closed and excluded.

How does survivorship bias affect hiring?

Teams may overvalue backgrounds common among current high performers while overlooking capable candidates from less visible paths.

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