Index

Cognitive Biases

Representativeness Heuristic

A mental shortcut where the probability of an event is estimated by how much it resembles a typical case, often ignoring relevant statistical information.

The representativeness heuristic causes people to assess likelihood based on how closely something matches a prototype, ignoring base rates and sample sizes.

Core question

Am I judging this probability by resemblance to a stereotype, or by actual data?

Example

An investor assumes a polished pitch from a Stanford dropout signals a future unicorn because it matches the archetype, ignoring that most startups with similar profiles still fail.

How to apply

  1. 1.Start with base rates before adjusting for how representative the case appears.
  2. 2.Ask how large and relevant the sample is before drawing conclusions.
  3. 3.Test whether your typicality judgment would survive formal probability analysis.

Pitfalls

  • ·Dismissing pattern recognition entirely — stereotypes sometimes carry useful signal.
  • ·Over-relying on base rates when strong case-specific evidence exists.
  • ·Treating representativeness as always wrong instead of as one input among many.

Questions

What is a classic representativeness heuristic example?

The Linda problem: people rate 'Linda is a bank teller and a feminist' as more probable than 'Linda is a bank teller,' because the description resembles a feminist prototype.

How does the representativeness heuristic affect business?

Teams may pattern-match candidates, strategies, and opportunities to successful archetypes without checking whether the underlying statistics support the comparison.

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