Name the prototype you’re comparing against before deciding
Make the prototype you’re using as a reference explicit — hidden templates bias decisions without scrutiny.
Why it works
Much representativeness reasoning is invisible: the mind silently matches a candidate, product, or plan to a stored prototype and outputs an evaluation without surfacing the template used. By naming the prototype explicitly ("I’m comparing this business idea to the prototypical tech startup"), you make the comparison visible for scrutiny — you can then ask whether the prototype is appropriate, whether the match is real, and what the base rate of success is for that category.
How to do it
- Before making an evaluative judgment, ask: "What is the prototype I’m comparing this to?"
- Name it explicitly out loud or in writing.
- Ask: "Is this the right reference category for this decision?"
- Ask: "What is the base rate of the outcome I’m predicting for that category?"
Evidence
Category-based evaluation is a foundational concept in categorization research (Rosch, 1975) and is directly linked to representativeness heuristic application in judgment research. Making implicit comparisons explicit is a general metacognitive debiasing strategy with support from the broader decision-quality literature. (mechanistic)
Naming the prototype is a necessary but not sufficient step; the hard work is then correctly estimating the base rate for that category, which is a separate skill.
Common mistake
Naming the prototype but treating it as confirming the comparison rather than interrogating it — the point is to question whether the resemblance justifies the inference.
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More practices for The Representativeness Heuristic — Judging by Resemblance
- Look up base rates before forming a resemblance judgment
Before deciding "this looks like X," ask how common X actually is in the relevant population.
- Identify which features are actually diagnostic
Separate features that genuinely differentiate categories from ones that just complete the picture.
- Recognize that random sequences don’t "owe" balance
Random processes have no memory — a run of heads doesn’t make tails more likely.
- Treat small samples with explicit skepticism
A short sequence can look representative without being statistically reliable — adjust confidence for sample size.
- Audit person-judgments for prototype substitution
Check whether a judgment about a person is based on their actual behavior or on resemblance to a type.
- Expect extreme results to regress toward average
Unusually good or bad performance predicts more average performance next time — account for this before giving praise or blame.