Definition
Representativeness Heuristic: People estimate the probability that something belongs to a category by how similar it is to a “typical–or prototypical member of that category, while neglecting base rates and sample size, leading to systematic probability biases.[1]
Mechanism and evidence
Tversky & Kahneman (1974) demonstrated representativeness in “heuristics and biases” e.g. judging someone as likely to be an engineer because a description “fits–the stereotype, while ignoring the proportion of engineers in the population.[1]
Consumer decision patterns
“Looks like a premium brand” — assume high quality; one review that “looks like a hit” — treat as general conclusion; someone who “looks like an expert” — trust their recommendation. Ignoring base rates (defect rates, fake-review rates) leads to misjudgment.
Mitigation (Selection Logic)
Representativeness undermines systematic evaluation: base judgments on evidence and base rates, not “how much it looks like a good product.” In M5, check whether you over-weighted “typical impression–and underused statistical information.
- Ask “What is the base rate for this kind of thing” (e.g. defect rate, failure rate).
- Separate “case narrative–from “statistical evidence” don’t let one vivid story replace the distribution.
- For high-stakes decisions, use checklists and dimensions instead of “feels like.