What is base-rate neglect?

Ignoring how common or rare something is and judging by specific details instead — leading to errors like overestimating disease risk from an accurate-seeming positive test.

What’s an example of the base-rate fallacy?

A 99%-accurate test for a 1-in-10,000 disease: a positive result still means you probably don’t have it, because false positives vastly outnumber true cases.

How do you avoid base-rate neglect?

Anchor on the base rate first, then update with the specific evidence (Bayesian reasoning) rather than letting vivid details replace the statistics.

Base-rate neglect

Also known as the base-rate fallacy · Probability & judgement

Base-rate neglect is the tendency to ignore how common or rare something is (the base rate) and judge by specific, vivid details instead. It’s why a positive test for a rare disease feels alarming even when most positives are false alarms.

Why it happens

Specific, individuating information feels more relevant and concrete than dry statistics, so we anchor on the case in front of us and forget how common or rare the underlying category actually is.

Examples

Fearing a rare-disease diagnosis from a positive test, ignoring that the disease’s rarity makes most positives false.
Judging someone "probably a librarian" from a shy description, ignoring that there are far more salespeople.
Over-fearing dramatic-but-rare risks (terrorism) over common ones (heart disease).

How to counter it

Start from the base rate before weighing the specific evidence.
Ask: how common is this category in the first place?
Combine the base rate with the evidence (Bayesian thinking) — don’t let one replace the other.

The deeper point

It’s why intuition and statistics clash on medical tests: the mind hears "99% accurate" as "I’m 99% likely to have it," but the rarer the condition, the more the base rate dominates — and the more wrong that feeling is.

Frequently asked

What is base-rate neglect?: Ignoring how common or rare something is and judging by specific details instead — leading to errors like overestimating disease risk from an accurate-seeming positive test.
What’s an example of the base-rate fallacy?: A 99%-accurate test for a 1-in-10,000 disease: a positive result still means you probably don’t have it, because false positives vastly outnumber true cases.
How do you avoid base-rate neglect?: Anchor on the base rate first, then update with the specific evidence (Bayesian reasoning) rather than letting vivid details replace the statistics.

Availability heuristic The gambler’s fallacy Bayesian thinking The map is not the territory

Editorial synthesis © ReadGlobe 2026, drawing on Kahneman’s Thinking, Fast and Slow, the Tversky–Kahneman research program, and the primary cognitive-science literature. · Last reviewed 2026-05-29.