What it is
Generalizing from a sample too small or too narrow to support the conclusion.
In the wild
All three people I asked liked the new logo, so everyone likes it.
How to answer it
Ask how many cases were seen and how they were chosen.
Evidence and sampling
Hasty generalization
Also called Faulty generalization, insufficient sample
In depth
Origin and naming
The medieval logicians knew it as 'secundum quid' — the converse of the fallacy of accident, which over-applies a rule; hasty generalization over-builds one. Modern textbooks call it the fallacy of insufficient sample. Every cliché about 'all politicians' and 'all journalists' is its handiwork.
More places it shows up
After two bad rides with one app, a commuter declares the whole ride-share industry is a scam.
A manager interviews three candidates from the same university, finds them unprepared, and tells HR to stop shortlisting that school.
A tourist has one rude encounter in a new city and posts that 'people here are unwelcoming'.
A developer's first attempt with a new framework hits a bug, and the team chat concludes the framework is 'not production ready'.
How to spot it
- The conclusion arrives with 'all', 'every', 'always' — and the evidence fits in one hand.
- Ask 'how many have you actually seen?' and the answer is usually a number you can count on your fingers.
- The sample is self-selected: people the speaker happened to meet, posts the algorithm happened to surface.