Hasty generalization

Informal fallacies → Generalization / composition / division

Hasty generalization is when you decide something is true for everyone (or always) based on just a few examples. A small or unrepresentative sample isn't enough to support a broad conclusion. The same idea appears as the cognitive bias of insensitivity to sample size: we tend to treat small samples as more reliable than they are. To avoid the fallacy, you need more data or a proper sample before generalising. Related: Insensitivity to sample size.

Examples

  • Two of my friends got sick after eating at the new café, so that café makes people sick.

  • The first three applicants were weak, so we'll get no good candidates.

  • I've had two bad dates with people from that app, so everyone on that app is awful.

  • Three countries that raised the minimum wage saw job losses, so raising the minimum wage always costs jobs.

  • The first two episodes were boring, so the whole series is bad.

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