Steelman
Hasty generalization
Also called jumping to conclusions, overgeneralization, small-sample fallacy, unrepresentative sample.
Drawing a broad conclusion from too few, or unrepresentative, cases.
What it is
Generalizing about a whole group from a sample that is too small, or not representative enough, to support a conclusion that broad.
The pattern
A few observed members of group G have property P. Therefore all (or most) of G have P.
An example
“My first two taxi drivers in Lisbon were rude, so Lisbon’s taxi drivers are rude.”
Two rides are far too few to say anything about thousands of drivers.
Why it fails
A small or skewed sample can easily differ from the whole population, by chance or because of how it was chosen. The broader and more confident the conclusion (“all”, “always”), the more evidence it needs. A bigger sample doesn’t cure bias: a huge sample gathered the wrong way is still unrepresentative.
When it’s not a fallacy
Most lists skip this part. Calling a fair point a fallacy is a mistake too.
It can be fair when
- The population is uniform in the relevant respect. One careful measurement of an electron’s charge tells you about all electrons.
- The conclusion is suitably modest: “some”, “in my experience”, “this suggests”.
- A single counterexample is used to refute an “all” claim. One black swan does disprove “all swans are white”.
Try one
A question from the app. The wrong answers are its usual look-alikes.
Pat, on a music forum: “Streaming has been terrible for musicians, and now there’s proof. Kim ran a poll in Broke Musicians United, the group for artists who can’t cover rent from music. 1,200 answered, and 90% said streaming made their lives harder.”
What’s the flaw?
The poll only asked artists who already can’t live off music, the people most likely to blame streaming. A big poll of a lopsided group can’t speak for all musicians.
A fair reply
“That’s a group built around struggling. What do musicians outside it say?”
Often confused with
- Anecdotal evidence
Anecdotal evidence is a kind of hasty generalization that rests on a personal story, often while ignoring better data.
- Accident
Accident runs the other way: it applies a sound general rule to an exceptional case the rule wasn’t meant for.
Where the name comes from
Hasty means too quick, and a generalization is a claim about a whole group. So the name says it: a claim about everyone, made before enough cases are in. Jumping to conclusions leaps over the evidence, and overgeneralization stretches a claim past the cases behind it. Small-sample and unrepresentative sample name the two usual causes: too few cases, or cases unlike the whole group.
Sources
- Fallacies, Bradley Dowden, Internet Encyclopedia of Philosophy
In the app
Practice spotting it on arguments that sound real.
Steelman has more examples of hasty generalization, how to answer it in a real conversation, and practice rounds that mix it with its look-alikes, so you learn the difference, not just the name. Coming soon to iPhone and Android.
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