# Hasty generalization

"Over-extension" redirects here. For the error common in language-learning, see Errors in early word use.

Hasty generalization is an informal fallacy of faulty generalization by reaching an inductive generalization based on insufficient evidence—essentially making a hasty conclusion without considering all of the variables. In statistics, it may involve basing broad conclusions regarding the statistics of a survey from a small sample group that fails to sufficiently represent an entire population.[1] Its opposite fallacy is called slothful induction, or denying a reasonable conclusion of an inductive argument (e.g. "it was just a coincidence").

## Examples

Hasty generalization usually shows this pattern

1. X is true for A.
2. X is true for B.
3. X is true for C.
4. X is true for D.
5. Therefore, X is true for E, F, G, etc.

For example, if a person travels through a town for the first time and sees 10 people, all of them children, he may erroneously conclude that there are no adult residents in the town.

Or: A person is looking at a number line. The number 1 is a square number; 3 is a prime number, 5 is a prime number, and 7 is a prime number; 9 is a square number; 11 is a prime number, and 13 is a prime number. Therefore, the person says, all odd numbers are either prime or square. In reality, 15 is a counterexample.

## Alternative names

The fallacy is also known as:

• Illicit generalization
• Fallacy of insufficient sample
• Generalization from the particular
• Leaping to a conclusion
• Hasty induction
• Law of small numbers
• Unrepresentative sample
• Secundum quid

When referring to a generalization made from a single example it has been called the fallacy of the lonely fact[2] or the proof by example fallacy.[3]

When evidence is intentionally excluded to bias the result, it is sometimes termed the fallacy of exclusion and is a form of selection bias.[4]