Hasty generalization is an informal fallacy of faulty generalization by reaching an inductive generalization based on insufficient evidence鈥 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").
[edit] Examples
Hasty generalization usually shows this pattern
X is true for A.
X is true for B.
X is true for C.
X is true for D.
Therefore, X is true for E, F, G, etc.
- A person travels through a town for the first time. He sees 10 people, all of them children. The person then concludes that there are no adult residents in the town.
- A person is looking at a number line. 3 is a prime number, 5 is a prime number, and 7 is a prime number. 9 is probably an anomaly, so it is ignored. 11 is a prime number, and 13 is a prime number. Therefore, the person says, all odd numbers are prime.
[edit] Alternative names
The fallacy is also known as:
- Fallacy of insufficient statistics
- 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]
[edit] See also
[edit] References
[edit] External links