Hasty generalizations arise when we illegitimately generalize from a nonrepresentative sample. They are the source of many stereotypes.


1) I've met three redheads and they were all mean, so all redheads are mean.

2) The car that just cut me off is from South Dakota, so all South Dakotans are jerks.

3) Everyone who responded to the survey said the exercise program helped them lose weight. Therefore, everyone who used the program lost weight.


The most common type of hasty generalization is generalizing from too small a sample size. For example, it is a hasty generalization to infer all redheads are mean after meeting only three redheads.

However, sometimes it is valid to generalize from a small sample size. For example, I can generalize stoves will burn my hand after experiencing only one stove burn my hand. This is logically permissible because one stove is usually representative of all stoves. You should carefully examine each situation to determine if it is representative.

Also, large sample sizes do not always prevent this fallacy. For example, I might fallaciously conclude that most Americans support the Democratic President because a poll of thousands of Democrats recorded a 70% approval rate. Clearly, this poll is not random and does not accurately represent all Americans since it is only polling Democrats. To avoid a hasty generalization, a large sample size is a good start, but it should also be random and representative.

How to avoid

Do not generalize from small and/or unrepresentative samples.


  1. Identify a few stereotypes that are probably based on hasty generalizations.
  2. Identify some ways to make sample sizes random and representative.


  1. Answers will vary: Italians love spaghetti. Texans wear boots. Philosophers are ugly. Just look at me…
  2. Answers will vary. Take a statistics course.