What Is the Hasty Generalization Fallacy?

Hasty generalization, also known as “faulty generalization”, is a logical fallacy in which someone generalizes from a too-small sample size. The conclusion of the argument is made hastily without looking at more reliable statistics which would enable the arguer to make a more accurate judgment about the situation or issue.

This is also an example of jumping to conclusions, which is a term in psychology that refers to decisions or judgments made without having all the relevant facts, thus reaching an unwarranted conclusion.

In this article, we’ll explain in detail how this faulty line of reasoning works, as well as examine a variety of examples.

What Is Hasty Generalization?

The fallacy of hasty generalization occurs when someone makes a general statement based on an insufficient or nonrepresentative sample, rather than looking at a broader range of available data.

Put simply, one draws a conclusion about the whole or majority of the whole on the basis of too few examples.

As such, its typical logical form is:

1. Sample 1 is taken from population Y.
2. Sample 1, which is a very small portion of population Y, has quality X.
3. Therefore, population Y has quality X.

For example, if someone asserts that all people from country X must be bad drivers because the two people they’ve met from that country were awful at it, they are guilty of hasty generalization.

Biased Thinking

This type of argumentation is common and is often difficult to avoid due to our built-in biases.

For instance, the bias of group attribution error refers to our natural tendency to assume that the characteristics and preferences of one group member are similar to those of the other members of the same group. This is the source of many incorrect generalizations and stereotyping.

Examples

• “I just arrived in this country and the first 2 local people I met were so rude. Everyone in this country must be unfriendly!”
• “I asked five people in the street what is their favorite color, and four of them said blue. Therefore, 80% of the population prefer blue over any other color.”
• “My dad has smoked 2 packs of cigarettes every day for 20 years, and he doesn’t have any health problems. Smoking can’t be dangerous!”
• “Our exercise program helped several people to lose weight while building more muscle. Therefore, this program will be effective for everyone.”
• “Did you see that woman just run a red light? Women are awful drivers.”

The following example was given in An Introduction to Logic by H.W.B Joseph:

Take my son, Martyn. He’s been eating fish and chips his whole life, and he just had a cholesterol test, and his level is below the national average.
What better proof could there be than a frier’s son?

Related fallacies

Accident Fallacy

Accident fallacy is based on the assumption that a general rule applies to every situation when, in fact, there are exceptions. It occurs when someone applies such a rule to a case in which the rule is inapplicable.

A simple example would be: “taking a life is a crime and morally wrong; therefore, termite control is a crime and morally wrong”.

This, like the topic of this article, is a form of jumping to conclusions.

Slothful Induction

This one can be seen as the opposite of the faulty generalization: in slothful induction, the conclusion of an inductive argument is rejected despite strong evidence to the contrary.

One example: Brian has had 10 car accidents in the last twelve months, but he insists that it is just a coincidence and not his fault, even though the evidence overwhelmingly suggests otherwise.

Originally published at https://fallacyinlogic.com on March 22, 2020.