Hasty Generalization Fallacy | Definition & Examples

A hasty generalization fallacy is a claim made on the basis of insufficient evidence. Instead of looking into examples and evidence that are much more in line with the typical or average situation, you draw a conclusion about a large population using a small, unrepresentative sample.

Due to this, we often form a judgment about a group of people or items based on too small of a sample, which can lead to wrong conclusions and misinformation.

Hasty generalization fallacy is also called overgeneralization fallacy, faulty generalization, and argument from small numbers.

What is a hasty generalization fallacy?

A hasty generalization fallacy occurs when people draw a conclusion from a sample that is too small or consists of too few cases.

When we try to understand and come up with a general rule for a situation or a problem, the examples we use should be typical of the situation at hand. If we only consider exceptional cases or just a few instances of a certain phenomenon, we commit a hasty generalization fallacy. In other words, we jump to conclusions.

In the previous example, you don’t even know whether the passengers you crossed paths with were even Germans. They could have been from any country in the world, and had they been Germans, it would have been unreasonable to characterize an entire population based on the behavior of a few passengers.

Because the conclusion is not logically justified by sufficient evidence, hasty generalization is a form of logical fallacy or reasoning error. More specifically, it is an informal fallacy: the problem lies in the content of the argument, not its structure (formal fallacy).

How does hasty generalization fallacy work

An argument based on a hasty generalization moves from particular statements to a general statement. However, inferring a conclusion about an entire class of things from inadequate knowledge about some of its members is a logical leap.

Hasty generalization usually follows this pattern:

1. We take a small sample from a population, the sample usually being our own experiences.
2. We draw a conclusion based on this small sample.
3. We extrapolate our conclusion to the population.

In other words, “if it’s true in this case, then it is true in all cases.”

Why does hasty generalization fallacy matter?

In statistics, hasty generalization fallacy is often the outcome of sampling bias (i.e., when one uses a sample that does not represent the entire population). This can be accidental or intentional, like in the case of misleading statistics.

Hasty generalizations based on the misuse of statistics make their way into advertisements, political debates, and the media, creating false narratives or serving as a marketing tactic.

Because we are inclined to draw conclusions from our experiences, hasty generalizations usually crop up in everyday conversations. This is often the case when we make absolute claims on the basis of our (narrow) experience or an isolated incident.

Due to this, hasty generalizations about individuals and the groups they belong to can lead to various forms of stereotyping, like outgroup bias. The problem is that our experience provides only a small sample size, which is insufficient to support most generalizations.

Hasty generalization fallacy examples

Because of the constant need for new, attention-grabbing content, the media often fall prey to hasty generalization fallacies.

Hasty generalization fallacies can be used intentionally as a persuasion technique.

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If you want to know more about fallacies, research bias, or AI tools, make sure to check out some of our other articles with explanations and examples.

Frequently asked questions about the hasty generalization fallacy

What is the opposite of the hasty generalization fallacy?

The opposite of the hasty generalization fallacy is called slothful induction fallacy or appeal to coincidence.

It is the tendency to deny a conclusion even though there is sufficient evidence that supports it. Slothful induction occurs due to our natural tendency to dismiss events or facts that do not align with our personal biases and expectations. For example, a researcher may try to explain away unexpected results by claiming it is just a coincidence.

How can you avoid a hasty generalization fallacy?

To avoid a hasty generalization fallacy we need to ensure that the conclusions drawn are well-supported by the appropriate evidence. More specifically:

• In statistics, if we want to draw inferences about an entire population, we need to make sure that the sample is random and representative of the population. We can achieve that by using a probability sampling method, like simple random sampling or stratified sampling.
• In academic writing, use precise language and measured phases. Try to avoid making absolute claims, cite specific instances and examples without applying the findings to a larger group.
• As readers, we need to ask ourselves “does the writer demonstrate sufficient knowledge of the situation or phenomenon that would allow them to make a generalization?”

What is the difference between the hasty generalization fallacy and anecdotal evidence fallacy?

The hasty generalization fallacy and the anecdotal evidence fallacy are similar in that they both result in conclusions drawn from insufficient evidence. However, there is a difference between the two:

• The hasty generalization fallacy involves genuinely considering an example or case (i.e., the evidence comes first and then an incorrect conclusion is drawn from this).
• The anecdotal evidence fallacy (also known as “cherry-picking”) is knowing in advance what conclusion we want to support, and then selecting the story (or a few stories) that support it. By overemphasizing anecdotal evidence that fits well with the point we are trying to make, we overlook evidence that would undermine our argument.