Hasty Generalization

Hasty generalization is one of the most common logical fallacies we encounter at work, study and home. Many racial prejudices we carry have their roots in hasty generalizations subsequently strengthened and perpetuated by a confirmation bias.


The Hasty Generalization fallacy is also known by many other names:  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

This fallacy is committed when a person draws a conclusion about a population based on a sample that is not large enough. It has the following form:

  Sample X, which is too small, is taken from population Y.
  Conclusion C is drawn about Population Y based on X 

- A handful of cricketers were caught in match-fixing, and you conclude that the entire team must be guilty of being involved in match fixing.
- You know a rottweiler dog that is gentle. You conclude that rottweiler dogs generally are gentle dogs

The way this fallacy operates is that a conclusion about a universe is drawn based on a small sample.
Sometimes we are the victims of this fallacy, and sometimes we are the perpetrators. That we feel compelled to draw conclusions may have something to do with the inherent nature of the brain that it is not very good at dealing with uncertainties.
Let us take a hypothetical conclusion one used to see a lot in our older movies. The opinion that is perpetuated in these movies is that the rich are snobbish, class conscious and look down on the not so wealthy. There are many in real life who hold the same belief.
If you happen to be the one who disbelieves it, how do you rebut the conclusion that the rich are snobbish or that you cannot generalize about all the rich being snobbish. It is hardly practical to go about taking a statistical survey of the rich (even if you could figure out the question or test for snobbish qualities, or qualify who is rich). Or lets take the earlier conclusion that 'all cricketers are involved in match-fixing', or that all politicians have self-interest at their core.

We instinctively know that these generalizations cannot be right, but rebutting these arguments logically is difficult, because you have anecdotal evidence thrown at you. Counter examples may not be available in the same abundance. But this does not make the generalization correct.

The next time you come across a hasty generalization, you do not need to argue the point. All you need to do it is point out that the conclusion is based on a small sample making it a 'hasty generalization' - a logical fallacy.
Students are very prone to arrive at hasty generalizations about many things in their place of study and amongst friends.

Quite often (not always) many students take the opinions of a few friends about which colleges are good, and come to a conclusion about good and bad colleges. Similarly careers are discussed amongst a few friends. and they have conclusions about good and bad careers.

We, as working professionals and businesses are not immune to making hasty generalizations.

When launching a new dealer scheme, a salesman makes conclusions about how good the scheme is based on the opinions of the first two or three dealers they meet. If three dealers tell them it's a bad scheme, the managers are delivered the verdict that the scheme is bad. The Salesman has arrived at a hasty generalization based on a small sample of dealers. When the universe of dealers in the region is large and the universe across all regions runs into thousands, it is not wise to accept any conclusion about the dealer universe based on a very small sample. Decisions based on such hasty generalizations can only be bad decisions.

If HR has a bad experience with two recruits from a company X, they are quite likely to brand all employees from Company X as risky recruits. And the reverse conclusion happens if the experience is good. Either way it would be a hasty generalization, to draw conclusions about all the employees of an organization based on a small sample.

Let's say the Marketing department of a business takes to social media for the first time. If the first two or three small budget test campaigns do not produce the desired response or results, the company may conclude that social media is an inappropriate marketing channel for its business. The fact is that two or three test campaigns are not adequate to arrive at a conclusion. Doing so would be a hasty generalization - especially if failure at these campaigns are taken as the principal reasons for deciding that these marketing channels are inappropriate for the business. There could be many reasons why the channels could be inappropriate, but the evidence from failure of three small budget test campaigns is not adequate to come to a conclusion about the relevance or usefulness of the channel.

Look around, and we will find many examples of hasty generalization in our daily interactions at work and in society. Many political speeches and television news are packed with hasty generalizations from beginning to end. And don't be surprised to see hasty generalizations in decision making at many levels of the organization.

On the flip side, it is rarely practical or desirable to wait and get sufficient statistical evidence to support each and every conclusion we arrive at in business or in our personal lives. We need to take decisions with insufficient evidence all the time. The difference is that, awareness of this fallacy makes us wary about attaching disproportionate credibility to conclusions based on small sample sizes. We are more likely to be open to correction when new evidence comes in. This awareness also keeps us alert to the possibility of evidence that supports the contrary of our initial conclusions.

At the societal level, awareness of this fallacy will make us wary when we listen to hasty generalizations about an entire community or a class of people. We are more likely to examine these conclusions minutely, and also more likely to keep an open mind. Political speeches and television news lose the power to shape our opinions with conclusions based on slim evidence or anecdotal evidence.

Hasty generalizations are especially insidious when it comes to conclusions about communities or any other collective set of people - such as all supporters of a political party, or people belonging to a particular caste, geography or profession. Once we make some hasty generalizations about these folks, our Confirmation bias kicks in and strengthens these prejudices till they become very deep convictions and we lose the capability to look at any evidence impartially. It is always a good idea to look at any generalization about people and communities with a lot of scepticism.

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