Hasty Generalization

Hasty generalization comes in different shades and is easily one of the most common fallacies you could come across any day. In the study of logical fallacies, hasty generalization is a fallacy that occurs when an individual reaches a general conclusion hastily, based on evidence drawn from a very small sample size. In plain words; hasty generalization simply means jumping into conclusion based on insufficient evidence. The instance or sample used to arrive at such quick conclusions are insufficient and unrepresentative of the larger population. 

The fallacy is so easily committed that we could say everybody commits it every day – but then, we’d be committing a hasty generalization too! Nevertheless, it is quite factual to state that lots of generalizations that are assumed to be true are hasty and often bracket a large population that doesn’t fall into the lot. This is especially the case with stereotypes. For example, “Blondes are dumb” is one of those statements which some people hold to be true, even if the projections on the media are usually intended for comic relief. 

Often times, when people make hasty generalizations, it feels so casual they tend to believe their conclusions to be hard facts. What they fail to realize is that (1) they are drawing their conclusions from a small sample size and that (2) they probably failed to eliminate alternatives; that is, they failed to consider other instances/factors that are relevant to the prevailing situation before drawing their conclusions.

Example:

(While on a chat)

Mickey: I just arrived Kazakhstan, and I can tell you for a fact that Kazakhstanis are rude people! VERY RUDE PEOPLE!

Suzan: oh yeah? What happened, babe? What did they do?

Mickey: The driver of the first airport taxi I tried to hop on was very rude. I wasn’t going to have any of that, so I had to wait and look for another taxi. Wasted my time! 

Suzan: So sorry babe, what are you going to do now? 

Mickey: Oh, I am on my way to the hotel now, got another driver who’s quite nice. I am just trying to take deep breaths and relax cos I was so furious…

From the scenario above, Mickey made a hasty generalization by saying that Kazakhstanis are rude people based on ONE experience with an airport taxi driver. And that is a really small sample size. Also, how about the driver he eventually went along with; was the driver also rude? No. Mickey stated that he was nice, but then, he also said that Kazakhstanis are rude people. See how hasty his generalization was?

Considering Mickey’s conversation with Suzan, you should take a look at the many other names which the fallacy of hasty generalization goes by and determine how his conclusion is faulty based on each name.

Thus:

  1. Hasty induction 
  2. Statistics of small numbers 
  3. Lonely fact fallacy 
  4. Argument from small numbers 
  5. Insufficient statistics 
  6. Faulty generalization 
  7. Unrepresentative sample 
  8. Insufficient sample 
  9. Overgeneralization

While it is important to try as much as possible to avoid making hasty generalizations, it doesn’t mean that by default, all generalizations are bad or wrong. There are exceptions where even though a conclusion was reached based on a small sample size or seemingly insufficient evidence, the fallacy of hasty generalization is not committed. For example:

“The soup is well seasoned, I tasted a spoonful.”

“I don’t like Guinness stout, I have had four bottles of it on different occasions, and they are too bitter for my liking.”

In the examples above, unlike other examples given earlier, the population sizes are homogenous. By tasting a spoonful from a pot of soup, you can determine what other portions of the soup would taste like. The same goes for the stout; the brand is manufactured such that the taste is consistent in all bottles, so by tasting one, you can determine how all others would taste, even if it’s a small sample size. 

For a more expansive insight on the fallacy of hasty generalization, let’s review an article that seems to try hard not to appear like it is jumping into a faulty generalization. 

Article Review: Why All Cops Are Bad

In this article, the author vehemently states that all cops are bad, irrespective of how you’d like to perceive or address cops. The author goes on to give reasons why all cops are bad and why there are no exceptions – hence, there is no good cop. Reasons highlighted to drive home that all cops are bad stem from sociopolitical sentiments surrounding supposed institutional racism, with Black Lives Matter as the focal point. 

Through the length of the article, the author brings up and trashes some excuses that could be used to dismantle the hasty conclusion (All Cops Are Bad), such as the common analogy of a few bad apples among good apples. The author also tries to weaken the argument of any and all persons that argues contrary to their point, saying they are:-

  1. Comparing the actions of individual cops
  2. Not researched 
  3. Definitely not black 

Let’s take a look at the hasty generalizations made in the article. 

Overgeneralization flaws:

“Why all cops are bad.”

“To side with the police and argue that there are some good apples, you must have personal relationships in mind. I get it; your friend is a good cop and it’s hard to label someone “bad” when their actions are relatively not as severe as other officers.”

“…people who don’t agree with ACAB are doing so because of one or more of these three things: One, they compare the actions of individual cops…Two, they are not researched…Three, they are most definitely not Black.”

Insufficient statistics flaws: 

“Despite the overwhelming evidence of all cops being bad…”

Explanation:

From the excerpts highlighted under “overgeneralization flaws,” you may notice a pattern. The author states as facts, conclusions that were obviously drawn from assumptions which the author had made in order to bolster their points. 

First off, to say that all cops are bad could be tantamount to saying that all humans are bad. Understandably, the author tried to knock out that narrative by stating that;

“…it’s not about the person themselves, it’s about the deafening silence they project during duty and the actions they don’t take to correct injustice. It doesn’t matter if an officer is a good person serving at church on Sundays, because they still serve to enact racism.”

Depending on how invested you are in the author’s cause, you might begin to think that the author’s conclusion, even if hasty, is not wrong. Hence, it can’t be tagged as a hasty generalization. The article implies that the whole institution is bad; therefore, anyone that joins the institution to become a cop automatically becomes bad. Unfortunately for the author, that’s an overgeneralization of the institution and the cops therein. 

For the second excerpt: there are chances that someone can say that there are some good cops without having any personal relationship with any cop. 

On the third excerpt: you can decide there are good cops based on your assessment of a whole police unit. There are as many researches to show that cops are good – good for society. Should one ignore those while doing their research, or is it that researches that point towards the direction that opposes ACAB doesn’t count? Surely, there are blacks that think there are good cops as much as there are bad cops, so the conclusion that anyone that opposes the author’s argument is definitely not black is a highly flawed generalization. 

On perusing the article to fish out indicators of the “overwhelming evidence of all cops being bad…” the shreds of evidence brought forward were based on the issue of institutionalized racism. While racism is an important issue that needs urgent address, it doesn’t cover numerous other activities that cops engage in while discharging their duties. Maybe, if every section of the institution – both the faults and the good – are fully considered, we might eventually come to the conclusion that all cops are bad, or all cops are good, or that some cops are good while the others are bad. We can’t tell. But one thing is certain; the author used insufficient statistics to reach a hasty generalization. Such statistics as “Police officers are responsible for 90 percent of Black killings in the U.S.,” which was later recanted by the author. 

Fixing The Flaws

The author could have used alternative titles like “Why most cops are bad” or “Why some cops are bad.” Those would have made a less fallacious title, but well, the author already made it obvious that there is no in-between. 

Also, the article could have been structured to be more of an opinion piece by using an alternative title such as, “Why I think all cops are bad.” With such a title, opposing opinions can be attacked without making lots of hasty generalizations along the line. 

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