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What Is Hasty Generalization?
A hasty generalization is a fallacious generalization that is usually false due to insufficient sample size. In all cases, hasty generalizations refer to conclusions drawn from insufficient information, or where a logical pathway is reversed. This error is known by other names including hasty induction, the fallacy of the lonely fact, the fallacy of insufficient statistics, the fallacy of insufficient sample, converse accident, or simply jumping to conclusions.
Within the scientific community, researchers are cautioned to approach the act of generalizing as prudently as possible. By its nature, inductive reasoning does require observers to make generalizations. But before such generalizations can be made, it is important to apply critical thinking and confirm that you aren’t subscribing to any logical fallacies. Any type of fallacy will diminish your argument and weaken the overall strength of your writing.
3 Examples of Hasty Generalization
Each of the following situations presents a potential fallacy of hasty generalization:
- Reliance on an overly small sample size: Let’s say you met a woman from Texas who loved the Dallas Mavericks basketball team. You could generalize and say that: “Texans love the Dallas Mavericks,” but this would be a hasty generalization. The lone woman you met may be an unrepresentative sample of all Texans. After all, the state also houses the Houston Rockets and San Antonio Spurs; surely many Texans support those teams over the Mavericks.
- Secundum quid fallacy: Another hasty generalization based on an overly small sample size is one in which you invert a logical progression to explain a data point, a fallacy known as secundum quid fallacy. Take our Dallas Mavericks fan. As a fan of the team, she logically watches NBA games. It is therefore correct to say that “people who watch Dallas Mavericks games watch NBA games.” But if you invert the statement and say “people who watch NBA games watch Dallas Mavericks games,” then you have made a secundum quid fallacy, a form of faulty generalization. Indeed, there are plenty of NBA fans in New York who might watch a lot of Knicks and Nets but never view a Mavericks game.
- Faulty inductive reasoning: Let’s say you arrive home to your apartment to find a couch cushion torn apart and your dog looking guilty in the corner. Inductive reasoning will lead you to conclude that your dog destroyed the cushion. Inductive generalization is less about drawing conclusions and more about establishing rules. Let’s say, though, that you observe the scene with a trashed cushion and a guilty-looking dog, and you declare: “Every time I leave the house, my dog will destroy a couch cushion.” Perhaps this would prove to be true. But it’s also possible that you would have made an error known as a hasty generalization.
How to Avoid Hasty Generalizations in Your Writing
Good writing must never be diminished by overgeneralization, nor must it be tarnished by similar logical fallacies including red herrings, straw man fallacy, ad hominem fallacy, slippery slope fallacy, an appeal to ignorance, incorrect causality (post hoc ergo propter hoc), appeal to the stone (argumentum ad lapidem), “whataboutism” (tu quoque), circular reasoning, begging the question, false dilemma fallacy, and all forms of informal fallacies. You can avoid these fallacies—and specifically the hasty generalization fallacy—by doing the following:
- Consider a larger sample size. If you’re going to generalize, make sure you’re drawing conclusions from a large sample of data.
- Offer counterexamples. Showing multiple sides of an argument increases the thoroughness of your writing.
- Use precise language. Write with careful, measured phrases when employing inductive reasoning, and avoid diluting your statement to the point of equivocation.
By keeping your writing free from hasty generalizations, you increase the chances that your work will hold up against the scrutiny of fact-checking and will, therefore, better represent the point you are trying to make.
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