Faulty generalization is a common fallacy \u2013 an error in an argument \u2013 in both academic and non-academic discourse. But what is faulty generalization? And how do you avoid it in your work?\u00a0Check out our guide to find out.\r\nWhat Is Faulty Generalization?\r\nFaulty generalization \u2013 sometimes known as hasty generalization or defective induction \u2013 involves drawing a conclusion for an entire population based on a limited sample. In other words, we make a faulty generalization when we jump to an unjustified conclusion. For instance:\r\nMy friend Bill is bald, so I assume nobody called Bill has hair.\r\nIn this case, our sample is Bald Bill. The population we\u2019re generalizing about is everyone called Bill. But this is obviously an unjustified leap!\r\nThere are many people called Bill, so assuming we can say anything about all of them based on a single hairless person is a hasty generalization.\r\nFaulty Generalization: Is the Sample Big Enough?\r\nNot all faulty generalizations are as obvious as the one above. For instance:\r\nOur study found that 80% of felines prefer Meow brand cat food.\r\nThis sounds impressive because 80% is a large percentage. But unless we know how many cats were tested, we can't generalize to other felines. If the study included 10,000 cats, we might have a decent sample size to draw conclusions. But if they only tested five cats, we couldn\u2019t say anything useful about all cats based on what 80% (i.e., four cats) did.\r\n\r\n[caption id="attachment_9869" align="aligncenter" width="450"] The other cat out of the five prefers granola.\r\n(Photo: Alexas_Fotos)[\/caption]\r\n\r\nThe key is that we should not jump to conclusions about large populations based on a limited sample size. And if we do not know how large the sample in a study was, we cannot know how reliable its conclusions are.\r\nFaulty Generalization: Is the Sample Representative?\r\nIn other cases, we need to look at whether the sample is representative of the wider population to which we are generalizing. For example, imagine we conducted another survey about house cats:\r\nIn a study of 50,000 domestic felines, we found that 91% regularly climb trees. This suggests that the instinct to climb trees is strong in all felines.\r\nA rate of 91% in a sample of 50,000 is impressive, so this could sound convincing. But are we sure domestic cats are representative of other felines? A house cat has some things in common with a lion, but it is also different in many ways. And generalizing from one to the other would ignore these differences. Lions, for example, rarely climb trees.\r\n\r\n[caption id="attachment_9867" align="aligncenter" width="450"] There are worse places to take a nap, though.\r\n(Photo: Graham-H)[\/caption]\r\n\r\nIn this case, then, we need to be careful about generalizing not because the sample is too small, but because we can\u2019t be sure it reflects the wider population to which we are generalizing.\r\nHow to Avoid Faulty Generalizations\r\nNone of the above means we cannot make generalizations in our arguments. Inductive reasoning \u2013 i.e., drawing probable conclusions based on evidence \u2013 is vital in many fields of study. But we need to draw conclusions based on reliable evidence. Thus, to avoid faulty generalizations, you should:\r\n\r\n\tMake sure generalizations are based on a large enough sample. You can use statistical tools to work out the minimum sample size required.\r\n\tUse a sample that is representative of the wider population. The sampling techniques you use will be important in this respect.\r\n\tIf you are using someone else\u2019s data, always check how they selected a sample before applying their results to a wider population.\r\n\r\nIn summary, make sure you don\u2019t generalize based on a small or unrepresentative sample. Having your documents proofread can also help you to express your arguments as clearly as possible, thus preventing misunderstandings from arising when you make generalizations.