Hasty Generalisation
Hasty generalization (Anecdotal)
Definition: Making assumptions about a whole group or range of cases based on a sample that is inadequate (usually because it is atypical or just too small). Stereotypes about people ("librarians are shy and smart," "wealthy people are snobs," etc.) are a common example of the principle underlying hasty generalization.
Example: "My roommate said her philosophy class was hard, and the one I'm in is hard, too. All philosophy classes must be hard!" Two people's experiences are, in this case, not enough on which to base a conclusion.
The person committing the fallacy is misusing the following type of reasoning, which is known variously as Inductive Generalization, Generalization, and Statistical Generalization: 1. X% of all observed A's are B''s. 2. Therefore X% of all A's are Bs.
The fallacy is committed when not enough A's are observed to warrant the conclusion. If enough A's are observed then the reasoning is not fallacious.
Tip: Ask yourself what kind of "sample" you're using: Are you relying on the opinions or experiences of just a few people, or your own experience in just a few situations? If so, consider whether you need more evidence, or perhaps a less sweeping conclusion. (Notice that in the example, the more modest conclusion "Some philosophy classes are hard for some students" would not be a hasty generalization.)
Here are some more examples of hasty generalisations fallacies. See if you can identify the fallacy and write this in the following format
“A means B.”
We will then discuss what is wrong with each one: 1. Bill: "You know, those feminists all hate men."
Joe: "Really?"
Bill: "Yeah. I was in my philosophy class the other day and that Rachel chick gave a presentation."
Joe: "Which Rachel?"
Bill: "You know her. She's the one that runs that feminist group over at the Women's Center. She said that men are all sexist pigs. I asked her why she believed this and she said that her last few
Definition: Making assumptions about a whole group or range of cases based on a sample that is inadequate (usually because it is atypical or just too small). Stereotypes about people ("librarians are shy and smart," "wealthy people are snobs," etc.) are a common example of the principle underlying hasty generalization.
Example: "My roommate said her philosophy class was hard, and the one I'm in is hard, too. All philosophy classes must be hard!" Two people's experiences are, in this case, not enough on which to base a conclusion.
The person committing the fallacy is misusing the following type of reasoning, which is known variously as Inductive Generalization, Generalization, and Statistical Generalization: 1. X% of all observed A's are B''s. 2. Therefore X% of all A's are Bs.
The fallacy is committed when not enough A's are observed to warrant the conclusion. If enough A's are observed then the reasoning is not fallacious.
Tip: Ask yourself what kind of "sample" you're using: Are you relying on the opinions or experiences of just a few people, or your own experience in just a few situations? If so, consider whether you need more evidence, or perhaps a less sweeping conclusion. (Notice that in the example, the more modest conclusion "Some philosophy classes are hard for some students" would not be a hasty generalization.)
Here are some more examples of hasty generalisations fallacies. See if you can identify the fallacy and write this in the following format
“A means B.”
We will then discuss what is wrong with each one: 1. Bill: "You know, those feminists all hate men."
Joe: "Really?"
Bill: "Yeah. I was in my philosophy class the other day and that Rachel chick gave a presentation."
Joe: "Which Rachel?"
Bill: "You know her. She's the one that runs that feminist group over at the Women's Center. She said that men are all sexist pigs. I asked her why she believed this and she said that her last few