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# Inductive Fallacy Study Guide (page 2)

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Updated on Apr 25, 2014

## Hasty Generalization

In this fallacy, there's not enough data or cases to warrant a generalization, For example, a waitress complains, "Those Southerners left me a lousy tip. All Southerners are cheap!" She has made a generalization about tens of millions of people based on an experience with a few of them. A hasty generalization like hers takes this form:

1. A very small sample A is taken from population B.
2. Generalization C is made about population B based on sample A.

There are two common reasons for hasty generalizations. One is because of bias or prejudice. For instance, a sexist person could conclude that all women are bad drivers because he had an accident with one. Hasty generalizations are also often made because of negligence or laziness. It is not always easy to get a large enough sample to draw a reasonable conclusion. But if you can't get the right sample, do not make the generalization. Better yet, make an attempt to add to your sample size. Improve your argument with better evidence.

How do you know when your sample is large enough? There is no one rule that applies to every type of sample, so you will need to use the "practicality and reasonability" test. What is the largest sample you can gather that makes sense, practically? Will it be large enough so that you can reasonably make a generalization about it? Reread the section on statistics in Lesson 10 to refresh your memory about the problems that can occur when taking a sample, and how those problems can be recognized and/or avoided.

Try to avoid jumping to conclusions, and learn to spot when others have done so in their arguments. If a generalization is the result of prior opinions about people in question, the bias needs to be removed and the generalization rethought, based on real information. You also need to take the time to get a large enough sample so that a generalization drawn from that sample makes sense. To generalize about a large group of people, you need to find out about many more of them than when generalizing about a very small group.

#### Examples

• I asked eight of my coworkers what they thought of the new manufacturing rules, and they all thought they are a bad idea. The new rules are generally unpopular.
• That new police drama is a really well done show. All police dramas are great shows.
• Omar threw the ball from left field to the second baseman, and he made an incredible double play. Whenever Omar gets the ball, he should throw it to the second baseman.

#### Tip

You can avoid erroneous generalizations by being specific. People should know exactly what your message is.

## Composition

This fallacy occurs when the qualities of the parts of a whole are assumed to also be the qualities of the whole. It is a fallacy because there is no justification for making this assumption. For example, someone might argue that because every individual part of a large machine is lightweight, the machine itself is lightweight.

This argument is fallacious; you cannot conclude that because the parts of a whole have (or lack) certain qualities, the whole that they are parts of has those qualities. Let's look at another example. A girl's mother tells her, "You love meatloaf, applesauce, ice cream, and pickles. So, you will love what we're having for dinner tonight! I made a meatloaf, applesauce, ice cream, and pickle casserole." This is an example of the fallacy of composition because, while the girl loves all of those foods individually, one cannot reasonably conclude that she will love them when they are put together as a casserole (a whole made of the likeable parts is not necessarily likeable).

Sometimes an argument that states the properties of the parts are also the properties of the whole can be a strong one. For example, if every piece of a table is wood, there's no fallacy in the conclusion that the whole table is wood. To determine whether a statement is fallacious or not, you need to determine if there's a justification for the inference from parts to whole.

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