Inductive Fallacies

Inductive reasoning consists of inferring from the properties of a sample to the properties of a whole class of entities.

For example, suppose we have a barrel containing of 1,000 beans. Some of the beans are black and some of the beans are white. Suppose now we take a sample of 100 beans from the barrel and that 50 of them are white and 50 of them are black. Then we could infer inductively that half the beans in the barrel (that is, 500 of them) are black and half are white.

All inductive reasoning depends on the similarity of the sample and the population. The more similar the same is to the population as a whole, the more reliable will be the inductive inference. On the other hand, if the sample is relevantly dissimilar to the population, then the inductive inference will be unreliable.

The following inductive fallacies are described in this section:

Hasty Generalization
Unrepresentative Sample
False Analogy
Slothful Induction
Fallacy of Exclusion

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