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Fearful Generalization

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Reasoning is a lot like chainsaw. It is a useful tool when handled properly, but when wielded badly (perhaps while impaired by fear) it can create a bloody mess. While this analogy can be applied broadly to the various methods of logic, the focus is on the inductive generalization and how it can be turned into a wayward chainsaw under the influence of fear. Consisting of a premise and a conclusion, the inductive generalization is a simple argument: Premise 1: P% of observed Xs are Ys. Conclusion: P% of all Xs are Ys. Briefly put, the quality of an inductive generalization depends on the quality of the first premise, which is usually referred to as being a sample. The larger and more representative the sample, the stronger the argument (the more likely it is that the conclusion will be true if the premise is true). There are thus two main ways an inductive generalization can be bad. The first occurs when the sample is too small to adequately support the conclusion. For. . .

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News source: A Philosopher's Blog

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