In contrast, if he infers that she is not interested when she is in fact interested, then he has made an error of false negative (what the statisticians call the "Type II" error).

相反，如果他推断，她不感兴趣，而实际上她感兴趣，那么他犯了假阴性的错误(统计学家称为“第二类”错误)。

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So we make two types of errors: a type I error, or false positive, is believing a pattern is real when it is not; a type II error, or false negative, is not believing a pattern is real when it is.

因此我们常犯两类错误：第一类，是虚假的肯定，当原型并不存在时它认为原型是真的；第二类，是虚假的否定，当原型存在时它却认为原型是虚假的。

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Causes of the two types of errors and how to control type II error are discussed and solution is proposed for reference.

本文对于两类错误的成因以及如何控制第二类错误进行了探讨，希望对于第二类错误的控制提出一些解决的方法。

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