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)。
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。
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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