If he infers that she is interested when she is in fact not interested, then he has made an error of false positive (what the statisticians call the "Type I" error).
如果他推断说，她感兴趣，而她其实上不感兴趣，那么他犯了假阳性的错误(统计人员所谓的“第一类”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。
This is also known as a Type I error.
这也被称为第一种误差。
This is also known as a Type I error.
这也被称为第一种误差。
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