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。
Compared with the mixed linear model analysis, the analysis of variance could result in higher type I or II statistical error rates.
相对线性混合模型分析法，方差分析法分析该试验时，依测验的效应不同会导致第一类或第二类统计错误率的增大。
Compared with the mixed linear model analysis, the analysis of variance could result in higher type I or II statistical error rates.
相对线性混合模型分析法，方差分析法分析该试验时，依测验的效应不同会导致第一类或第二类统计错误率的增大。
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