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推论统计学中,零假设英语:null hypothesis,又译虚无假设原假设,符号:)是做统计检验时的一类假设。零假设的内容一般是希望能证明为错误的假设,或者是需要着重考虑的假设。在相关性检验中,一般会取“两者之间无关联”作为零假设,而在独立性检验中,一般会取“两者之间非獨立”作为零假设。

与零假设相对的是备择假设英语Alternative hypothesis(或对立假设),即希望证明是正确的另一种可能。从数学上来看,零假设和备择假设的地位是相等的,但是在统计学的实际运用中,常常需要强调一类假设为应当或期望实现的假设。如果一个统计检验的结果拒绝零假设(结论不支持零假设),而实际上真实的情况属于零假设,那么称这个检验犯了第一类错误。反之,如果检验结果支持零假设,而实际上真实的情况属于备择假设,那么称这个检验犯了第二类错误。通常的做法是,在保持第一类错误出现的机会在某个特定水平上的时候(即显著性差异值或α值),尽量减少第二类错误出现的概率。

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  • Adèr, H. J.; Mellenbergh, G. J. & Hand, D. J. Advising on research methods: A consultant's companion. Huizen, The Netherlands: Johannes van Kessel Publishing. 2007. ISBN 90-79418-01-3. 
  • Efron, B. Large-Scale Simultaneous Hypothesis Testing. Journal of the American Statistical Association. 2004, 99 (465): 96. doi:10.1198/016214504000000089.  The application of significance testing in this paper is an outlier. Tests to find a null hypothesis? Not trying to show significance, but to find interesting cases?
  • Rice, William R.; Gaines, Steven D. 'Heads I win, tails you lose': testing directional alternative hypotheses in ecological and evolutionary research. TREE. June 1994, 9 (6): 235–237. doi:10.1016/0169-5347(94)90258-5.  Directed tests combine the attributes of one-tailed and two-tailed tests. "...directed tests should be used in virtually all applications where one-sided tests have previously been used, excepting those cases where the data can only deviate from H0, in one direction."

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