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In statistics, Fisher's method, also known as Fisher's combined probability test, is a technique for data fusion or "meta-analysis" (analysis of analyses). It was developed by and named for Ronald Fisher. In its basic form, it is used to combine the results from several independent tests bearing upon the same overall hypothesis (''H''0). ==Application to independent test statistics== Fisher's method combines extreme value probabilities from each test, commonly known as "p-values", into one test statistic (''X''2) using the formula : where ''p''''i'' is the p-value for the ''i''th hypothesis test. When the p-values tend to be small, the test statistic ''X''2 will be large, which suggests that the null hypotheses are not true for every test. When all the null hypotheses are true, and the ''p''''i'' (or their corresponding test statistics) are independent, ''X''2 has a chi-squared distribution with 2''k'' degrees of freedom, where ''k'' is the number of tests being combined. This fact can be used to determine the p-value for ''X''2. The distribution of ''X''2 is a chi-squared distribution for the following reason. Under the null hypothesis for test ''i'', the p-value ''p''''i'' follows a uniform distribution on the interval (). The negative natural logarithm of a uniformly distributed value follows an exponential distribution. Scaling a value that follows an exponential distribution by a factor of two yields a quantity that follows a chi-squared distribution with two degrees of freedom. Finally, the sum of ''k'' independent chi-squared values, each with two degrees of freedom, follows a chi-squared distribution with 2''k'' degrees of freedom. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Fisher's method」の詳細全文を読む スポンサード リンク
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