Judicious use of multiple hypothesis tests
Judicious use of multiple hypothesis tests
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DOI：
10.1111/j.1523-1739.2005.00269.x
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发表时间：
2005-02-01
影响因子：
6.3
通讯作者：
Askins, RA
中科院分区：
文献类型：
--
作者：
Roback, PJ;Askins, RA
在分析统计结果表时，必须首先决定是否适当调整显著性水平。如果主要目标是假设生成或初步筛选潜在的保护问题，那么使用标准的比较显著性水平可能是合适的，以避免II型错误（没有检测到真实的差异或趋势）。然而，如果主要目标是对假设进行严格的检验，则需要对多个检验进行调整。为了控制族I型错误率（拒绝至少一个真实零假设的概率），标准Bonferroni方法（如霍尔姆方法）的顺序修改将提供比标准Bonferroni方法更大的统计功效。通过控制错误发现率（FDR）（发现显著的测试中假阳性的预期比例）的程序可以实现额外的功效。霍尔姆的顺序Bonferroni方法和两个FDR控制程序的结果进行了多元回归分析的栖息地变量和丰富的25种森林鸟类在日本的关系，和FDR控制程序提供了相当大的统计功率。
When analyzing a table of statistical results, one must first decide whether adjustment of significance levels is appropriate. If the main goal is hypothesis generation or initial screening for potential conservation problems, then it may be appropriate to use the standard comparisonwise significance level to avoid Type II errors (not detecting real differences or trends). If the main goal is rigorous testing of a hypothesis, however, then an adjustment for multiple tests is needed. To control the familywise Type I error rate (the probability of rejecting at least one true null hypothesis), sequential modifications of the standard Bonferroni method, such as Holm's method, will provide more statistical power than the standard Bonferroni method. Additional power may be achieved through procedures that control the false discovery rate (FDR) (the expected proportion of false positives among tests found to be significant). Holm's sequential Bonferroni method and two FDR-controlling procedures were applied to the results of multiple-regression analyses of the relationship between habitat variables and the abundance of 25 species of forest birds in Japan, and the FDR-controlling procedures provided considerably greater statistical power.