Lesack Kyle, Naugler Christopher
Faculty of Medicine, Bachelor of Health Sciences Program, Room G503, O'Brien Centre for the BHSc, 3330 Hospital Drive N.W. Calgary, Alberta T2N 4N1, 2.
J Pathol Inform. 2011;2:52. doi: 10.4103/2153-3539.91130. Epub 2011 Dec 26.
Increased type I error resulting from multiple statistical comparisons remains a common problem in the scientific literature. This may result in the reporting and promulgation of spurious findings. One approach to this problem is to correct groups of P-values for "family-wide significance" using a Bonferroni correction or the less conservative Bonferroni-Holm correction or to correct for the "false discovery rate" with a Benjamini-Hochberg correction. Although several solutions are available for performing this correction through commercially available software there are no widely available easy to use open source programs to perform these calculations. In this paper we present an open source program written in Python 3.2 that performs calculations for standard Bonferroni, Bonferroni-Holm and Benjamini-Hochberg corrections.
多重统计比较导致的I型错误增加仍然是科学文献中的一个常见问题。这可能会导致虚假结果的报告和传播。解决这个问题的一种方法是使用Bonferroni校正、不太保守的Bonferroni-Holm校正来校正P值组的“全家族显著性”,或者使用Benjamini-Hochberg校正来校正“错误发现率”。虽然有几种解决方案可以通过商业软件进行这种校正,但没有广泛可用的易于使用的开源程序来执行这些计算。在本文中,我们展示了一个用Python 3.2编写的开源程序,该程序可以执行标准Bonferroni、Bonferroni-Holm和Benjamini-Hochberg校正的计算。