Out of the 166 articles published in Journal of Industrial Microbiology and Biotechnology (JIMB) in 2019–2020 (not including special issues or review articles), 51 of them used a statistical test to compare two or more means. The most popular test was the (Standard) t-test, which often was used to compare several pairs of means. Other statistical procedures used included Fisher's Least Significant Difference (LSD), Tukey's Honest Significant Difference (HSD), and Welch's t-test; and to a lesser extent Bonferroni, Duncan's Multiple Range, Student-Newman-Keuls, and Kruskal-Wallis tests. This manuscript examines the performance of some of these tests with simulated experimental data, typical of those reported by JIMB authors. The results show that many of the most common procedures used by JIMB authors result in statistical conclusions that are prone to have large false positive (Type I) errors. These error-prone procedures included the multiple t-test, multiple Welch's t test, and Fisher's LSD. These multiple comparisons procedures were compared with alternatives (Fisher-Hayter, Tukey's HSD, Bonferroni, and Dunnett's t-tests) that were able to better control Type I errors.

中文翻译：

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JIMB 作者在比较平均值时使用的统计程序的讨论和评估

在 2019-2020 年工业微生物学与生物技术杂志 (JIMB) 上发表的 166 篇文章中（不包括特刊或评论文章），其中 51 篇使用统计检验来比较两种或多种平均值。最流行的检验是（标准）t 检验，它通常用于比较几对均值。使用的其他统计程序包括 Fisher 最小显着差异 (LSD)、Tukey 诚实显着差异 (HSD) 和 Welch t 检验；以及较小程度上的 Bonferroni、Duncan 多重范围、Student-Newman-Keuls 和 Kruskal-Wallis 检验。本手稿使用模拟实验数据（JIMB 作者报告的典型数据）检查了其中一些测试的性能。结果表明，JIMB 作者使用的许多最常用程序得出的统计结论很容易出现较大的误报（I 型）错误。这些容易出错的程序包括多重 t 检验、多重 Welch t 检验和 Fisher LSD。将这些多重比较程序与能够更好地控制 I 型错误的替代方法（Fisher-Hayter、Tukey's HSD、Bonferroni 和 Dunnett t 检验）进行比较。