1) 严格的 bonferroni,就是p值乘以检验的总数。 The adjustment methods include the Bonferroni correction ("bonferroni") in which the p-values are multiplied by the number of comparisons. 2) 略保守的矫正方法包括: "holm", "hochber
The adjustment methods include the Bonferroni correction ("bonferroni") in which the p-values are multiplied by the number of comparisons. Less conservative corrections are also included by Holm (1979) ("holm"), Hochberg (1988) ("hochberg"), Hommel (1988) ("hommel"), Benjamini & Hochberg (...
Adjust P-values for Multiple Comparisons Description Given a set of p-values, returns p-values adjusted using one of several methods.Usage p.adjust(p, method = p.adjust.methods, n = length(p))Arguments p: numeric vector of p-values (possibly with NAs). Any other R object is coerced by...
The same considerations apply when using significance tests in science: if you plan to do many such tests, you need to adjust for the fact that a "significant" result is more likely to occur by chance alone. (TheR languagehasfunctionsandpackagesfor making such "multiple comparison" corrections....
Corrections for multiple comparisons To account for multiple testing in genetic association studies, we used the Benjamini and Hochberg false discovery rate (FDR) method,29 which has been European Journal of Human Genetics 1266 XPO1 and OTX1 as candidate genes for autism X Liu et al shown to ...
Statistical comparisons were performed using two-tailed Student’s t test as well as one-way analyses of variance (ANOVAs) or two-way repeated measures ANOVAs. For post hoc analysis, we used Bonferroni’s corrections for multiple comparisons. Statistical analysis was performed with GraphPad Prism ...
In this section I will deal with the significance filter, multiple comparisons and some forms of P-hacking, and I need to point out immediately that most of the issues are not specific to P-values even if some of them are enabled by the unfortunate dichotomisation of P-values into ...
Researchers need to limit the testing they perform during a study and use the proper corrections for multiple comparisons and hypothesis tests. Learn more aboutPost Hoc Multiple Comparisonsand theBonferroni Correction. Excessive Model Fitting This problem is like excessive hypothesis testing but relates ...
1.17 0.145 0.050 0.194 0.124 0.187 0.092 0.647 0.422 0.678 0.174 0.0285 0.0149 0.0043 0.0089 0.00020 0.00034 0.00014 Significant taxonomic differences in mean elemental concentrations were determined using least squares mean t-tests where P values were adjusted using Bonferroni corrections (P = 0.017...
For secondary outcomes, corrections for multiple comparisons are not planned; thus, these outcome data will necessarily be interpreted as exploratory. All study results will be analyzed in the full analysis set (including patients randomly allocated to receive i.v. lidocaine or QLB) and in the ...