Bonferroni method

Bon·fer·ro·ni meth·od

(bōn-fer-rō'nē meth'ŏd)
Multiple comparison method used in studies involving analysis of variance.
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The significance level was set at 0.05/2=0.025 based on the Bonferroni method to adjust for two tests conducted under the two treatments separately.
The Bonferroni method was further used to determine the origin of this difference, and the revised P = 0.017.
Compared with full searching (dynamic) method, the Bonferroni method shows no significant difference between the methods for noises [alpha] = 5,4,3, except for a significant difference between the method that maximizes the sum of efficiency with the full searching method at [alpha] = 5 (p < .01).
Post hoc analysis between the different pairs of ethnicities: multiple comparisons were corrected with the Bonferroni method; significant differences are highlighted as A for Asian versus White and B for Asian versus Black African and Caribbean.
The nonparametric Kruskal-Wallis test was performed to determine significant differences in IHC scores between the three groups, followed by multiple comparison with an adjustment of p value by the Bonferroni method (a pairwise test smaller than 0.05/3 = 0.017 was significant at the 0.05 level and 0.01/3 = 0.0033 at the 0.01 level).
Odds Ratio (OR) values were calculated using the SPSS IBM software, compared by means of the two-tailed Pearson's Chi-Square or Fisher exact test when expected values in any cell are <5, multiple comparisons correction for adjusted-p values was made with SAS University software using false discovery rate (FDR) method (24) and Bonferroni method. Finally, the Hardy-Weinberg (HW) equilibrium of genotypic frequencies in both groups was determined with the online software GENPOP version 4.2.
When controlling for Type I error at the 0.05 level across all three comparisons, Holm's sequential Bonferroni method was used as shown in Table 1.
In order to prevent type-1 errors resulting from multiple comparisons, we used the Bonferroni method.
Significant difference according to the Bonferroni method was defined as a p value of <0.05 (*).
Post hoc t-tests corrected by the Bonferroni method indicated that the Low AQ group had larger P300 amplitudes in the Unexpected conditions (SD = 1.712) than in the Expected conditions (SD = 2.521; p = .036, [[eta].sub.p.sup.2] = .234).
In addition, a post hoc multiple-comparison analysis was used with Bonferroni method. Values of P < 0.05 were considered statistically significant.