Bonferroni method

Bon·fer·ro·ni meth·od

(bōn-fer-rō'nē meth'ŏd)
Multiple comparison method used in studies involving analysis of variance.
Medical Dictionary for the Health Professions and Nursing © Farlex 2012
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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.