The phrase "multiple comparisons for" is correct and usable in written English.
You can use this phrase when you are comparing two or more items or ideas to one another. For example: "We conducted multiple comparisons for the different types of video game consoles to determine which had the best features."
Then we performed multiple comparisons for the main effects and the results are presented in Fig. 2.
Table 4 shows a summary of the Friedman test with Dunn's post-test for multiple comparisons for qualitative image noise.
We used nonparametric tests (chi-square test, Kruskal-Wallis H test, Mann-Whitney U test, and Spearman's rho) with Bonferroni correction for multiple comparisons for determining the statistical significance of the results.
Level of significance (multiple comparisons for each group): Moderate disability, *P < 0.05; **P < 0.01; Mild disability, °P < 0.05; °°P < 0.01; and Healthy, ^P < 0.05; ^^P < 0.01. Figure 2 Comparison between groups of the pain intensity (A) and perceived fatigue (B) immediately (6 min) and 24 hours after the provocation chewing test.
Mean residence times in each arm were compared using one-way ANOVA on (x + 0.5 1/2 transformed data (to insure normality) with arms as blocks, this was followed by Dunnett test for simple configuration (pheromone versus every other arm) or by Newman-Keul test (to avoid multiple comparisons) for choice configuration [44].
To address the issue of multiple comparisons, for each analysis, the significant cluster size was determined using a Monte Carlo simulation program (AlphaSim) as previously done in our laboratory [37, 38], with a cumulative proportion criterion of less than 0.05 [39].
The connectivity clusters that we report here survived correction for multiple comparisons for the entire brain.
We did not adjust for all multiple comparisons for all SNPs in all types of lymphoma.
Correction for multiple comparisons for the within-factor CONDITION was based on False Discovery Rate (FDR) [26].
An alternative approach to adjusting for multiple comparisons for assessing significance is to validate findings those pathways that show marginal significance across data sets.
To avoid Type I errors due to multiple comparisons, for the main effect of Stimulation a Bonferroni corrected P-value<0.007 P-value<0.007
