Sentence examples for multiple groups testing from inspiring English sources

For continuous variables, differences between two groups were tested by means of either Student t-test or Mann–Whitney U-test, while for multiple groups testing was done by ANOVA or Kruskal-Wallis ANOVA.

Finally, results of multiple group testing as displayed in Table 6 indicate invariance across the three measurement points (ΔCFI = 0.000 and 0.000; ΔRMSEA = 0.001 and 0.002; ΔSRMR = 0.004 and 0.002).

The multiple group test showed the transcription levels among these three groups were significant different (P-value = 0.006).

Appropriate multiple group tests (one-way ANOVA and Kruskal-Wallis) were used to compare the four groups to determine if there was a difference between them and, if positive, a Tukey's honestly significance difference test or individual Mann-Whitney U tests with Bonferroni's correction were used to determine where the difference lay, depending on the normality of the data.

Comparisons between the groups were performed with the two independent samples' t-test; those between multiple groups were tested with one-way ANOVA, and those between any groups were tested with SNK.

Two of 21 subjects (9.5%) in the single-dose groups and 3 of 11 subjects (27.3%) in the multiple-dose groups tested positive for ADAs following MEDI-546 administration.

Overall differences between multiple groups were tested using the nonparametric Kruskal-Wallis test.

Comparisons between multiple groups were tested using one-way ANOVA and Bonferroni post hoc tests.

Comparisons between multiple groups were tested using one-way ANOVA with Bonferroni post hoc test, and comparisons to the DMSO group reported.

Comparisons between multiple groups were tested using one-way ANOVA with Bonferroni post hoc test, and comparisons to the -JQ1 or GM1 group reported.

Multiple groups were tested by one-way ANOVA, and Dunnett's multiple comparison test was used to determine which groups were significantly different from the control group.