Comparison of data from nested data tables using nested t test or nested one-way ANOVA (using mixed effects model).

Analysis of repeated measures data (one-, two-, and three-way) using a mixed effects model (similar to repeated measures ANOVA, but capable of handling missing data).

Three-way ANOVA (limited to two levels in two of the factors, and any number of levels in the third).

Tukey, Newman-Keuls, Dunnett, Bonferroni, Holm-Sidak, or Fisher’s LSD multiple comparisons testing main and simple effects.

Two-way ANOVA, with repeated measures in one or both factors.

Two-way ANOVA, even with missing values with some post tests.

Calculate the relative risk and odds ratio with confidence intervals.

Fisher's exact test or the chi-square test.

Kruskal-Wallis or Friedman nonparametric one-way ANOVA with Dunn's post test.

When this is chosen, multiple comparison tests also do not assume sphericity.

Greenhouse-Geisser correction so repeated measures one-, two-, and three-way ANOVA do not have to assume sphericity.Many multiple comparisons test are accompanied by confidence intervals and multiplicity adjusted P values.One-way ANOVA without assuming populations with equal standard deviations using Brown-Forsythe and Welch ANOVA, followed by appropriate comparisons tests (Games-Howell, Tamhane T2, Dunnett T3).Ordinary or repeated measures ANOVA followed by the Tukey, Newman-Keuls, Dunnett, Bonferroni or Holm-Sidak multiple comparison tests, the post-test for trend, or Fisher’s Least Significant tests.Perform many t tests at once, using False Discovery Rate (or Bonferroni multiple comparisons) to choose which comparisons are discoveries to study further.

Wilcoxon test with confidence interval of median.

Kolmogorov-Smirnov test to compare two groups.

Nonparametric Mann-Whitney test, including confidence interval of difference of medians.

Automatically generate volcano plot (difference vs.

Reports P values and confidence intervals.

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