Paired or unpaired t tests. Reports P values and confidence intervals.

Automatically generate volcano plot (difference vs. P value) from multiple t test analysis.

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

Kolmogorov-Smirnov test to compare two groups.

Wilcoxon test with confidence interval of median.

Perform many t tests at once, using False Discovery Rate (or Bonferroni multiple comparisons) to choose which comparisons are discoveries to study further.

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.

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)

Many multiple comparisons test are accompanied by confidence intervals and multiplicity adjusted P values.

Greenhouse-Geisser correction so repeated measures one-, two-, and three-way ANOVA do not have to assume sphericity. When this is chosen, multiple comparison tests also do not assume sphericity.

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

Fisher’s exact test or the chi-square test. Calculate the relative risk and odds ratio with confidence intervals.

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

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

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).

Kaplan-Meier survival analysis. Compare curves with the log-rank test (including test for trend)

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