Do Your Groups Really Differ? Find Out In Minutes

How it works

The analysis compares your numeric outcome across the groups you define, running the full standard toolkit at once. For two groups: Welch's t-test (the robust default), Student's t-test, and the Mann-Whitney U test, plus Cohen's d for effect size. For three or more: one-way ANOVA, Welch's ANOVA, and the Kruskal-Wallis test, plus eta-squared and Tukey HSD pairwise comparisons. A normality check on the residuals decides whether the parametric or nonparametric result is the safer headline.

Use it whenever you have one numeric measure and a column that splits rows into groups — experiment variants, regions, plans, cohorts — and want to know if the difference is real and how big it is.

Not for comparing two categorical columns (use a chi-square / categorical association tool), for paired before/after measurements on the same subjects, or for outcomes that are counts of rare events.

What data do you need?

One numeric outcome plus one group column. For example, weekly sales by store region:

Minimum 10 rows · Best with 30-50,000 rows and 2-8 groups

What's in the report?

Standard-library analysis: does a numeric outcome differ between groups? One deck runs the whole toolkit — Welch and Student t-tests for two groups, one-way and Welch ANOVA for three or more, plus Mann-Whitney and Kruskal-Wallis nonparametric checks, effect sizes (Cohen's d, eta-squared), and pairwise differences with confidence intervals — and tells you which result to trust for your data.