Comparing groups (t-test / ANOVA)

Compares a numeric measure across independent groups and routes automatically to the right test family: two groups → a t-test, three or more → ANOVA.

When to use it

When a grouping variable splits respondents into separate groups (e.g. passed vs. failed, or region A/B/C) and you want to know whether a measure differs between them.

Inputs

  • Dependent variable(s) — the numeric measure(s) to compare. You can list several.

  • Grouping — exactly one categorical column defining the groups.

Options

Method

Detect automatically, Parametric homogeneous (Student’s t / standard ANOVA), Parametric inhomogeneous (Welch’s), or Non-parametric (Mann–Whitney for two groups / Kruskal–Wallis for more).

Assumption checks

Auto / Yes / No — runs checks such as Levene’s test for equal variances and a normality check. (With Detect automatically, assumption checks must be on so the method can be chosen.)

Other

Effect size (Cohen’s d for two groups; η² / partial η² for ANOVA), Confidence intervals, handling of missing grouping values, Verbal indicators (in-table columns), Verbal report (dropdown for how much written interpretation: None / Key findings / Significant only / Full), Number columns, and Plots.

Output

  • A descriptives-by-group table.

  • The test result with statistic, df, p, and (if requested) effect size and CI.

  • Assumption-check results when enabled.

  • A post-hoc comparison for ANOVA where applicable.

Notes

  • Groups need a minimum number of cases; very small groups are reported as insufficient.

  • For comparing conditions measured on the same people (repeated measures), use Paired / repeated measures instead.