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Statistics: tests, sample size and randomisation

Run tests on your datasets, size a study with a power curve, and generate reproducible allocation sequences — each with a methods sentence.

Updated 2026-09-05

Tests on a dataset

Open a dataset and choose Tests: independent t-test (Welch by default, Student's optional), Mann–Whitney U, paired t-test, Wilcoxon signed-rank, one-way ANOVA, Kruskal–Wallis, Pearson and Spearman correlation, chi-squared with Fisher's exact for 2×2 tables, and linear regression with one or more predictors.
Assign variables to the slots (scale variables for outcomes, categorical ones for groups; value labels name the groups). Cases with a missing value in any chosen variable are left out, and the active Select Cases filter is respected unless you untick it.
Every result gives the statistic, degrees of freedom, an exact p-value, an effect size (Cohen's d, η², r, φ or Cramér's V, rank-biserial r, R²) with confidence intervals where they exist, group summaries or the full table, warnings about small samples or unequal variances, and a sentence ready for the Methods or Results section. Tests are logged in the dataset's history.

Project → Analysis → Sample size & power: two means (Cohen's d or a difference and SD), two proportions, one proportion, correlation, chi-squared and survival (log-rank, Schoenfeld). Set α, power and expected dropout.
You get n per group, the total, the achieved power, a methods sentence you can paste, and for two-means designs a power curve showing how power grows with n.

Simple, permuted-block or stratified permuted-block allocation for any number of groups; block sizes are multiples of the group count and chosen at random per block.
A seed makes the sequence reproducible — keep it in the trial master file. The balance per group (and per stratum) is shown, the sequence can be downloaded as CSV, and a methods sentence is one click away.

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