List every statistical test this engine implements, with its effect-size metric, design (one- or two-group) and supported analysis types.

Describe one test: its exact G*Power menu title, effect-size metric, extra parameters, and which analysis types it supports.

Run one power analysis and return the full numeric result plus ready-to-paste prose.

solve_for: "n" -> smallest sample size reaching power (a priori) "power" -> achieved power of an explicit design (post hoc) "alpha" -> alpha needed to reach power (criterion) "effect_size" -> smallest detectable effect (sensitivity) "compromise" -> alpha and power implied by beta_alpha_ratio

Supply n1+n2 or n(+allocation_ratio) for the two-group test and n otherwise. predictors applies to ttest-regression-coef; df to ttest-generic.

Convert raw descriptive statistics into the standardised effect size a test needs.

Calculators: d-two-independent-means mean1, mean2, sd1, sd2 [, equal_n, n1, n2] dz-matched-pairs mean1, mean2, sd1, sd2, correlation dz-from-difference mean_difference, sd_difference d-one-sample mean, reference, sd f2-from-r-squared r_squared f2-from-partial-r-squared partial_r_squared r-from-d-balanced d

Tabulate power against sample size -- useful for showing a user the trade-off instead of a single number. Capped at 500 rows.

Report engine version, the specification it implements, and the documented behavioural findings it encodes. Use this when a user asks how trustworthy the numbers are or where they come from.