Pg mean tests

Skill Exekiel179/pingouin-psych-stats/skills/pg-mean-tests

Skill plugin for Claude Code, Codex & other AI agents: small, safety-first Pingouin workflows for psychology statistics — assumption checks, S0–S5 supervision gates, reproducible Python, and APA-style reporting.

Install

npx -y skills add Exekiel179/pingouin-psych-stats --skill pg-mean-tests

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Run or generate Pingouin code for one-sample, independent, paired, Welch, and corrected pairwise mean comparisons in psychology studies.

SKILL.md

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PG Mean Tests

Use for t tests and post hoc pairwise comparisons where the dependent variable is approximately continuous.

Load

Read:

../../references/supervision-gates.md
../../references/pingouin-api-quickref.md
../../references/pingouin-optimization.md
../../references/apa-output-template.md if writing results.

Decision Rules

One sample against a known value -> pg.ttest(x, y=<value>).
Two independent groups -> pg.ttest(x, y, paired=False, correction="auto").
Same participants measured twice -> pg.ttest(x, y, paired=True).
More than two group levels or multiple pairwise contrasts -> pg.pairwise_tests.
Non-parametric pairwise comparisons -> pg.pairwise_tests(..., parametric=False).

Required Inputs

Outcome column.
Group or condition column.
Subject ID for paired/repeated comparisons.
Which comparisons are planned versus post hoc.
Multiple-comparison correction: default to holm for post hoc families unless the user specifies another correction.
Alternative hypothesis: default to two-sided.

Code Patterns

Independent t test:

x = df.loc[df["group"].eq("A"), "score"]
y = df.loc[df["group"].eq("B"), "score"]
res = pg.ttest(x, y, paired=False, correction="auto",
               alternative="two-sided", confidence=0.95).round(3)
pg.print_table(res)

Paired t test from wide columns:

res = pg.ttest(df["pre"], df["post"], paired=True,
               alternative="two-sided", confidence=0.95).round(3)
pg.print_table(res)

Pairwise tests:

res = pg.pairwise_tests(data=df, dv="score", between="group",
                        parametric=True, padjust="holm",
                        effsize="hedges").round(3)
pg.print_table(res)

Repeated pairwise tests:

res = pg.pairwise_tests(data=df, dv="score", within="condition",
                        subject="id", padjust="holm",
                        effsize="hedges").round(3)

Output Checks

Inspect res.columns before writing prose. Expected t-test columns often include T, dof, p-val, CI95%, cohen-d, BF10, and power.

Guardrails

Do not use deprecated pairwise_ttests.
Do not call independent tests for paired data.
Do not omit multiplicity correction for exploratory multi-comparison families.
Report Welch/student choice when independent groups are imbalanced.
If the outcome is ordinal or severely non-normal with small n, mention non-parametric alternatives.
End result-bearing answers with one compact S0-S5 audit line.