Pg Power

PG Power

Use when the user asks for sample size, power, detectable effect, or planning assumptions.

Load

Read:

• ../../references/supervision-gates.md
• ../../references/pingouin-api-quickref.md

Function Choice

• One-sample, paired, or equal-n two-sample t test -> pg.power_ttest.
• Unequal independent groups -> pg.power_ttest2n.
• Between-subject ANOVA -> pg.power_anova.
• Repeated-measures ANOVA -> pg.power_rm_anova.
• Correlation -> pg.power_corr.

Required Inputs

• Target test and design.
• Effect size assumption and source: prior study, smallest effect size of interest, pilot, or convention.
• Alpha.
• Desired power, usually .80 or .90.
• Tail/alternative and allocation ratio where relevant.
• Number of groups or repeated measurements.

Code Patterns

Two-sample t test:

n = pg.power_ttest(d=0.5, n=None, power=0.80,
                   alpha=0.05, contrast="two-samples")
print(n)

Unequal groups:

power = pg.power_ttest2n(nx=30, ny=45, d=0.5, alpha=0.05)
print(power)

Correlation:

n = pg.power_corr(r=0.3, n=None, power=0.80, alpha=0.05)
print(n)

ANOVA:

n = pg.power_anova(eta_squared=0.06, k=3, n=None,
                   power=0.80, alpha=0.05)
print(n)

Reporting

State:

• Solved quantity.
• All fixed assumptions.
• Whether n is per group or total. If Pingouin output meaning is uncertain, verify with docs/help and say so.
• Attrition inflation if the user gives expected dropout.
• Sensitivity table for multiple plausible effect sizes when planning is uncertain.

Guardrails

• Do not pretend conventional effect sizes are study-specific evidence.
• Do not compute post hoc power as if it adds evidential value beyond CI/effect size unless the user explicitly requests it.
• For complex mixed/nested designs, Pingouin power functions may be insufficient; recommend simulation or specialized tools.
• End planning answers with one compact S0-S5 audit line where relevant.