pg-powerlisted
Install: claude install-skill Exekiel179/pingouin-psych-stats
# 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:
```python
n = pg.power_ttest(d=0.5, n=None, power=0.80,
alpha=0.05, contrast="two-samples")
print(n)
```
Unequal groups:
```python
power = pg.power_ttest2n(nx=30, ny=45, d=0.5, alpha=0.05)
print(power)
```
Correlation:
```python
n = pg.power_corr(r=0.3, n=None, power=0.80, alpha=0.05)
print(n)
```
ANOVA:
```python
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 dropou