power-analysis-guide
Sample size calculation and statistical power analysis guide
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill power-analysis-guide --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Power Analysis Guide Calculate appropriate sample sizes for your study using power analysis, understand effect sizes, and avoid underpowered or wastefully overpowered designs. ## Core Concepts ### The Four Parameters of Power Analysis Every power analysis involves four interrelated quantities. Fix any three to solve for the fourth: | Parameter | Symbol | Definition | Typical Value | |-----------|--------|-----------|---------------| | **Effect size** | d, r, f, etc. | Magnitude of the phenomenon you expect to detect | Varies by field | | **Significance level** (alpha) | alpha | Probability of Type I error (false positive) | 0.05 | | **Statistical power** (1 - beta) | 1 - beta | Probability of detecting a true effect | 0.80 or 0.90 | | **Sample size** | N | Number of observations needed | Solve for this | ### Error Types | | H0 is true (no effect) | H0 is false (effect exists) | |---|---|---| | **Reject H0** | Type I error (alpha) | Correct (power = 1 - beta) | | **Fail to reject H0** | Correct (1 - alpha) | Type II error (beta) | ## Effect Size Conventions ### Cohen's d (Two-Group Comparison) ``` d = (M1 - M2) / SD_pooled ``` | Size | Cohen's d | Interpretation | |------|--------
- Core Concepts
- The Four Parameters of Power Analysis
- Error Types
- Effect Size Conventions
- Cohen's d (Two-Group Comparison)
- Correlation (r)
- Cohen's f (ANOVA)
- Odds Ratio (Logistic Regression)
- Power Analysis in Python (statsmodels)
- Two-Sample t-Test
- Paired t-Test
- One-Way ANOVA
- Chi-Square Test
- Multiple Regression
What does the power-analysis-guide skill do?
Sample size calculation and statistical power analysis guide
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill power-analysis-guide --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.