pnas-statistics
Use to enforce PNAS's statistics and reproducibility reporting — n and replication, test choice and assumptions, effect sizes with uncertainty, multiple-comparison control, randomization/blinding, sample-size justification, pre-registration where relevant, and reproducible code.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill pnas-statistics --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.
# Statistics & Reproducibility (pnas-statistics) ## When to trigger - Results report P values but not effect sizes or n. - "Three independent experiments" is claimed but replication is unclear. - Multiple comparisons are run with no correction. - A reviewer is likely to ask "were analyses pre-specified?" and there's no answer. - The analysis is not reproducible from the deposited code (`pnas-data`). ## The reporting backbone (every quantitative claim) Each claim needs: **effect size + uncertainty + n + test + what n means.** - [ ] **n** stated, with the unit of replication (biological vs technical replicates; cells vs animals vs subjects vs experiments). - [ ] **Effect size** with **95% CI** (preferred) or SD/SEM clearly labeled — not P alone. - [ ] **Exact P values** (e.g., P = 0.013), not "P < 0.05", unless extremely small. - [ ] **Test named and justified** (assumptions checked: normality, variance homogeneity, independence). - [ ] **Multiple comparisons** corrected (Bonferroni/Holm/FDR) when many tests are run. ## Replication and design - Distinguish **biological replication** (independent samples) from **technical replication** (re-measurement). The former is what counts. - St
- When to trigger
- The reporting backbone (every quantitative claim)
- Replication and design
- Discipline-specific notes across PNAS divisions
- Avoid the classic reviewer kills
- Reproducibility package
- Pre-registration & transparency (where relevant)
- Before / after: a reporting sentence in PNAS register
- PNAS editor / referee expectation checklist
- Output format
- Anti-patterns
What does the pnas-statistics skill do?
Use to enforce PNAS's statistics and reproducibility reporting — n and replication, test choice and assumptions, effect sizes with uncertainty, multiple-comparison control, randomization/blinding, sample-size justification, pre-registration where relevant, and reproducible code.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill pnas-statistics --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/Awesome-Journal-Skills, a repository with 909 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.