# 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. - Skill: `thedixitjain/pnas-statistics` (Agent Skill) - Install (CLI): `npx skillmds add thedixitjain/pnas-statistics` - Raw SKILL.md: https://api.skillmd.com/api/skills/thedixitjain/pnas-statistics/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: thedixitjain (https://skillmd.com/u/thedixitjain) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/thedixitjain/pnas-statistics --- # 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. - State **how the sample size was chosen** (power analysis or explicit rationale), not post-hoc. - Report **randomization** of subjects/treatments and **blinding** of measurement/analysis where applicable, or state why not. - Report **inclusion/exclusion criteria** and any excluded data, with reasons, decided in advance. ## Discipline-specific notes across PNAS divisions PNAS spans **Biological, Physical, and Social Sciences**, so match the rigor conventions of your division: - **Biological:** replication unit, ARRIVE-style animal reporting, antibody/reagent validation. - **Social/behavioral:** pre-registration is increasingly expected; report power, sampling frame, and deviations from the plan. - **Physical/computational:** report uncertainties, error propagation, and numerical reproducibility (seeds, solver settings). ## Avoid the classic reviewer kills - **Pseudoreplication**: treating technical replicates / cells from one animal as independent n. - **HARKing / p-hacking**: presenting exploratory findings as confirmatory. Label exploratory work as such. - **"Representative" images** with no quantification across replicates. - **Bar chart + SEM** masking a tiny, variable n. - Comparing two effects by their **significance** ("significant here, not there") instead of testing the **difference**. ## Reproducibility package - Analysis code in a repository (see `pnas-data`), with a README and environment/versions. - A reproducibility/reporting summary if requested; list software, versions, seeds. - Deterministic where possible; report random seeds for simulations/ML. ## Pre-registration & transparency (where relevant) - For confirmatory studies (especially human-subjects / behavioral work in the Social Sciences division), note **pre-registration** (OSF/AsPredicted) if done. - Separate pre-specified analyses from post-hoc exploration explicitly in the text. ## Before / after: a reporting sentence in PNAS register PNAS reviewers span divisions, so a statistics sentence has to survive a reader who does not share your field's shorthand. Tighten a vague claim into the reporting backbone. - **Before:** "Treatment significantly increased expression (P < 0.05, n = 3), confirming our hypothesis." - **After:** "Treatment raised expression 2.4-fold (95% CI 1.7–3.3; two-sided Welch's t test, P = 0.008; n = 6 biological replicates, each the mean of 3 technical replicates), consistent with the predicted mechanism." The revision names the effect and its uncertainty, states the unit of replication, gives an exact P, and separates biological from technical n — the four things a PNAS editor flags when a general-audience claim rests on thin evidence. ## PNAS editor / referee expectation checklist What a PNAS handling editor and cross-division referees actively look for: - [ ] **Broad significance is earned, not asserted** — the statistical advance supports the general claim in the Significance Statement, not a narrower one. - [ ] **Reporting standards met** — every panel's n, test, and error definition appears in its legend, not buried in Methods. - [ ] **Reproducibility** — a referee could re-run the analysis from deposited code, versions, and seeds (`pnas-data`). - [ ] **Data availability** — primary data underlying each quantitative figure is deposited, not "available on request." - [ ] **No selective reporting** — exploratory and confirmatory analyses are labeled; excluded data and its rationale are disclosed. ## Output format ``` 【Per-claim backbone】 effect+CI / n / unit-of-n / test / assumptions → list gaps 【Replication】 biological vs technical clear? yes/no 【Sample-size rationale】 power/justification present? yes/no 【Randomization & blinding】 reported / N/A-justified / missing 【Multiplicity】 corrected? method 【Division-specific rigor】 (Bio / Physical / Social) conventions met? yes/no 【Reproducibility】 code + versions + seeds present? yes/no 【Next】 pnas-data ``` ## Anti-patterns - **Do not** report P without effect size and n. - **Do not** count technical replicates as independent observations. - **Do not** infer "no effect" from a non-significant test on an underpowered sample. - **Do not** present post-hoc subgroup findings as if pre-specified. --- **Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `PNAS-Skills/skills/pnas-statistics/SKILL.md`