Agent Skills
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pnas-statistics
GitHub用于强制执行PNAS统计与可重复性报告规范，涵盖效应量、不确定性、样本量定义、多重比较校正、随机化及预注册等关键要素。
Trigger Scenarios
结果报告P值但缺失效应量或n
声称独立实验但未明确复制情况
进行多重比较且无校正
缺乏预先指定的分析计划说明
代码不可复现
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill pnas-statistics -g -y
SKILL.md
Frontmatter
{
"name": "pnas-statistics",
"description": "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."
}
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.
Version History
- 1839142 Current 2026-07-05 14:11
