Agent Skills
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pnasnexus-statistics
GitHub用于确保PNAS Nexus统计与可重复性报告规范，涵盖效应量、样本量、多重比较校正及预注册报告等要求。
Trigger Scenarios
结果仅报告P值而缺乏效应量或n
声称独立实验但复制不明确
未校正多重比较
分析不可复现
确认性研究考虑预注册
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill pnasnexus-statistics -g -y
SKILL.md
Frontmatter
{
"name": "pnasnexus-statistics",
"description": "Use to enforce PNAS Nexus's statistics and reproducibility reporting — n and replication, test choice and assumptions, effect sizes with uncertainty, multiple-comparison control, randomization\/blinding, sample-size justification, and reproducible code. Also covers whether a Registered Report (Stage 1\/2) is the right route for confirmatory work."
}
Statistics & Reproducibility (pnasnexus-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 (
pnasnexus-data). - The study is confirmatory and you want reviews before collecting data — consider a Registered Report.
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.
Registered Reports: a PNAS Nexus route for confirmatory work
PNAS Nexus offers Registered Reports, where the study design and analysis plan are peer-reviewed before data are collected (Stage 1, ≤3 pp), and — on in-principle acceptance — the completed study (Stage 2) is published largely regardless of whether the hypothesis was supported, provided the pre-registered plan was followed.
Consider a Registered Report when:
- The study is confirmatory / hypothesis-testing and you want to guard against p-hacking and publication bias.
- A null or mixed result would still be informative to the field.
- The design benefits from reviewer input before the expense of data collection.
In the Stage 2 manuscript, separate pre-registered (confirmatory) analyses from post-hoc (exploratory) ones explicitly, and report deviations from the Stage 1 plan.
Discipline-specific notes across PNAS Nexus divisions
PNAS Nexus spans biological/health/medical, physical sciences & engineering, and social & political sciences, so match the rigor conventions of your division:
- Biological / health / medical: replication unit, ARRIVE-style animal reporting, antibody/reagent validation, clinical-study reporting standards (CONSORT/STROBE) where applicable.
- Social / political / behavioral: pre-registration is increasingly expected; report power, sampling frame, and deviations from the plan.
- Physical / engineering / 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 (or run a Registered Report).
- "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 public repository, archived for a DOI (see
pnasnexus-data), with a README and environment/versions. - Deterministic where possible; report random seeds for simulations/ML.
- Because PNAS Nexus mandates that code and data be available in a public repository upon publication, build the reproducibility package as you go — it is not optional here.
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
【Registered Report?】 confirmatory work → Stage 1/2 considered? yes/no/N-A
【Division-specific rigor】 (Bio-Health-Medical / Physical-Engineering / Social-Political) conventions met? yes/no
【Reproducibility】 code + versions + seeds in a public repo (mandatory)? yes/no
【Next】 pnasnexus-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 — use a Registered Report for true confirmatory tests.
- Do not defer the reproducibility package — public data/code is mandatory at PNAS Nexus.
Version History
- 1839142 Current 2026-07-05 14:10
