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name: stats-reporting-audit description: >- Audit, revise, or draft manuscript statistical reporting for Nature / high-impact journal submissions. Use when the user asks to check statistical analysis sections, p values, confidence intervals, sample size, biological versus technical replicates, randomization, blinding, multiple-comparison correction, model assumptions, figure legends, Results statistics wording, reviewer comments about statistics, or Chinese academic drafts needing publication-ready Statistical analysis text. Also trigger on general paper-statistics requests such as 统计审查、统计分析小节、统计方法、p值、样本量、重复数、多重比较、置信区间、效应量、图注统计、审稿人统计意见. license: Apache-2.0

Nature Statistics Reporting Skill

Use this skill to make manuscript statistics transparent, reproducible, and appropriately bounded. It is a reporting and review skill, not a substitute for a statistician reanalysing raw data unless the user supplies the data and explicitly asks for computation.

Default stance

• Prioritize design transparency over decorative statistical language.
• Separate three questions: what was measured, what unit was analysed, and what inference was claimed.
• Treat the independent experimental unit as the default n; do not silently treat cells, fields of view, repeated readings, spectra, model runs, or technical replicates as independent biological or experimental samples.
• Prefer effect sizes, uncertainty intervals, sample sizes, and exact test definitions over significance-only phrasing.
• State missing information as AUTHOR_INPUT_NEEDED instead of inventing sample sizes, tests, software, corrections, exclusion rules, randomization, or blinding.
• If a journal-specific instruction, study-type guideline, or field standard conflicts with this skill, follow the more specific source and mark the source used.

Accepted inputs

The skill may receive:

• a Statistical analysis / Methods subsection
• Results paragraphs containing test statistics or p values
• figure panels, legends, captions, or source-data notes
• reviewer comments about statistics
• author notes in Chinese or English
• tables of reported comparisons
• raw or summary data, only when the user wants a concrete reanalysis or figure-statistics check

If the input is partial, run a bounded audit and state which parts cannot be assessed.

Workflow

1. Classify the task. Decide whether the user wants audit, rewrite, draft, reviewer-response support, figure-statistics alignment, or data-backed reanalysis.
2. Extract the design. Identify groups, treatments, time points, endpoints, blocking factors, repeated measures, randomization, blinding, exclusions, and missing-data handling.
3. Define n and replication. Separate independent experimental units, biological replicates, technical replicates, repeated measures, cells/fields/subsamples, simulations, and pooled observations.
4. Map claims to analyses, both ways. For each result claim, record the comparison/model, test family, assumptions, correction strategy, effect estimate, uncertainty, and exact p-value policy. Then map the other way: every test, statistic or interval that Statistical analysis declares must correspond to a result the paper reports. A declared-but-unused statistic (for example a paired t declared for every comparison while the text reports only mean differences and sign consistency across seeds) is removed from the Methods, not added to the Results.
5. Check common failure modes. Use references/common-failure-modes.md when the text involves nested data, many comparisons, cell-level measurements, interaction claims, correlations, regression, outliers, small samples, or significance-only reasoning.
6. Check reporting completeness. Use references/statistical-reporting.md to verify that Methods and Results give enough information for readers and reviewers to understand the analysis.
7. Align figure statistics. Use references/figure-statistics.md when figure legends, panel labels, stars, error bars, box plots, violin plots, source data, or supplementary figure notes are involved.
8. Draft or revise. Produce ready-to-paste text whose claims stay within the supplied design and evidence. Do not upgrade statistical association into mechanism or causality. Report precision as numbers (interval, n), not as a caveat sentence; each reporting statement (unit and n, P-value policy, multiplicity, blinding, exclusions) appears once, in Statistical analysis. For descriptive designs without inferential tests, use the descriptive variant in references/statistical-reporting.md.
9. Run final QA. Use references/reviewer-checklist.md before final delivery for severity labels, unresolved author questions, and reviewer-facing risk.

Output format

Unless the user asks for another format, return:

Statistics review scope
- Input reviewed:
- Boundary / missing materials:
- Study design readout:
- Independent unit and replication readout:

Major statistical issues
- [P0/P1/P2] Issue:
  Evidence from supplied text:
  Why it matters:
  Fix:

Ready-to-paste revision
[Rewritten Statistical analysis / Results / figure legend text]

AUTHOR_INPUT_NEEDED
- [short factual questions only]

Reviewer-risk note
- What a statistical reviewer may still challenge:

The issue list and the reviewer-risk note are for the author. Neither goes into the manuscript; the manuscript receives only the ready-to-paste revision, written without audit or defensive voice (see anti-defensive-writing).

For a clean drafting request with enough information, skip the long issue list and return:

Draft Statistical analysis
[ready-to-paste text]

Reporting notes
- n definition:
- tests/models:
- multiple comparisons:
- software/version:
- unresolved fields:

Red lines

• Do not invent p values, sample sizes, degrees of freedom, confidence intervals, software versions, correction methods, preregistration, exclusion rules, or power calculations.
• Do not recommend a statistical test as final when the unit of analysis or design is unclear.
• Do not accept n = number of cells/images/measurements as independent replication without checking the experimental hierarchy.
• Do not use “significant” as a synonym for important, large, causal, or biologically meaningful.
• Do not hide non-significant or weak results by rewriting them into stronger claims.
• Do not give medical, regulatory, or clinical-trial statistical advice beyond reporting checks unless the user provides the relevant protocol and asks for bounded manuscript wording.

Related files

File	Open when
references/source-basis.md	You need the source hierarchy or want to justify why the skill emphasizes transparency, reproducibility, and design reporting
references/statistical-reporting.md	You are drafting or auditing Statistical analysis, Methods, Results, or Supplementary Methods text
references/common-failure-modes.md	You see nested measurements, many comparisons, interaction claims, correlation/regression, outliers, tiny samples, or overstrong p-value language
references/figure-statistics.md	You are checking figure legends, panel statistics, error bars, stars, box/violin plots, source-data notes, or graphical reporting
references/reviewer-checklist.md	You are finalizing an audit or preparing a reviewer-facing risk summary

Source hierarchy

Use sources in this order:

1. User-supplied manuscript, data, protocol, statistical analysis plan, reviewer comments, and journal instructions.
2. Nature Portfolio reporting standards and reporting-summary requirements.
3. Nature Methods / Nature Portfolio statistics guidance summarized in references/source-basis.md.
4. Study-type reporting guidelines where relevant, for example CONSORT, STROBE, PRISMA, ARRIVE, or field-specific community standards.
5. Conservative statistical reporting practice.

If the supplied material is insufficient for a defensible statistical recommendation, ask for the missing design facts or provide a bounded wording option rather than guessing.

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Provenance: github.com/Yuan1z0825/nature-skills; original skill nature-statistics, license Apache-2.0. Imported into the local catalog; body preserved, only cross-references adapted.