nature-statistics

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. For each result claim, record the comparison/model, test family, assumptions, correction strategy, effect estimate, uncertainty, and exact p-value policy.
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 conservative, ready-to-paste text. Keep claims within the supplied design and evidence. Do not upgrade statistical association into mechanism or causality.
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:

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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nature-statistics 技能

English

nature-statistics 用于审查、改写或起草 Nature / 高影响力期刊投稿中的统计报告文本，重点是透明、可复核、与实验设计一致，而不只是 p 值是否显著。

适合用它做什么

• 检查 Statistical analysis / Methods 中统计方法是否完整。
• 改写 Results、figure legends 和 source data 中的统计说明。
• 区分 biological replicates、technical replicates、重复测量、视野/细胞/子样本和独立实验单位。
• 识别伪重复、嵌套数据、多重比较、交互解释、相关性过度解释和显著性滥用。
• 根据审稿人统计意见生成保守回应或修改建议。

典型请求

• “帮我检查这段 Statistical analysis 是否符合 Nature 风格。”
• “图注里的 error bars、n 和 p 值怎么写更清楚？”
• “审稿人说统计不充分，帮我列修改方案和回复草稿。”

你需要提供

• 样本量、重复层级、实验单位、分组、检验方法和多重比较方式。
• 图表、图注、Results 文本、统计输出或审稿意见。
• 哪些事实已确认，哪些需要保守标注。

产出

• 统计报告审查：主要问题、风险等级和需要作者确认的信息。
• 可粘贴的 Statistical analysis、Results 或 figure legend 改写。
• 审稿统计问题的逐点回应草稿。
• AUTHOR_INPUT_NEEDED 清单，用于标记缺失样本量、检验或设计事实。

边界

• 不会替作者编造样本量、重复数、p 值、效应量、模型假设或检验名称。
• 不直接替代统计师或完整再分析；如需重新建模，应要求原始数据和分析代码。
• 不能仅凭图像判断实验单位和独立重复，需要作者确认设计。

相关技能

• nature-figure：把统计信息落实到图件和 source data。
• nature-response：回应统计类审稿意见。
• nature-polishing：润色统计描述的英文表达。