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Chinese

Nature Statistics Reporting Skill

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.
使用本技能可使手稿统计内容透明、可复现且表述恰当。本技能为报告与审查技能，除非用户提供数据并明确要求计算，否则不能替代统计学家重新分析原始数据。

Default stance

默认原则

  • 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.
  • 优先考虑设计透明度，而非装饰性统计语言。
  • 区分三个问题：测量了什么、分析的单位是什么、提出了何种推断主张。
  • 将独立实验单位作为默认的
    n
    ；不得默认将细胞、视野、重复读数、光谱、模型运行或技术重复视为独立的生物学或实验样本。
  • 相较于仅强调显著性的表述，更倾向于使用效应量、不确定区间、样本量及精确的检验定义。
  • 缺失信息标注为
    AUTHOR_INPUT_NEEDED
    ，不得自行编造样本量、检验方法、软件、校正方式、排除规则、随机化或盲法信息。
  • 若期刊特定要求、研究类型指南或领域标准与本技能冲突，遵循更具体的来源，并标注所使用的来源。

Accepted inputs

接受的输入

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.
本技能可接收以下内容：
  • 统计分析/方法小节
  • 包含检验统计量或p values的结果段落
  • 图版、图注、标题或源数据说明
  • 审稿人关于统计的意见
  • 中英文作者说明
  • 已报告比较的表格
  • 原始或汇总数据（仅当用户需要具体的重新分析或图统计检查时）
若输入内容不完整，进行有限范围的审核，并说明无法评估的部分。

Workflow

工作流程

  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.
  1. 任务分类：确定用户需要的是审核、重写、撰写、审稿意见回复支持、图统计对齐，还是基于数据的重新分析。
  2. 提取研究设计：识别分组、处理方式、时间点、终点、区组因素、重复测量、随机化、盲法、排除规则及缺失数据处理方式。
  3. 定义
    n
    与重复类型    ：区分独立实验单位、生物学重复、技术重复、重复测量、细胞/视野/子样本、模拟及合并观测值。
  4. 关联主张与分析：针对每个结果主张，记录比较/模型、检验类别、假设、校正策略、效应估计值、不确定性及精确p值原则。
  5. 检查常见失败模式：当文本涉及嵌套数据、多重比较、细胞水平测量、交互作用主张、相关性、回归、异常值、小样本或仅强调显著性的推理时，参考
    references/common-failure-modes.md
    。
  6. 检查报告完整性：使用
    references/statistical-reporting.md
    验证方法与结果部分是否提供了足够信息，以便读者和审稿人理解分析过程。
  7. 对齐图统计内容：当涉及图注、图版标签、星号、误差棒、箱线图、小提琴图、源数据或补充图说明时，参考
    references/figure-statistics.md
    。
  8. 撰写或修改：生成严谨、可直接粘贴的文本。确保主张符合提供的研究设计与证据。不得将统计关联升级为机制或因果关系。
  9. 最终质量检查：在最终交付前，使用
    references/reviewer-checklist.md
    进行严重程度标记、未解决的作者问题及面向审稿人的风险评估。

Output format

输出格式

Unless the user asks for another format, return:
text
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:
text
Draft Statistical analysis
[ready-to-paste text]

Reporting notes
- n definition:
- tests/models:
- multiple comparisons:
- software/version:
- unresolved fields:
除非用户要求其他格式，否则返回：
text
统计审查范围
- 已审核的输入内容：
- 边界/缺失材料：
- 研究设计解读：
- 独立单位与重复类型解读：

主要统计问题
- [P0/P1/P2] 问题：
  来自提交文本的证据：
  影响原因：
  修正方案：

可直接粘贴的修改内容
[重写后的统计分析/结果/图注文本]

AUTHOR_INPUT_NEEDED
- [仅简短事实性问题]

审稿人风险提示
- 统计审稿人仍可能质疑的内容：
对于信息充足的纯撰写请求，可跳过冗长的问题列表，返回：
text
统计分析撰写稿
[可直接粘贴的文本]

报告说明
- n的定义：
- 检验/模型：
- 多重比较：
- 软件/版本：
- 未明确字段：

Red lines

红线规则

  • 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.
  • 不得编造p values、样本量、自由度、confidence intervals、软件版本、校正方法、预注册信息、排除规则或功效计算结果。
  • 当分析单位或研究设计不明确时，不得将某一统计检验作为最终推荐。
  • 未经检查实验层级，不得将
    n = 细胞/图像/测量次数
    视为独立重复。
  • 不得将“significant”作为重要、显著（指效应大）、因果或生物学意义的同义词。
  • 不得通过重写隐藏非显著性或弱结果，使其看起来更强。
  • 除非用户提供相关方案并要求有限范围的手稿表述指导，否则不得提供超出报告检查范围的医学、监管或临床试验统计建议。

Related files

相关文件

FileOpen when
references/source-basis.mdYou need the source hierarchy or want to justify why the skill emphasizes transparency, reproducibility, and design reporting
references/statistical-reporting.mdYou are drafting or auditing Statistical analysis, Methods, Results, or Supplementary Methods text
references/common-failure-modes.mdYou see nested measurements, many comparisons, interaction claims, correlation/regression, outliers, tiny samples, or overstrong p-value language
references/figure-statistics.mdYou are checking figure legends, panel statistics, error bars, stars, box/violin plots, source-data notes, or graphical reporting
references/reviewer-checklist.mdYou are finalizing an audit or preparing a reviewer-facing risk summary
文件适用场景
references/source-basis.md需要来源层级说明，或需解释本技能为何强调透明度、可复现性及设计报告时
references/statistical-reporting.md撰写或审核统计分析、方法、结果或补充方法文本时
references/common-failure-modes.md遇到嵌套测量、多重比较、交互作用主张、相关性/回归、异常值、极小样本或过度强调p值的语言时
references/figure-statistics.md检查图注、图版统计、误差棒、星号、箱线/小提琴图、源数据说明或图形报告内容时
references/reviewer-checklist.md完成审核或准备面向审稿人的风险总结时

Source hierarchy

来源优先级

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.
按以下顺序使用来源：
  1. 用户提供的手稿、数据、方案、统计分析计划、审稿意见及期刊要求。
  2. 《自然》系列期刊报告标准及报告摘要要求。
  3. 总结于
    references/source-basis.md
    中的《自然方法》/《自然》系列期刊统计指南。
  4. 相关的研究类型报告指南，例如CONSORT、STROBE、PRISMA、ARRIVE或特定领域的社区标准。
  5. 严谨的统计报告实践。
若提供的材料不足以形成可靠的统计建议，需询问缺失的设计信息，或提供有限范围的表述选项，而非猜测。