---

name: review-data-analysis locale: wenyan-ultra source_locale: en source_commit: 82c77053 fence_basis_commit: 82c77053 translator: "Julius Brussee homage — caveman" translation_date: "2026-04-26" description: > Review a data analysis for quality, correctness, and reproducibility. Covers data quality assessment, assumption checking, model validation, data leakage detection, and reproducibility verification. Use when reviewing a colleague's analysis before publication, validating an ML pipeline before production deployment, auditing a report for regulatory or business decision-making, or performing a second-analyst review in a regulated environment. license: MIT allowed-tools: Read Grep Glob Bash WebFetch metadata: author: Philipp Thoss version: "1.0" domain: review complexity: advanced language: multi tags: data-quality, model-validation, leakage, reproducibility, statistics, review

審數析

評數析管之正、韌、可復。

用

• 審同事析簿或本於發前
• 驗 ML 管於生產發前
• 審析報為規或業決
• 估析支所述結乎
• 行第二析審於規環

入

• 必：析碼（本、簿、管定）
• 必：析出（果、表、圖、模指）
• 可：原數或數典
• 可：析計或協（預登或臨）
• 可：標群與決脈

行

一：估數質

評析前審入數：

## Data Quality Assessment

### Completeness
- [ ] Missing data quantified (% by column and by row)
- [ ] Missing data mechanism considered (MCAR, MAR, MNAR)
- [ ] Imputation method appropriate (if used) or complete-case analysis justified

### Consistency
- [ ] Data types match expectations (dates are dates, numbers are numbers)
- [ ] Value ranges are plausible (no negative ages, future dates in historical data)
- [ ] Categorical variables have expected levels (no misspellings, consistent coding)
- [ ] Units are consistent across records

### Uniqueness
- [ ] Duplicate records identified and handled
- [ ] Primary keys are unique where expected
- [ ] Join operations produce expected row counts (no fan-out or drop)

### Timeliness
- [ ] Data vintage appropriate for the analysis question
- [ ] Temporal coverage matches the study period
- [ ] No look-ahead bias in time-series data

### Provenance
- [ ] Data source documented
- [ ] Extraction date/version recorded
- [ ] Any transformations between source and analysis input documented

得：數質患書、含其對果或影。 敗：數不可審→自碼估質（何察與化施）。

二：察設

各統法或所用模：

Method	Key Assumptions	How to Check
Linear regression	Linearity, independence, normality of residuals, homoscedasticity	Residual plots, Q-Q plot, Durbin-Watson, Breusch-Pagan
Logistic regression	Independence, no multicollinearity, linear logit	VIF, Box-Tidwell, residual diagnostics
t-test	Independence, normality (or large n), equal variance	Shapiro-Wilk, Levene's test, visual inspection
ANOVA	Independence, normality, homogeneity of variance	Shapiro-Wilk per group, Levene's test
Chi-squared	Independence, expected frequency ≥ 5	Expected frequency table
Random forest	Sufficient training data, feature relevance	OOB error, feature importance, learning curves
Neural network	Sufficient data, appropriate architecture, no data leakage	Validation curves, overfitting checks

## Assumption Check Results
| Analysis Step | Method | Assumption | Checked? | Result |
|---------------|--------|------------|----------|--------|
| Primary model | Linear regression | Normality of residuals | Yes | Q-Q plot shows mild deviation — acceptable for n>100 |
| Primary model | Linear regression | Homoscedasticity | No | Not checked — recommend adding Breusch-Pagan test |

得：各統法之設明察或認。 敗：設違→察作者處之乎（韌法、化、敏析）。

三：察數漏

數漏即訓集外訊影模、致過樂效：

常漏模：

• 標漏：直編標變之徵（如「treatment_outcome」用以測「treatment_success」）
• 時漏：未來訊用以測過（自測時不可得之數計徵）
• 訓測污：預處（縮、補、徵選）於分前合全集
• 群漏：相關察（同患、同器）跨訓測分
• 徵工漏：聚於整集計、非於訓折內

## Leakage Assessment
| Check | Status | Evidence |
|-------|--------|----------|
| Target leakage | Clear | No features derived from target |
| Temporal leakage | CONCERN | Feature X uses 30-day forward average |
| Train-test contamination | Clear | StandardScaler fit on train only |
| Group leakage | CONCERN | Patient IDs not used for stratified split |

得：諸常漏模察含潔/憂態。 敗：漏發見→重行無漏徵估其影（若可）或標令析者察。

四：驗模效

為測模：

• 應問之正指（非僅準——考精、召、F1、AUC、RMSE、MAE）
• 交驗或留策述且應
• 訓對測/驗集效較（過擬察）
• 基較（樸模、隨機、前法）
• 效指之信區或標誤
• 相子群效估（公、邊例）

為推/釋模：

• 模合統報（R²、AIC、BIC、deviance）
• 系正釋（向、量、義）
• 多共線估（VIF < 5–10）
• 影察識（Cook 距、leverage）
• 多規測時模較

得：模驗應用例（測對推）。 敗：測集效疑近訓效→標潛漏。

五：估可復

## Reproducibility Checklist
| Item | Status | Notes |
|------|--------|-------|
| Code runs without errors | [Yes/No] | Tested on [environment description] |
| Random seeds set | [Yes/No] | Line [N] in [file] |
| Dependencies documented | [Yes/No] | requirements.txt / renv.lock present |
| Data loading reproducible | [Yes/No] | Path is [relative/absolute/URL] |
| Results match reported values | [Yes/No] | Verified: Table 1 ✓, Figure 2 ✗ (minor discrepancy) |
| Environment documented | [Yes/No] | Python 3.11 / R 4.5.0 specified |

得：可復重行析驗（或數無時自碼估）。 敗：果不確復→定異於浮容內乎抑示問。

六：書審

## Data Analysis Review

### Overall Assessment
[1-2 sentences: Is the analysis sound? Does it support the conclusions?]

### Data Quality
[Summary of data quality findings, impact on results]

### Methodological Concerns
1. **[Title]**: [Description, location in code/report, suggestion]
2. ...

### Strengths
1. [What was done well]
2. ...

### Reproducibility
[Tier assessment: Gold/Silver/Bronze/Opaque with justification]

### Recommendations
- [ ] [Specific action items for the analyst]

得：審供可動饋含特碼處引。 敗：時限→質與漏察優於格患。

驗

• 數質跨完、恆、唯、時、源估
• 各所用法統設察
• 數漏系估
• 模效以應指與基驗
• 可復估（碼行、果合）
• 饋特、引碼行或報段
• 調建設且協

忌

• 唯審碼：析計與結與實同要
• 忽數質：壞數上精模生信誤答
• 由複設正：95% 準隨機林或有數漏；簡 t 或為正法
• 不行碼：可則行碼以驗可復。讀碼不足
• 失林為樹：勿迷碼格患而失基析誤

參

• review-research — 廣研法與稿審
• validate-statistical-output — 雙程驗法
• generate-statistical-tables — 發備統表
• review-software-architecture — 碼構與設審