方法对比
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| 图表报告：数据可视化标准× | 统计报告标准：分析的透明报告× | |
|---|---|---|
| 领域 | 学术写作 | 学术写作 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1983 | 2005 |
| 提出者≠ | Tufte (visual communication theory), ICMJE standards, APA style guide | Statistical and methodological literature; emphasized by Cumming (2013), ICMJE, and replication crisis discussions |
| 类型 | Guideline | Guideline |
| 开创性文献≠ | American Psychological Association (2020). Publication Manual of the American Psychological Association (7th ed.). Washington, DC: American Psychological Association. ISBN: 978-1-4338-3216-1 | Cumming, G. (2013). The new statistics: Why and how. Psychological Science, 25(1), 7–29. DOI ↗ |
| 别名 | data visualization, table design, figure captions | reporting statistics, statistical transparency, effect size reporting |
| 相关 | 4 | 4 |
| 摘要≠ | Tables and figures are the primary means of presenting research data in scientific manuscripts. A well-designed table or figure enables readers to grasp complex data patterns instantly; a poorly designed one obscures findings or misleads. The ICMJE Recommendations and APA Publication Manual establish standards for table and figure formatting, captions, legends, and referencing. Tables are best used for precise numerical values and comparisons across rows and columns; figures (graphs, plots, images) are better for illustrating trends, relationships, or distributions. Both must be self-contained (understandable without consulting the text) and referenced clearly in the manuscript. | Transparent reporting of statistical results—including effect sizes, confidence intervals, p-values, and assumptions—is essential for scientific integrity and reproducibility. Many published studies report p-values in isolation without effect sizes or confidence intervals, making it impossible for readers to assess the magnitude of findings. Statistical reporting standards, emphasized by Cumming (2013), the American Statistical Association, and the ICMJE, require effect sizes, confidence intervals, and discussion of uncertainty. This enables readers to judge whether findings are practically significant (not just statistically significant) and to compare effect sizes across studies in meta-analyses. Poor statistical reporting wastes research and prevents proper synthesis of evidence. |
| ScholarGate数据集 ↗ |
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