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| 응답 동료 심사 의견: 수정 서신 및 원고 수정× | 통계 보고 표준: 분석의 투명한 보고× | |
|---|---|---|
| 분야 | 학술 글쓰기 | 학술 글쓰기 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도 | 2005 | 2005 |
| 창시자≠ | Journal editors and publishing community; formalized by Clydesdale et al. and ICMJE | Statistical and methodological literature; emphasized by Cumming (2013), ICMJE, and replication crisis discussions |
| 유형 | Guideline | Guideline |
| 원전≠ | Clydesdale, G. J., Seymour, K. J., & Toy, M. S. (2013). How to write a response to reviewers. British Journal of Ophthalmology, 97(1), 1–2. link ↗ | Cumming, G. (2013). The new statistics: Why and how. Psychological Science, 25(1), 7–29. DOI ↗ |
| 별칭 | revision letter, response to reviewers, rebuttal letter | reporting statistics, statistical transparency, effect size reporting |
| 관련 | 4 | 4 |
| 요약≠ | A response to reviewers (or 'revision letter') is a formal document that authors submit alongside a revised manuscript, addressing each reviewer comment point-by-point. The response letter shows the editor and reviewers that you have carefully considered their feedback, explained changes made in light of their suggestions, and justified any points of disagreement. A thoughtful, respectful response to reviewers significantly increases the likelihood of acceptance; a dismissive or defensive response can lead to rejection despite good science. The response letter is not an argument but a demonstration of engagement, transparency, and scientific integrity. | 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. |
| ScholarGate데이터셋 ↗ |
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