Statistical Reporting Standards
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.
원본 기록
방법의 원본 기록에서 그대로 복사된 인용입니다. 이로부터 수준별 검증이 추론되지 않습니다.
- Cumming, G. (2013). The new statistics: Why and how. Psychological Science, 25(1), 7–29. · DOI 10.1177/0956797613504966
- Fidler, F., Thomason, N., Cumming, G., Finch, S., & Leeman, J. (2005). Editors can lead researchers to confidence intervals, but can't make them think: Statistical reform lessons from medicine. Psychological Science, 15(2), 119–126. · DOI 10.1111/j.0963-7214.2004.01502008.x
- International Committee of Medical Journal Editors (2023). Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals. · URL
큐레이션된 주장
각각 자체 평가와 함께 증거 원장에 유지된 주장입니다.
원장에 주장 평가가 없는 경우 이 보기에서는 주장 평가를 만들지 않습니다.
관련 방법
방법 그래프에서 생성되었으며 기계가 제안한 관계로 표시됩니다 — 증거 주장이 추론되지 않습니다.