手法を比較

	EQUATORネットワーク：健康研究の報告基準	統計報告基準：分析の透明性のある報告
分野	アカデミックライティング	アカデミックライティング
系統	Process / pipeline	Process / pipeline
提唱年	2006	2005
提唱者	EQUATOR Network (founded 2006); hosted by University of Oxford	Statistical and methodological literature; emphasized by Cumming (2013), ICMJE, and replication crisis discussions
種類	Standard	Guideline
原典	Moher, D., Altman, D. G., Schulz, K. F., Simera, I., & Wager, E. (2012). Guidelines for reporting health research: A user's manual. British Medical Journal, 345, e5997.	Cumming, G. (2013). The new statistics: Why and how. Psychological Science, 25(1), 7–29.
別名	EQUATOR, reporting guidelines, PRISMA, CONSORT	reporting statistics, statistical transparency, effect size reporting
関連	4	4
概要	EQUATOR (Enhancing QUAlity and Transparency Of health Research) is a global network that develops, endorses, and promotes reporting guidelines for health and life sciences research. Founded in 2006 and hosted by the University of Oxford, EQUATOR maintains a library of 500+ guidelines covering study designs (randomized trials, observational studies, systematic reviews, case reports, qualitative research, etc.). Major guidelines include CONSORT (randomized controlled trials), STROBE (observational studies), PRISMA (systematic reviews and meta-analyses), and CARE (case reports). These guidelines specify which items must be reported and how to report them, reducing inconsistency and enabling readers to assess study validity. Many journals now require adherence to relevant EQUATOR guidelines.	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.