Сравнение методов

	Структура IMRaD: Введение, Методы, Результаты и Обсуждение	Statistical Reporting Standards
Область	Академическое письмо	Академическое письмо
Семейство	Process / pipeline	Process / pipeline
Год появления	1970	2005
Автор метода	International scientific publishing community (adopted widely by 1970s)	Statistical and methodological literature; emphasized by Cumming (2013), ICMJE, and replication crisis discussions
Тип	Guideline	Guideline
Основополагающий источник	International Committee of Medical Journal Editors (2023). Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals.	Cumming, G. (2013). The new statistics: Why and how. Psychological Science, 25(1), 7–29.
Другие названия	IMRaD, IMRAD, scientific manuscript structure	reporting statistics, statistical transparency, effect size reporting
Связанные	5	4
Сводка	IMRaD is the standard organizational framework for scientific manuscripts in biomedical and natural sciences research. It separates reporting into four sequential sections—Introduction (why the research was conducted), Methods (how it was done), Results (what was found), and Discussion (what the findings mean)—enabling readers to understand, evaluate, and reproduce the work. Adopted as best practice by the International Committee of Medical Journal Editors (ICMJE) since the 1970s, IMRaD structure is now mandated or strongly recommended by most peer-reviewed journals.	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.