Porovnat metody

	Struktura IMRaD: Úvod, Metody, Výsledky a Diskuze	Standardy pro statistické vykazování: transparentní vykazování analýz
Obor	Akademické psaní	Akademické psaní
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	1970	2005
Tvůrce	International scientific publishing community (adopted widely by 1970s)	Statistical and methodological literature; emphasized by Cumming (2013), ICMJE, and replication crisis discussions
Typ	Guideline	Guideline
Původní zdroj	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.
Další názvy	IMRaD, IMRAD, scientific manuscript structure	reporting statistics, statistical transparency, effect size reporting
Příbuzné	5	4
Shrnutí	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.