Porovnat metody

	Jasnost vědeckého psaní: Principy pro precizní akademickou komunikaci	Standardy pro statistické vykazování: transparentní vykazování analýz
Obor	Akademické psaní	Akademické psaní
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	1959	2005
Tvůrce	Scientific writing tradition; modern frameworks from Greenhalgh (1997), Strunk & White (2000), and writing educators	Statistical and methodological literature; emphasized by Cumming (2013), ICMJE, and replication crisis discussions
Typ	Guideline	Guideline
Původní zdroj	Strunk, W., Jr., & White, E. B. (2000). The Elements of Style (4th ed.). New York: Longman. ISBN: 978-0-205-30902-4	Cumming, G. (2013). The new statistics: Why and how. Psychological Science, 25(1), 7–29.
Další názvy	clarity in writing, scientific communication, technical writing	reporting statistics, statistical transparency, effect size reporting
Příbuzné	4	4
Shrnutí	Clear scientific writing enables readers to understand methodology, results, and implications without confusion. Clarity is not ornamental—it is essential to scientific integrity. Unclear writing obscures findings, enables misinterpretation, wastes readers' time, and reduces impact and citations. Scientific clarity requires active voice (when appropriate), conciseness (eliminating redundancy), precise word choice (correct terminology), logical organization, and transparent reasoning. These principles apply across disciplines and are supported by style guides (APA, Vancouver), writing textbooks, and journal editors' expectations. Clear writing also helps authors think more precisely; the act of writing clearly often reveals gaps or inconsistencies in logic.	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.