방법 비교

	과학적 글쓰기의 명료성: 정확한 학술적 소통을 위한 원칙	통계 보고 표준: 분석의 투명한 보고
분야	학술 글쓰기	학술 글쓰기
계열	Process / pipeline	Process / pipeline
기원 연도	1959	2005
창시자	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
유형	Guideline	Guideline
원전	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.
별칭	clarity in writing, scientific communication, technical writing	reporting statistics, statistical transparency, effect size reporting
관련	4	4
요약	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.