เปรียบเทียบวิธี

	ขนาดอิทธิพล	ช่วงความเชื่อมั่น
สาขาวิชา	สถิติการวิจัย	สถิติการวิจัย
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	1988	1937
ผู้ริเริ่ม	Jacob Cohen	Jerzy Neyman
ประเภท	Concept	Concept
แหล่งต้นตำรับ	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 0-8058-0283-5	Neyman, J. (1937). Outline of a Theory of Statistical Estimation Based on the Classical Theory of Probability. Philosophical Transactions of the Royal Society, 236, 333–380.
ชื่อเรียกอื่น	ES, Cohen's d, standardized effect, practical significance	CI, 95% CI, credible interval, interval estimate
ที่เกี่ยวข้อง	4	4
สรุป	Effect size quantifies the magnitude of a research finding independent of sample size. While a p-value tells you whether a result is statistically significant, an effect size tells you how big the result is. Jacob Cohen formalized effect size measurement in behavioral sciences (1988), establishing standard benchmarks (small = 0.2, medium = 0.5, large = 0.8 for Cohen's d). Effect sizes are essential for meta-analysis, power analysis, and communicating the practical importance of research findings.	A confidence interval (CI) is a range of values, calculated from sample data, that likely contains the true population parameter. Introduced by Jerzy Neyman in 1937, it provides an interval estimate rather than a single point estimate, incorporating both the observed value and the uncertainty around it. The standard 95% confidence interval is a robust, intuitive alternative to p-values for communicating research results.