方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 效应量× | 置信区间× | |
|---|---|---|
| 领域 | 研究统计学 | 研究统计学 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1988 | 1937 |
| 提出者≠ | Jacob Cohen | Jerzy Neyman |
| 类型 | Concept | Concept |
| 开创性文献≠ | 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. DOI ↗ |
| 别名 | ES, Cohen's d, standardized effect, practical significance | CI, 95% CI, credible interval, interval estimate |
| 相关 | 4 | 4 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
|
|