A Primer on the Understanding, Use, and Calculation of Confidence Intervals that are Based on Central and Noncentral Distributions
2001/08/01 by Geoff Cumming, Sue Finch · 515 citations
Computer Science · Decision Sciences · Mathematics · Medicine · Psychology · #Computer science #Confidence interval #Data Analysis with R #Econometrics #Epistemology #Mathematics #Medicine #Meta-analysis #Meta-analysis and systematic reviews #Microsoft excel #Psychology #Simple (philosophy) #Statistical Methods in Clinical Trials #Statistical analysis #Statistical hypothesis testing #Statistical power #Statistical software #Statistics
paper · doi:10.1177/0013164401614002
published in Educational and Psychological Measurement 61(4), 532-574 (SAGE Publishing)
openalex publication_date 2001/08/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/03
Abstract
Reform of statistical practice in the social and behavioral sciences requires wider use of confidence intervals (CIs), effect size measures, and meta-analysis. The authors discuss four reasons for promoting use of CIs: They (a) are readily interpretable, (b) are linked to familiar statistical significance tests, (c) can encourage meta-analytic thinking, and (d) give information about precision. The authors discuss calculation of CIs for a basic standardized effect size measure, Cohen’s δ (also known as Cohen’s d), and contrast these with the familiar CIs for original score means. CIs for δ require use of noncentral t distributions, which the authors apply also to statistical power and simple meta-analysis of standardized effect sizes. They provide the ESCI graphical software, which runs under Microsoft Excel, to illustrate the discussion. Wider use of CIs for δ and other effect size measures should help promote highly desirable reform of statistical practice in the social sciences.
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