The statistical recommendations of the American Psychological Association Publication Manual: Effect sizes, confidence intervals, and meta‐analysis

2011/10/13 by Geoff Cumming, Fiona Fidler, Pav Kalinowski +1 · 136 citations
Decision Sciences · Engineering · Mathematics · Medicine · Psychology · #Applied psychology #Association (psychology) #Computer science #Confidence interval #Econometrics #Engineering #Estimation #Interpretation (philosophy) #Mathematics #Medicine #Mental Health Research Topics #Meta-analysis #Meta-analysis and systematic reviews #Point (geometry) #Point estimation #Psychology #Statistical Methods in Clinical Trials #Statistics

paper · pdf · doi:10.1111/j.1742-9536.2011.00037.x

published in Australian Journal of Psychology 64(3), 138-146 (Wiley)

openalex publication_date 2011/10/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

Estimation based on effect sizes, confidence intervals, and meta‐analysis usually provides a more informative analysis of empirical results than does statistical significance testing, which has long been the conventional choice in psychology. The sixth edition of the American Psychological Association Publication Manual now recommends that psychologists should, wherever possible, use estimation and base their interpretation of research results on point and interval estimates. We outline the Manual's recommendations and suggest how they can be put into practice: adopt an estimation framework, starting with the formulation of research aims as ‘How much?’ or ‘To what extent?’ questions. Calculate from your data effect size estimates and confidence intervals to answer those questions, then interpret. Wherever appropriate, use meta‐analysis to integrate evidence over studies. The Manual's recommendations can assist psychologists improve they way they do their statistics and help build a more quantitative and cumulative discipline.

Citations

• A Primer on the Understanding, Use, and Calculation of Confidence Intervals that are Based on Central and Noncentral Distributions
• Error bars in experimental biology

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