Process / pipelinestatistical-magnitude

Effect Size

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.

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Sources

  1. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 0-8058-0283-5
  2. Cumming, G. (2012). Understanding the New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis. Routledge. ISBN: 0-415-87968-8
  3. Lakens, D. (2013). Calculating and Reporting Effect Sizes to Facilitate Cumulative Science: A Practical Primer for t-Tests and ANOVAs. Frontiers in Psychology, 4, 863. DOI: 10.3389/fpsyg.2013.00863

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Referenced by

ScholarGateEffect Size (Effect Size: Quantifying the Magnitude of Research Findings). Retrieved 2026-06-04 from https://scholargate.app/en/research-statistics/effect-size