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Avoid Cohen's 'Small', 'Medium', and 'Large' for Power Analysis.

Joshua Correll¹, Christopher Mellinger¹, Gary H McClelland¹

¹Department of Psychology and Neuroscience, University of Colorado, Boulder, CO, USA.

Trends in Cognitive Sciences

|January 20, 2020

Summary

This summary is machine-generated.

Researchers should avoid Jacob Cohen's definitions for small, medium, and large effect sizes in power analysis. These arbitrary and inconsistent criteria can significantly skew sample size calculations, impacting research validity.

Keywords:

effect size research design research methods

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Area of Science:

Psychology
Statistics
Research Methodology

Background:

Accurate effect size estimation is crucial for statistical power analysis.
Jacob Cohen's conventions for small, medium, and large effect sizes are widely used.
The validity of power analysis depends on appropriate effect size specification.

Purpose of the Study:

To critically evaluate the utility of Jacob Cohen's effect size definitions in power analysis.
To highlight the arbitrary and inconsistent nature of Cohen's conventions.
To advocate for the avoidance of these definitions in research.

Main Methods:

Conceptual analysis of effect size definitions.
Comparison of Cohen's conventions across different statistical tests (e.g., t-tests, regression).
Examination of the impact of inconsistent definitions on sample size (n) estimation.

Main Results:

Cohen's effect size definitions are based on non-scientific criteria and lack empirical grounding.
These definitions vary significantly and illogically depending on the chosen statistical test.
Inconsistent application of Cohen's conventions can lead to incorrect sample size estimations, potentially doubling or halving the required 'n'.

Conclusions:

Jacob Cohen's widely used effect size definitions are problematic and should be avoided in power analyses.
Researchers must seek more scientifically grounded and consistent methods for specifying effect sizes.
Adopting alternative, context-specific effect size estimation strategies is essential for meaningful power analysis and robust research findings.