Simple calculations seem to show that larger studies should have higher statistical power, but empirical meta-analyses of published work in criminology have found zero or weak correlations between sample size and estimated statistical power. This is “Weisburd’s paradox” and has been attributed by Weisburd et al. (in Crime Justice 17:337–379, 1993) to a difficulty in maintaining quality control as studies get larger, and attributed by Nelson et al. (in J Exp Criminol 11:141–163, 2015) to a negative correlation between sample sizes and the underlying sizes of the effects being measured. We argue against the necessity of both these explanations, instead suggesting that the apparent Weisburd paradox might be explainable as an artifact of systematic overestimation inherent in post-hoc power calculations, a bias that is large with small N.
We discuss Weisburd’s paradox in light of the concepts of type S and type M errors, and re-examine the publications used in previous studies of the so-called paradox.
We suggest that the apparent Weisburd paradox might be explainable as an artifact of systematic overestimation inherent in post-hoc power calculations, a bias that is large with small N.
Speaking more generally, we recommend abandoning the use of statistical power as a measure of the strength of a study, because implicit in the definition of power is the bad idea of statistical significance as a research goal.
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Our summary is at http://www.stat.columbia.edu/~gelman/documents/weisburd_table_of_studies.pdf.
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National Science Foundation (Grant No. SES-1534414), Institute of Education Sciences (Grant No. R305D140059-16), Office of Naval Research (Grant No. N00014-15-1-2541), Defense Advanced Research Projects Agency (Grant No. DARPA BAA-16-32)
We thank Justin Pickett and Gary Sweeten for suggesting this topic, several reviewers for helpful comments, and the U.S. National Science Foundation, Institute of Education Sciences, Office of Naval Research, and Defense Advanced Research Projects Agency for partial support of this work.
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Gelman, A., Skardhamar, T. & Aaltonen, M. Type M Error Might Explain Weisburd’s Paradox. J Quant Criminol (2017). https://doi.org/10.1007/s10940-017-9374-5
- Weisburd paradox
- Type M error
- Statistical power
- Publication bias