Type M Error Might Explain Weisburd’s Paradox

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 We...

Full description

Saved in:  
Bibliographic Details
Main Author: Gelman, Andrew (Author)
Contributors: Aaltonen, Mikko ; Skardhamar, Torbjørn
Format: Electronic Article
Language:English
Published: 2020
In: Journal of quantitative criminology
Year: 2020, Volume: 36, Issue: 2, Pages: 295-304
Online Access: Presumably Free Access
Volltext (Resolving-System)
Journals Online & Print:
Drawer...
Check availability: HBZ Gateway
Keywords:
Publication bias
Statistical power
Type M error
Weisburd paradox
Description
Summary: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.
ISSN:1573-7799
DOI:10.1007/s10940-017-9374-5