Simpson's Paradox, Lord's Paradox, and Suppression Effects are the same phenomenon – the reversal paradox

Yu-Kang Tu¹^,2 , David Gunnell³ and Mark S Gilthorpe¹

¹Biostatistics Unit, Centre for Epidemiology & Biostatistics, University of Leeds, 30/32 Hyde Terrace, Leeds, LS2 9JT, UK

²Leeds Dental Institute, University of Leeds, Clarendon Road, Leeds, LS2 9LU, UK

³Department of Social Medicine, University of Bristol, Bristol, BS8 2PR, UK

author email corresponding author email

Emerging Themes in Epidemiology 2008, 5:2doi:10.1186/1742-7622-5-2


Published:	22 January 2008

Abstract

This article discusses three statistical paradoxes that pervade epidemiological research: Simpson's paradox, Lord's paradox, and suppression. These paradoxes have important implications for the interpretation of evidence from observational studies. This article uses hypothetical scenarios to illustrate how the three paradoxes are different manifestations of one phenomenon – the reversal paradox – depending on whether the outcome and explanatory variables are categorical, continuous or a combination of both; this renders the issues and remedies for any one to be similar for all three. Although the three statistical paradoxes occur in different types of variables, they share the same characteristic: the association between two variables can be reversed, diminished, or enhanced when another variable is statistically controlled for. Understanding the concepts and theory behind these paradoxes provides insights into some controversial or contradictory research findings. These paradoxes show that prior knowledge and underlying causal theory play an important role in the statistical modelling of epidemiological data, where incorrect use of statistical models might produce consistent, replicable, yet erroneous results.