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Int J Epidemiol. 2011 Jun;40(3):780-5. doi: 10.1093/ije/dyr041. Epub 2011 Mar 30.

The Simpson's paradox unraveled.

Author information

1
Department of Epidemiology, Harvard School of Public Health, Harvard-MIT Division of Health Sciences and Technology, Boston, MA 02115, USA. miguel_hernan@post.havard.edu

Abstract

BACKGROUND:

In a famous article, Simpson described a hypothetical data example that led to apparently paradoxical results.

METHODS:

We make the causal structure of Simpson's example explicit.

RESULTS:

We show how the paradox disappears when the statistical analysis is appropriately guided by subject-matter knowledge. We also review previous explanations of Simpson's paradox that attributed it to two distinct phenomena: confounding and non-collapsibility.

CONCLUSION:

Analytical errors may occur when the problem is stripped of its causal context and analyzed merely in statistical terms.

PMID:
21454324
PMCID:
PMC3147074
DOI:
10.1093/ije/dyr041
[Indexed for MEDLINE]
Free PMC Article

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