The spread of evidence-poor medicine via flawed social-network analysis
Open accessAbstract
The chronic widespread misuse of statistics is usually inadvertent, not intentional. We find cautionary examples in a series of recent papers by Christakis and Fowler that advance statistical arguments for the transmission via social networks of various personal characteristics, including obesity, smoking cessation, happiness, and loneliness. Those papers also assert that such influence extends to three degrees of separation in social networks. We shall show that these conclusions do not follow from Christakis and Fowler's statistical analyses. In fact, their studies even provide some evidence against the existence of such transmission. The errors that we expose arose, in part, because the assumptions behind the statistical procedures used were insufficiently examined, not only by the authors, but also by the reviewers. Our examples are instructive because the practitioners are highly reputed, their results have received enormous popular attention, and the journals that published their studies are among the most respected in the world. An educational bonus emerges from the difficulty we report in getting our critique published. We discuss the relevance of this episode to understanding statistical literacy and the role of scientific review, as well as to reforming statistics education.
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Network studies of peer influence and social contagion suffer from a stubborn challenge of disambiguating such effects from homophily among peers and within communities.
“network studies of peer influence and social contagion suffer from a stubborn challenge of disambiguating such effects from homophily among peers and within communities (Christakis and Fowler, 2007; Lyons, 2011; Shalizi and Thomas, 2011).”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Lyons, 2011
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O'Malley, A., Moen, E., Bynum, J., Austin, A. M., & Skinner, J. S. (2020) · Statistics in Medicine · doi:10.1002/sim.8466 · Cited by 14 · see citing works
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- Authors
- Lyons, R.
- Corresponding author
- Lyons, Russell
- ORCID
- Lyons, Russell
- Venue
- Statistics, Politics, and Policy
- Publisher
- Walter de Gruyter GmbH
- Published
- 2011-01-18
- Volume
- 2
- Issue
- 1
- ISSN
- 2151-7509 (ISSN-L)
- OpenAlex
- W2152314940
- Cited by
- 182
- References
- 25
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 3 · 2023: 3 · 2022: 6 · 2021: 7 · 2020: 7 · 2019: 10 · 2018: 22 · 2017: 14 · 2016: 13 · 2015: 19 · 2014: 13 · 2013: 32 · 2012: 16
- FWCI
- 20.49
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Statistics Politics and Policy (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistics Education and Methodologies
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- social network analysis, evidence-based medicine, smoking cessation, happiness, loneliness, statistical methodology critique, statistical literacy, peer review, statistics education, misuse of statistics
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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