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Lyons, Russell (2011) "The Spread of Evidence-Poor Medicine via Flawed Social-Network Analysis," Statistics, Politics, and Policy: Vol. 2: Iss. 1, Article 2.
DOI: 10.2202/2151-7509.1024
Available at: http://www.bepress.com/spp/vol2/iss1/2Abstract

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

... summarizing the major problems with C&F’s studies:
1. The data are not available to others.
2. The unavailable data are sparse for friendships.
3. The models used to analyze the sparse data contradict the data and the conclusions.
4. The method used to estimate the dubious models does not apply.
5. The statistical significance tests from the questionable estimates do not show
the proposed differences.
6. The wrongly proposed differences do not distinguish among homophily, environment,
and induction.
7. Associations at a distance are better explained by homophily than by induction.
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Prof. Ilan Talmud, Ph.D.
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University of Haifa
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