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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.

-- 
Prof. Ilan Talmud, Ph.D.
Head, Economic Sociology, Department of Sociology and Anthropology,
University of Haifa
Phones: 972-4-8240992 (office direct)
972-4-8240995 / 8249505 (secretaries)
(cell) 972-522-220914 Fax: 972-4-8240819

http://soc.haifa.ac.il/~talmud/


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