(via Decision Science News) Ouch! A new paper titled “The Spread of Evidence-Poor Medicine via Flawed Social-Network Analysis” (published here and available in manuscript format on arXiv) has come out arguing very strongly against the conclusions drawn by Christakis and Fowler in a series of papers where they put forward the idea that things like obesity and smoking can be transmitted through social networks; a kind of “social contagion.” I blogged about these ideas a while back after both Wired and the New York Times had published articles on them. The title (harsh!) and the abstract speaks for itself:
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.
Cosma Shalizi has co-authored another paper (available here) which makes a similar point in a much more, let’s say, polite way. My impression is that Shalizi is both sharp and trustworthy (I’ve learned a lot about statistics from his blog) so I’m inclined to think he is on to something.