Correlation is Not Causation: “Fat is Catching” Theory Exposed
Posted on January 5, 2009 Comments (1)
Jason M. Fletcher, Ph.D., assistant professor at the Yale School of Public Health in New Haven, Connecticut, along with Boston economist, Ethan Cohen-Cole, Ph.D., designed an ingenious study. They selected conditions that no one would seriously believe were spread by social networking and online friendships: height, headaches and acne. They then applied the same standard statistical methods used in Christakis and Fowler’s social networking research to “find” that acne, height and headaches have the same “social network effect.”
As they explained, patterns of association among people can lead to correlations in health conditions between friends that are not caused by direct social network effects at all.
There is a need for caution when attributing causality to correlations in health outcomes between friends using non-experimental data. Confounding is only one of many empirical challenges to estimating social network effects.
Excellent reminder of the risks of analyzing data for correlations. We continue to, far to often, fail to interpret data properly. Both authors of the study, received PhD’s from the University of Wisconsin-Madison which strengthens my belief that it is teaching students well (just kidding).
Also another example of the scientific inquiry process where scientists challenge the conclusions drawn by other scientists. It is a wonderful system, even if confusing and not the clean idea so many have of how science works.
Related: Correlation is Not Causation – Seeing Patterns Where None Exists – Statistics for Experimenters – 500 Year Floods – Playing Dice and Children’s Numeracy – The Illusion of Understanding – All Models Are Wrong But Some Are Useful – Data Doesn’t Lie But People Can Draw Faulty Conclusions from Data