Failure of Freakonomics
Andrew Gelman (Columbia) and Kaiser Fung (NYU) have an interesting article in the Jan/Feb 2012 issue of American Scientist that is worth a read. They review the popular Freakonomics franchise of Steven Levitt and Stephen Dubner. Freakonomics and SuperFreakonomics have set the standard for the popular statistics/economics genre, from which Gelman and Fung have both benefited. (click on their names to see their books)
However, Gelman and Fung have identified a "tendency in the Freakonomics body of work to present speculative or even erroneous claims with an air of certainty." Overall, I'm not so worried about the small errors they outline, but the reasons for the errors are concerning and the solutions are important ones to consider in any scientific discipline, including healthcare epidemiology.
One major problem they identify is Levitt and Dubner's reliance on linear informal social networks. For example, in the original Freakonomics, the network was "Levitt did the research, Dubner trusted Levitt, the Times trusted Dubner." However, as time pressures built and the need for more unique stories increased in SuperFreakonomics the network devolved into "Levitt trusts brilliant stars such as Myhrvold or Oster, Dubner trusts Levitt, and we the readers trust the Freakonomics brand."
The solution offered was that they should "leave friendship at the door." I think this is something all scientific disciplines could benefit from. It is clear that editorial boards, grant review committees and annual meeting planning committees are all at risk from reliance on a "linear" closed social network (in the past called "old boys' network"). They suggest that building more "non-linearity" into their research and evaluation would protect the process from what I might call a "friendship" bias. Excellent advice, perhaps difficult to put into practice, but worth the effort.
link: American Scientist Jan/Feb 2012
However, Gelman and Fung have identified a "tendency in the Freakonomics body of work to present speculative or even erroneous claims with an air of certainty." Overall, I'm not so worried about the small errors they outline, but the reasons for the errors are concerning and the solutions are important ones to consider in any scientific discipline, including healthcare epidemiology.One major problem they identify is Levitt and Dubner's reliance on linear informal social networks. For example, in the original Freakonomics, the network was "Levitt did the research, Dubner trusted Levitt, the Times trusted Dubner." However, as time pressures built and the need for more unique stories increased in SuperFreakonomics the network devolved into "Levitt trusts brilliant stars such as Myhrvold or Oster, Dubner trusts Levitt, and we the readers trust the Freakonomics brand."
The solution offered was that they should "leave friendship at the door." I think this is something all scientific disciplines could benefit from. It is clear that editorial boards, grant review committees and annual meeting planning committees are all at risk from reliance on a "linear" closed social network (in the past called "old boys' network"). They suggest that building more "non-linearity" into their research and evaluation would protect the process from what I might call a "friendship" bias. Excellent advice, perhaps difficult to put into practice, but worth the effort.
link: American Scientist Jan/Feb 2012
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