Social Science Statistics Blog

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1 December 2006

Babel of Statistics

As requirement for my doctoral program I am required to take a basic epidemiology class this semester. It's been interesting to see how the basic analytics in epi are the same as in say, econometrics, but how much the language and preferences differ across the fields.

One striking difference is the preference for confidence intervals rather than coefficients and standard errors. Epidemiologists don't like p-values for all the same reason that economists dislike them without additional information. But epidemiologists seem to be in love with confidence intervals. Obviously it's a handy statistic but to me it seems to generate a misleading emphasis on the popular 5 percent level. It just pre-empts any thinking about the process of getting that interval. But most epi or medical publication reports not much else.

On the other hand maybe other social sciences could benefit from what epidemiologists call "positive criteria for causality." Those include the existence of plausible (gasp!) mechanisms of cause-and-effect and dose-response relations (dose of exposure is related to level of disease). Other fields often overly rely only on the strength of association and it would be a good idea to think about other positive criteria more seriously.

Other items are pure lingo. For example, epidemiologists seem to call misclassification what economists call measurement error. But at any rate the differences in terminologies and preferences are surprising. When did the academic tribes separate? Also accepted techniques from one field often seem like innovation in another. Why is there not more communication between the fields? It seems like all could benefit from a wider discussion and application, and it's an easy way to publish so the incentives are right too.

Posted by Sebastian Bauhoff at December 1, 2006 1:34 PM