by From Lars Syll At least since the time of Keynes’s famous critique of Tinbergen’s econometric methods, those of us in the social science community who have been impolite enough to dare to question the preferred methods and models applied in quantitative research in general and economics more specifically, are as a rule met with disapproval. Although people seem to get very agitated and upset by the critique — just read the commentaries on this blog if you don’t believe me — defenders of received theory always say that the critique is ‘nothing new’, that they have always been ‘well aware’ of the problems, and so on, and so on. So, for the benefit of all mindless practitioners of economics — who don’t want to be disturbed in their doings — eminent mathematical statistician David
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from Lars Syll
At least since the time of Keynes’s famous critique of Tinbergen’s econometric methods, those of us in the social science community who have been impolite enough to dare to question the preferred methods and models applied in quantitative research in general and economics more specifically, are as a rule met with disapproval. Although people seem to get very agitated and upset by the critique — just read the commentaries on this blog if you don’t believe me — defenders of received theory always say that the critique is ‘nothing new’, that they have always been ‘well aware’ of the problems, and so on, and so on.
So, for the benefit of all mindless practitioners of economics — who don’t want to be disturbed in their doings — eminent mathematical statistician David Freedman has put together a very practical list of vacuous responses to criticism that can be freely used to save your peace of mind:
We know all that. Nothing is perfect … The assumptions are reasonable. The assumptions don’t matter. The assumptions are conservative. You can’t prove the assumptions are wrong. The biases will cancel. We can model the biases. We’re only doing what everybody else does. Now we use more sophisticated techniques. If we don’t do it, someone else will. What would you do? The decision-maker has to be better off with us than without us … The models aren’t totally useless. You have to do the best you can with the data. You have to make assumptions in order to make progress. You have to give the models the benefit of the doubt. Where’s the harm?