Bigshot statistician keeps publishing papers with errors; is there anything we can do to get him to stop???

August 10, 2017
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(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)

OK, here’s a paper with a true theorem but then some false corollaries.

First the theorem:

The above is actually ok. It’s all true.

But then a few pages later comes the false statement:

This is just wrong, for two reasons. First, the relevant reference distribution is discrete uniform, not continuous uniform, so the normal CDF thing is at best just an approximation. Second, with Markov chain simulation, the draws theta_i^(l) are dependent, so for any finite L, the distribution of q_i won’t even be discrete uniform.

The theorem is correct because it’s in the limit as L approaches infinity, but the later statement (which I guess if true would be a corollary, although it’s not labeled as such) is false.

The error wasn’t noticed in the original paper because the method happened to work out on the examples. But it’s still a false statement, even if it happened to be true in a few particular cases.

Who are these stupid-ass statisticians who keep inflicting their errors on us??? False theorems, garbled data analysis, going beyond scientific argument and counterargument to imply that the entire field is inept and misguided, methodological terrorism . . . where will it all stop? Something should be done.

P.S. For those of you who care about the actual problem of statistical validation of algorithms and software which this is all about: One way to fix the above error is by approximating to a discrete uniform distribution on a binned space, with the number of bins set to something like the effective sample size of the simulations. We’re on it. And thanks to Sean, Kailas, and others for revealing this problem.

The post Bigshot statistician keeps publishing papers with errors; is there anything we can do to get him to stop??? appeared first on Statistical Modeling, Causal Inference, and Social Science.



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