(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
Richard Morey writes:
I suspect that like me, many people didn’t get a whole lot of detail about Neyman’s objections to the significance test in their statistical education besides “Neyman thought power is important”. Given the recent debate about significance testing, I have gone back to Neyman’s papers and tried to summarize, for the modern user of statistics, his main objections to the significance test, including the nice demonstration from his 1952 book that without consideration of an alternative, the you can match the distribution of all of your statistics *under the null* and get whatever you answer you like. I thought it might be of interest for your readers.
“The frequentist case against the significance test” is in two parts:
Part 1 (epistemology)
Part 2 (statistics).
I haven’t read this in detail but I’m sympathetic to the general point. I think that frequentist analysis (that is, figuring out the average properties of statistical methods, averaging over some model of the data and underlying parameters) can be valuable, but I also think that the classical framework of hypothesis testing and confidence intervals doesn’t really work—I think these ideas represent too crude a way to jump into inference and decision making.
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