(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
We have no fireworks-related posts for July 4th but at least we have an item that’s appropriate for the summer weather. It comes from Daniel Lakeland, who writes:
Recently in one of your blog posts (“priors I don’t believe”) there was a discussion in which I was advocating the use of dimensional analysis and dimensionless groups to normalize every model, including statistical regression models. People wanted an example that made sense in social sciences, but since I don’t really have any social science examples to hand, I couldn’t provide one. However, I do have an interesting example problem that I just blogged, although it’s a physics problem, perhaps it illustrates the techniques well enough that one of your readers could run with it on a social sciences problem, or alternatively perhaps one of your readers would be interested in collaborating with me to develop a social science example.
Lakeland’s post starts with:
I’ve been working on my front-crawl swim stroke as an effort to improve my fitness.
But it doesn’t take too long to get to this:
and this:
So you should enjoy it. This indeed is the sort of analysis we don’t see enough of in statistics, I think.
P.S. Regarding priors, I wanted also to feature this comment from Chris on that earlier post:
This non-technical description of Bayes’ rule bugged me: “Final opinion on headline = (initial gut feeling) * (study support for headline)”. I think I’d have written something like “educated guess” rather than “gut feeling”. Not all gut feelings are of equal merit. One can have gut feelings about technical matters where they have some experience and are reasonably well informed. (I’d call that an educated guess.) Conversely, one can have gut feelings re matters where they are thoroughly uninformed.
I agree. One problem with the whole “subjective Bayes” slogan is the elevation of subjectivity into a principle, which is all too close to an anything-goes view which is contrary to many of the goals of science.
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