“Theoretical statistics is the theory of applied statistics”: A scheduled conference on the topic

Ron Kenett writes:

We are planning a conference on 11/4 that might be of interest to your blog followers.

It is a hybrid format event on the foundations of applied statistics. Discussion inputs will be most welcome.

The registration link and other information are here.

I think that “11/4” refers to 11 Apr 2024; if not, I guess that someone will correct me in comments.

Kenett’s paper on the theory of applied statistics reminds me of my dictum that theoretical statistics is the theory of applied statistics. For example of how this principle can inform both theory and applications, see this comment at the linked post:

There are lots of ways of summarizing a statistical analysis, and it’s good to have a sense of how the assumptions map to the conclusions. My problem with the paper [on early-childhood intervention; see pages 17-18 of this paper here for background] was that they presented a point estimate of an effect size magnitude (42% earnings improvement from early childhood intervention) which, if viewed classically, is positively biased (type M error) and, if viewed Bayesianly, corresponds to a substantively implausible prior distribution in which an effect of 84% is as probable as an effect of 0%.

If we want to look at the problem classically, I think researchers who use biased estimates should (i) recognize the bias, and (ii) attempt to adjust for it. Adjusting for the bias requires some assumption about plausible effect sizes; that’s just the way things are, so make the assumption and be clear about what assumption your making.

If we want to look at the problem Bayesianly, I think researchers should have to justify all aspects of their model, including their prior distribution. Sometimes the justification is along the lines of, “This part of the model doesn’t materially impact the final conclusions so we can go sloppy here,” which can be fine, but it doesn’t apply in a case like this where the flat prior is really driving the headline estimate.

The point is that theoretical concepts such as “unbiased estimation” or “prior distribution” don’t exist in a vacuum; they are theoretically relevant to the extent that they connect to applied practice.

I assume that such issues will be discussed at the conference.