Statistics controversies from the perspective of industrial statistics

We’ve had lots of discussions here and elsewhere online about fundamental flaws in statistics culture: the whole p-value thing, statistics used for confirmation rather than falsification, corruption of the pizzagate variety, soft corruption in which statistics is used in the service of country-club-style backslapping, junk science routinely getting the imprimatur of the National Academy of Sciences and National Public Radio, etc etc etc.

Or, to step back and talk about the statistics community: the way that we, as a profession, always seem at war with ourselves: the Bayesians and the anti-Bayesians, still battling after all these centuries, leaving researchers in other fields unsure of what to do (except for the econometricians, who are all too sure of themselves).

We’ve had lots of discussion in this space by psychologists and economists, some political scientists, some philosophers, and lots of academic statisticians.

But we haven’t heard so much from statisticians working in industry.

With that in mind, Ron Kenett sends along this article on controversies in statistics and their relevance for statistical practice . Kenett frames much of this discussion in the form of checklists, which relates to our discussions here and here.

P.S. Given the title of this post, you may ask yourself, What is industrial statistics? I’m not quite sure. Maybe Kenett can give a good definition. To me, some characteristics of industrial statistics are:

– Designed experiments (rather than the use of existing data, as is common in academic social science).

– A focus on costs and benefits (rather than on confirmation, refutation, or adjudication of theories, as is common in academic work).

– Research focused on particular upcoming decisions (rather than driven by past work, as is common in academia).

I’m not arguing here that industrial statistics is better than, or more “real” than, academic statistics. I think there are real benefits to resolving puzzles and working on hard problems from the literature. My point is that “industrial statistics,” whatever it is, has a different feel from much of what we see in textbooks, journals, and blog posts.