  For many years, philosopher-of-statistics Deborah Mayo has been advocating
the concept of severe testing as a key part of hypothesis testing. Her recent
book, Statistical Inference as Severe Testing, is a comprehensive exposition of
her arguments in the context of a historical study of many threads of
statistical inference, both frequentist and Bayesian. Her foundational point of
view is called error statistics, emphasizing frequentist evaluation of the
errors called Type I and Type II in the Neyman-Pearson theory of frequentist
hypothesis testing. Since the field of elementary particle physics (also known
as high energy physics) has strong traditions in frequentist inference, one
might expect that something like the severity concept was independently
developed in the field. Indeed, I find that, at least operationally
(numerically), we high-energy physicists have long interpreted data in ways
that map directly onto severity. Whether or not we subscribe to Mayo's
philosophical interpretations of severity is a more complicated story that I do
not address here.

Other Statistics;High Energy Physics - Experiment;Data Analysis, Statistics and Probability