My review of this interesting paper.

I just posted a revision to the manuscript, dated July 26th 2019, that incorporates some (but not all) of the comments from @JesseClifton, @HarryCrane, and @RichardGill.  These are very interesting questions raised and I just don't think I have anything new to add at this time, beyond what I wrote in my response letter on March 25th 2019.  Someday, maybe I'll understand things better and can add something meaningul, or maybe a follow-up paper is needed.  But thanks again for the helpful feedback, I really appreciate it.

I think that the most important principle of statistical inference is Lucien LeCam's Principle 0: never trust any principles 100% (or something like that). We have to remain able to be surprised and to completely rethink our models. Any standard philosophical framework for statistical inference fails because of principle 0. And Principle 0 is responsible for major miscarriages of justice, scientific scandals, and everything... We need to bring personal moral responsibility back as a basic principle of statistical inference.

More technically, any of the existing frameworks is a "model" and though many models are useful, none of them are actually "true". The question is whether or not they are adequate for purpose. The role of statistics in science is an important role in a many party game. Bayes theory asks: what should I believe? Hypothesis testing puts us in a two person game. Not very interesting except as a very, very rough approximation. I am pretty sure that it is impossible to come up with a compelling multi-party framework. The situation is already bad enough in the already formalised context of a court-room. There are always many more than two parties even if legal theory sometimes likes to pretend there is.

Thanks for the feedback.  My response to Clifton's 02/13/2019 comments and Crane's 03/12/2019 comments is in the attached PDF file.

See attached for comments.

(Attached PDF of comments)