Here’s a link to the PDF.
Briggs, WM, HT Nguyen, D Trafimow, 2019. The Replacement for Hypothesis Testing. In Structural Changes and Their Econometric Modeling, Springer, V Kreinovich, S Sriboonchitta (eds.), pp 3–17. DOI 978-3-030-04263-9_1
Everything in the paper, by Messrs Briggs, Nguyen, and Trafimow, is peer-reviewed. And is therefore correct in all it says, and must be believed by everybody.
Here is the Abstract:
Classical hypothesis testing, whether with p-values or Bayes factors, leads to over-certainty, and produces the false idea that causes have been identified via statistical methods. The limitations and abuses of in particular p-values are so well known and by now so egregious, that a new method is badly in need. We propose returning to an old idea, making direct predictions by models of observables, assessing the value of evidence by the change in predictive ability, and then verifying the predictions against reality. The latter step is badly in need of implementation.
Those who have been following the free online statistics class in predictive methods will not find much new. The paper is succinct and can be passed on easily, though. So send it to your friends and enemies and let them marvel at its magnificence.
Bonus hint! There’s a newer, better, more astonishing peer-reviewed paper that will be posted this week. I suggest readers build up a buzz in its anticipation. Local launch parties would not be out of place. Since this will be a breakfast affair (for readers in the States), coffees can be Irish, and mimosas are a must. Subject? Here’s the hint: The death of p-values.