Fisher, Neyman-Pearson or NHST? A Tutorial for Teaching Data Testing

TL;DR

This paper provides a tutorial on teaching data testing procedures, comparing Fisher's significance tests, Neyman-Pearson's acceptance tests, and the combined NHST approach, highlighting their differences and offering improvements.

Contribution

It offers a clear educational guide on hypothesis testing theories and suggests ways to improve NHST in teaching and practice.

Findings

Clarifies differences between Fisher, Neyman-Pearson, and NHST methods.

Provides compromise solutions to improve NHST teaching.

Highlights the importance of teaching these concepts early in research education.

Abstract

Despite frequent calls for the overhaul of null hypothesis significance testing (NHST), this controversial procedure remains ubiquitous in behavioral, social and biomedical teaching and research. Little change seems possible once the procedure becomes well ingrained in the minds and current practice of researchers; thus, the optimal opportunity for such change is at the time the procedure is taught, be this at undergraduate or at postgraduate levels. This paper presents a tutorial for the teaching of data testing procedures, often referred to as hypothesis testing theories. The first procedure introduced is the approach to data testing followed by Fisher (tests of significance); the second is the approach followed by Neyman and Pearson (tests of acceptance); the final procedure is the incongruent combination of the previous two theories into the current approach (NSHT). For those researchers sticking with the latter, two compromise solutions on how to improve NHST conclude the tutorial.