Fisher, Neyman-Pearson or NHST? A Tutorial for Teaching Data Testing
  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. 
Jose D. Perezgonzalez
stat.OT math.ST stat.TH