This paper is from Session 4C: Rank-based inference, association measures and nonparametric statistics

which comes under Topic 4: Statistics education at the post-secondary level

Should we still teach rank-based distribution-free procedures?

Presenter

• E Jacquelin Dietz (Meredith College, Raleigh, United States)

Abstract

For decades, I have enjoyed teaching rank-based distribution-free inference procedures for two distinct reasons. First, I have believed these are useful data analysis methods that should be part of any applied statistician’s repertoire of statistical methods. Second, I have found rank-based tests ideal for teaching hypothesis testing – many students report that they never really understood sampling distributions and p-values until they studied rank-based tests. Recently, many instructors have begun teaching inference in introductory courses using bootstrapping and randomization tests in place of traditional normal theory methods. New software has made it feasible to apply randomization methods to the original observations. Is there now less motivation to rank data? Can we teach the fundamental concepts of hypothesis testing just as well using randomization methods on the original observations? Are rank procedures still important methods of data analysis that we should be teaching to our students?