Three Views of Statistical Tests

Bruno Lecoutre and Jacques Poitevineau
Additional contact information
Bruno Lecoutre: Universite de Rouen, CNRS
Jacques Poitevineau: Université Pierre et Marie Curie, CNRS

Chapter Chapter 3 in The Significance Test Controversy Revisited, 2022, pp 19-32 from Springer

Abstract: Abstract The rationale behind the three main views of statistical tests is briefly reviewed. Current practice is based on the Fisher “test of significance” and the Neyman-Pearson “hypothesis test.” Jeffreys’ approach is a Bayesian alternative based on the use of “objective” prior probabilities of hypotheses. The main similarities and dissimilarities of these three approaches will be examined from a methodological point of view: what is the aim of statistical inference, and what is the relevance of significance tests in experimental research? The dangers inherent in an uncritical application of the Neyman-Pearson approach will also be stressed.

Keywords: Automatic decision vs estimation; Deductive vs inductive reasoning; Fisher’s test of significance; Jeffreys’ Bayesian significance test; Learning from data and experience; Neyman-Pearson’s hypothesis test (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-662-65705-8_3

Ordering information: This item can be ordered from
http://www.springer.com/9783662657058

DOI: 10.1007/978-3-662-65705-8_3

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().