Literature DB >> 15101426

In support of null hypothesis significance testing.

Michael Mogie1.   

Abstract

Many criticisms have been levelled at null hypothesis significance testing (NHST). It is argued here that although there is reason to doubt that data subjected only to NHST have been subjected to sufficient analysis, the search for clear answers to well-formulated questions derived from substantive hypotheses is well served by NHST. To reliably draw inferences from data, however, NHST may need to be complemented by additional methods of analysis, such as the use of confidence intervals and of estimates of the degree of association between independent and dependent variables. It is argued that these should be seen as complements of, rather than as substitutes for, NHST since they do not directly test the strength of evidence against a null hypothesis.

Mesh:

Year:  2004        PMID: 15101426      PMCID: PMC1810001          DOI: 10.1098/rsbl.2003.0105

Source DB:  PubMed          Journal:  Proc Biol Sci        ISSN: 0962-8452            Impact factor:   5.349


  4 in total

1.  Toward evidence-based medical statistics. 1: The P value fallacy.

Authors:  S N Goodman
Journal:  Ann Intern Med       Date:  1999-06-15       Impact factor: 25.391

Review 2.  Statistical inference by confidence intervals: issues of interpretation and utilization.

Authors:  J Sim; N Reid
Journal:  Phys Ther       Date:  1999-02

3.  Low P-values or narrow confidence intervals: which are more durable?

Authors:  C Poole
Journal:  Epidemiology       Date:  2001-05       Impact factor: 4.822

4.  The biological relevance of testing for perfect symmetry

Authors: 
Journal:  Anim Behav       Date:  1997-08       Impact factor: 2.844

  4 in total
  1 in total

1.  The Value of the P Value.

Authors:  Dinesh Vyas; Archana Balakrishnan; Arpita Vyas
Journal:  Am J Robot Surg       Date:  2015-12
  1 in total

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