Literature DB >> 24233192

The researcher and the consultant: a dialogue on null hypothesis significance testing.

Andreas Stang, Charles Poole.   

Abstract

Since its introduction, null hypothesis significance testing (NHST) has caused much debate. Many publications on common misunderstandings have appeared. Despite the many cautions, NHST remains one of the most prevalent, misused and abused statistical procedures in the biomedical literature. This article is directed at practicing researchers with limited statistical background who are driven by subject matter questions and have empirical data to be analyzed. We use a dialogue as in ancient Greek literature for didactic purposes. We illustrate several, though only a few, irritations that can come up when a researcher with minimal statistical background but a good sense of what she wants her study to do, and of what she wants to do with her study, asks for consultation by a statistician. We provide insights into the meaning of several concepts including null and alternative hypothesis, one- and two-sided null hypotheses, statistical models, test statistic, rejection and acceptance regions, type I and II error, p value, and the frequentist' concept of endless study repetitions.

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Year:  2013        PMID: 24233192     DOI: 10.1007/s10654-013-9861-4

Source DB:  PubMed          Journal:  Eur J Epidemiol        ISSN: 0393-2990            Impact factor:   8.082


  6 in total

1.  Nonsignificance plus high power does not imply support for the null over the alternative.

Authors:  Sander Greenland
Journal:  Ann Epidemiol       Date:  2012-03-03       Impact factor: 3.797

Review 2.  Translating statistical findings into plain English.

Authors:  Stuart J Pocock; James H Ware
Journal:  Lancet       Date:  2009-04-15       Impact factor: 79.321

Review 3.  A dirty dozen: twelve p-value misconceptions.

Authors:  Steven Goodman
Journal:  Semin Hematol       Date:  2008-07       Impact factor: 3.851

4.  Null misinterpretation in statistical testing and its impact on health risk assessment.

Authors:  Sander Greenland
Journal:  Prev Med       Date:  2011-08-17       Impact factor: 4.018

5.  The role of model selection in causal inference from nonexperimental data.

Authors:  J M Robins; S Greenland
Journal:  Am J Epidemiol       Date:  1986-03       Impact factor: 4.897

6.  p values, hypothesis tests, and likelihood: implications for epidemiology of a neglected historical debate.

Authors:  S N Goodman
Journal:  Am J Epidemiol       Date:  1993-03-01       Impact factor: 4.897

  6 in total
  2 in total

1.  The Rotterdam Study: 2016 objectives and design update.

Authors:  Albert Hofman; Guy G O Brusselle; Sarwa Darwish Murad; Cornelia M van Duijn; Oscar H Franco; André Goedegebure; M Arfan Ikram; Caroline C W Klaver; Tamar E C Nijsten; Robin P Peeters; Bruno H Ch Stricker; Henning W Tiemeier; André G Uitterlinden; Meike W Vernooij
Journal:  Eur J Epidemiol       Date:  2015-09-19       Impact factor: 8.082

2.  The researcher and the consultant: from testing to probability statements.

Authors:  Ghassan B Hamra; Andreas Stang; Charles Poole
Journal:  Eur J Epidemiol       Date:  2015-06-25       Impact factor: 8.082

  2 in total

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