P – VALUE, A TRUE TEST OF STATISTICAL SIGNIFICANCE? A CAUTIONARY NOTE

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Abstract

While it’s not the intention of the founders of significance testing and hypothesis testing to have the two ideas intertwined as if they are complementary, the inconvenient marriage of the two practices into one coherent, convenient, incontrovertible and misinterpreted practice has dotted our standard statistics textbooks and medical journals. This paper examine factors contributing to this practice, traced the historical evolution of the Fisherian and Neyman-Pearsonian schools of hypothesis testing, exposed the fallacies and the uncommon ground and common grounds approach to the problem. Finally, it offers recommendations on what is to be done to remedy the situation.

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Most cited references 27

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

Steven N Goodman (1999)

An important problem exists in the interpretation of modern medical research data: Biological understanding and previous research play little formal role in the interpretation of quantitative results. This phenomenon is manifest in the discussion sections of research articles and ultimately can affect the reliability of conclusions. The standard statistical approach has created this situation by promoting the illusion that conclusions can be produced with certain "error rates," without consideration of information from outside the experiment. This statistical approach, the key components of which are P values and hypothesis tests, is widely perceived as a mathematically coherent approach to inference. There is little appreciation in the medical community that the methodology is an amalgam of incompatible elements, whose utility for scientific inference has been the subject of intense debate among statisticians for almost 70 years. This article introduces some of the key elements of that debate and traces the appeal and adverse impact of this methodology to the P value fallacy, the mistaken idea that a single number can capture both the long-run outcomes of an experiment and the evidential meaning of a single result. This argument is made as a prelude to the suggestion that another measure of evidence should be used--the Bayes factor, which properly separates issues of long-run behavior from evidential strength and allows the integration of background knowledge with statistical findings.

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The Empire of Chance

Gerd Gigerenzer, Zeno G. Swijtink, Theodore M Porter … (1989)

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Effect sizes and p values: what should be reported and what should be replicated?

R. González, J Harris, D Guthrie … (1996)

Despite publication of many well-argued critiques of null hypothesis testing (NHT), behavioral science researchers continue to rely heavily on this set of practices. Although we agree with most critics' catalogs of NHT's flaws, this article also takes the unusual stance of identifying virtues that may explain why NHT continues to be so extensively used. These virtues include providing results in the form of a dichotomous (yes/no) hypothesis evaluation and providing an index (p value) that has a justifiable mapping onto confidence in repeatability of a null hypothesis rejection. The most-criticized flaws of NHT can be avoided when the importance of a hypothesis, rather than the p value of its test, is used to determine that a finding is worthy of report, and when p approximately equal to .05 is treated as insufficient basis for confidence in the replicability of an isolated non-null finding. Together with many recent critics of NHT, we also urge reporting of important hypothesis tests in enough descriptive detail to permit secondary uses such as meta-analysis.

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Author and article information

Journal

Journal ID (nlm-ta): Ann Ib Postgrad Med

Journal ID (iso-abbrev): Ann Ib Postgrad Med

Journal ID (publisher-id): AIPM

Title: Annals of Ibadan Postgraduate Medicine

Publisher: Association of Resident Doctors (ARD), University College Hospital, Ibadan

ISSN (Print): 1597-1627

Publication date (Print): June 2008

Volume: 6

Issue: 1

Pages: 21-26

Affiliations

Dept. of Community Medicine, Ahmadu Bello University, Zaria, Nigeria.

Author notes

All Correspondence to: Dr. Tukur Dahiru MBBS, FMCPH, Dip HSM (Israel) DEPT OF COMMUNITY MEDICINE AHMADU BELLO UNIVERSITY, ZARIA, NIGERIA. E-mail: tukurdahiru@ 123456yahoo.com

Article

Publisher ID: AIPM-6-21

DOI: 10.4314/aipm.v6i1.64038

PMC ID: 4111019

PubMed ID: 25161440

SO-VID: 4124e202-e5c2-4b38-b793-0c30564fc4ed

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This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

P – VALUE, A TRUE TEST OF STATISTICAL SIGNIFICANCE? A CAUTIONARY NOTE

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Abstract

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Core Readings in Statistical Mediation Analysis

Most cited references 27

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

The Empire of Chance

Effect sizes and p values: what should be reported and what should be replicated?

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