Literature DB >> 36118788

Interpreting Results from Statistical Hypothesis Testing: Understanding the Appropriate P-value.

Eiki Tsushima1.   

Abstract

Clinical research based on epidemiological study designs requires a good understanding of statistical analysis. This paper discusses the common misconceptions of p-values so that researchers and readers of research papers will be able to properly present and understand the results of null hypothesis significance testing (NHST). The p-values calculated by NHST are categorized as three different types: "significant at p <0.05," "significant at p <0.01," or "not significant." If specified, they may be written as p = 0.124. The 95% confidence interval (CI) of the supplementary statistics is presented regardless of the p-value, and the range of the CI is observed and discussed to determine whether the results are clinically valid. The effect size (ES), which is a measure of the magnitude of the effect, is also referenced and discussed. However, the ES should not be overestimated. It is important to examine the actual descriptive statistics and consider them comprehensively as much as possible. A high detection power of 80% or more indicates that NHST with high accuracy was applied. However, even when it falls below 80%, it is important to consider the limitations of the study, because the results are not completely useless. ©2022 Japanese Society of Physical Therapy.

Entities:  

Keywords:  Confidence intervals; Effect size; Null hypothesis significance testing; P-value; Power

Year:  2022        PMID: 36118788      PMCID: PMC9437930          DOI: 10.1298/ptr.R0019

Source DB:  PubMed          Journal:  Phys Ther Res        ISSN: 2189-8448


  18 in total

Review 1.  Effect size, confidence interval and statistical significance: a practical guide for biologists.

Authors:  Shinichi Nakagawa; Innes C Cuthill
Journal:  Biol Rev Camb Philos Soc       Date:  2007-11

2.  Scientists rise up against statistical significance.

Authors:  Valentin Amrhein; Sander Greenland; Blake McShane
Journal:  Nature       Date:  2019-03       Impact factor: 49.962

3.  What is the Minimum Clinically Important Difference for the WOMAC Index After TKA?

Authors:  Nicholas D Clement; Michelle Bardgett; David Weir; James Holland; Craig Gerrand; David J Deehan
Journal:  Clin Orthop Relat Res       Date:  2018-10       Impact factor: 4.176

4.  Use of Confidence Intervals in Interpreting Nonstatistically Significant Results.

Authors:  Alexander T Hawkins; Lauren R Samuels
Journal:  JAMA       Date:  2021-11-23       Impact factor: 157.335

5.  Hypothesis testing, type I and type II errors.

Authors:  Amitav Banerjee; U B Chitnis; S L Jadhav; J S Bhawalkar; S Chaudhury
Journal:  Ind Psychiatry J       Date:  2009-07

6.  The reign of the p-value is over: what alternative analyses could we employ to fill the power vacuum?

Authors:  Lewis G Halsey
Journal:  Biol Lett       Date:  2019-05-31       Impact factor: 3.703

7.  Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations.

Authors:  Sander Greenland; Stephen J Senn; Kenneth J Rothman; John B Carlin; Charles Poole; Steven N Goodman; Douglas G Altman
Journal:  Eur J Epidemiol       Date:  2016-05-21       Impact factor: 8.082

Review 8.  Statistical Significance Versus Clinical Importance of Observed Effect Sizes: What Do P Values and Confidence Intervals Really Represent?

Authors:  Patrick Schober; Sebastiaan M Bossers; Lothar A Schwarte
Journal:  Anesth Analg       Date:  2018-03       Impact factor: 5.108

9.  Effect Size Guidelines, Sample Size Calculations, and Statistical Power in Gerontology.

Authors:  Christopher R Brydges
Journal:  Innov Aging       Date:  2019-09-04

10.  P-Value Demystified.

Authors:  Amrita Sil; Jayadev Betkerur; Nilay Kanti Das
Journal:  Indian Dermatol Online J       Date:  2019-11-01
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