P value and the theory of hypothesis testing: an explanation for new researchers.

Détails

ID Serval
serval:BIB_B9FAD4D239A1
Type
Article: article d'un périodique ou d'un magazine.
Sous-type
Synthèse (review): revue aussi complète que possible des connaissances sur un sujet, rédigée à partir de l'analyse exhaustive des travaux publiés.
Collection
Publications
Institution
Titre
P value and the theory of hypothesis testing: an explanation for new researchers.
Périodique
Clinical Orthopaedics and Related Research
Auteur⸱e⸱s
Biau David Jean, Jolles Brigitte M., Porcher Raphael
ISSN
1528-1132[electronic], 0009-921X[linking]
Statut éditorial
Publié
Date de publication
2010
Volume
468
Numéro
3
Pages
885-892
Langue
anglais
Résumé
In the 1920s, Ronald Fisher developed the theory behind the p value and Jerzy Neyman and Egon Pearson developed the theory of hypothesis testing. These distinct theories have provided researchers important quantitative tools to confirm or refute their hypotheses. The p value is the probability to obtain an effect equal to or more extreme than the one observed presuming the null hypothesis of no effect is true; it gives researchers a measure of the strength of evidence against the null hypothesis. As commonly used, investigators will select a threshold p value below which they will reject the null hypothesis. The theory of hypothesis testing allows researchers to reject a null hypothesis in favor of an alternative hypothesis of some effect. As commonly used, investigators choose Type I error (rejecting the null hypothesis when it is true) and Type II error (accepting the null hypothesis when it is false) levels and determine some critical region. If the test statistic falls into that critical region, the null hypothesis is rejected in favor of the alternative hypothesis. Despite similarities between the two, the p value and the theory of hypothesis testing are different theories that often are misunderstood and confused, leading researchers to improper conclusions. Perhaps the most common misconception is to consider the p value as the probability that the null hypothesis is true rather than the probability of obtaining the difference observed, or one that is more extreme, considering the null is true. Another concern is the risk that an important proportion of statistically significant results are falsely significant. Researchers should have a minimum understanding of these two theories so that they are better able to plan, conduct, interpret, and report scientific experiments.
Mots-clé
Arthroplasty, Replacement, Hip/rehabilitation, Data Interpretation, Statistical, Humans, Models, Statistical, Pain, Probability, Range of Motion, Articular, Reproducibility of Results, Research/methods, Research/standards, Severity of Illness Index, Statistics as Topic
Pubmed
Web of science
Création de la notice
09/03/2010 13:48
Dernière modification de la notice
20/08/2019 15:28
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