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A dirty dozen: twelve p-value misconceptions.

Steven Goodman1

  • 1Departments of Oncology, Epidemiology, and Biostatistics, Johns Hopkins Schools of Medicine and Public Health, Baltimore, MD, USA. Sgoodman@jhmi.edu

Seminars in Hematology
|June 28, 2008
PubMed
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Statistical P values are frequently misinterpreted in medical research. This commentary clarifies common P value errors and introduces Bayes

Area of Science:

  • Biostatistics
  • Medical Research Methodology
  • Statistical Inference

Background:

  • The P value is a ubiquitous measure of statistical evidence in medical research.
  • Its interpretation is complex and often leads to widespread misconstruction.
  • Misinterpretations have been documented extensively since the 1940s.

Purpose of the Study:

  • To review common misinterpretations of the P value.
  • To explain the reasons why these interpretations are incorrect.
  • To contrast the P value with the Bayes' factor as an evidential measure.

Main Methods:

  • Review of common P value misinterpretations.
  • Explanation of the inferential limitations of P values.
  • Comparison of P values with Bayes' factors.

Related Experiment Videos

Main Results:

  • Identified and explained a dozen common P value misinterpretations.
  • Detailed the consequences of these misunderstandings.
  • Highlighted the interpretability advantages of Bayes' factors over P values.

Conclusions:

  • Widespread P value misconceptions persist, leading to flawed statistical inference.
  • The P value lacks desirable evidential properties, unlike the Bayes' factor.
  • A critical consequence is the false belief in calculating error probability from single experiments without external context.