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Adjustment of p values for multiple hypotheses: why, when and how
  1. Stian Lydersen
  1. Regional Centre for Child and Youth Mental Health and Child Welfare, Norwegian University of Science and Technology, Trondheim, Norway
  1. Correspondence to Professor Stian Lydersen, Regional Centre for Child and Youth Mental Health and Child Welfare, Norwegian University of Science and Technology, Trondheim, Trøndelag, Norway; stian.lydersen{at}ntnu.no

Abstract

It is quite common to investigate multiple hypotheses in a single study. For example, a researcher may want to investigate the effect on several outcome variables or at different time points, compare more than two groups or undertake separate analyses for subgroups. This increases the probability of type I errors. Different procedures for multiplicity adjustment are available to control the probability of type I errors. In the present article, we describe some methods for multiplicity adjustment, along with recommendations.

  • Epidemiology
  • Economics
  • Machine Learning

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Footnotes

  • Handling editor Josef S Smolen

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

  • Provenance and peer review Not commissioned; externally peer reviewed.