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An introduction to power and sample size estimation
  1. S R Jones1,
  2. S Carley2,
  3. M Harrison3
  1. 1North Manchester Hospital, Manchester, UK
  2. 2Royal Bolton Hospital, Bolton, UK
  3. 3North Staffordshire Hospital, UK
  1. Correspondence to:
    Dr S R Jones, Emergency Department, Manchester Royal Infirmary, Oxford Road, Manchester M13 9WL, UK;
    steve.r.jones{at}bigfoot.com

Abstract

The importance of power and sample size estimation for study design and analysis.

  • research design
  • sample size
  • statistics

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Footnotes

  • Correction notice Following recent feedback from a reader, the authors have corrected this article. The original version of this paper stated that: “Strictly speaking, “power” refers to the number of patients required to avoid a type II error in a comparative study.” However, the formal definition of “power” is that it is the probability of avoiding a type II error (rejecting the alternative hypothesis when it is true), rather than a reference to the number of patients. Power is, however, related to sample size as power increases as the number of patients in the study increases. This statement has therefore been corrected to: “Strictly speaking, “power” refers to the probability of avoiding a type II error in a comparative study.

Linked Articles

  • Correction
    BMJ Publishing Group Ltd and the British Association for Accident & Emergency Medicine
  • Correction
    BMJ Publishing Group Ltd and the British Association for Accident & Emergency Medicine