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Too much statistical power can lead to false conclusions: a response to ‘Unsuitability of the epidemiological approach to bicycle transportation injuries and traffic engineering problems’ by Kary
  1. Jake Olivier1,
  2. Scott R Walter2
  1. 1School of Mathematics and Statistics, University of New South Wales, Sydney, Australia
  2. 2Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, University of New South Wales, Sydney, Australia
  1. Correspondence to Dr Jake Olivier, School of Mathematics and Statistics, University of New South Wales, Sydney, NSW 2052, Australia; j.olivier{at}

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Injury Prevention recently published a commentary critical of epidemiological approaches to cycling safety.1 The author, Kary, suggests that our paper,2 which reanalysed Walker's3 study of motor vehicle overtaking distance for cyclists, made false claims about type I errors and confused statistical significance and clinical significance. Kary supports this critique, along with other points in the commentary, with a non-peer-reviewed response posted by him to the Journal's website.4

In our paper, we note that, increasing power when computing sample size leads to an increase in …

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  • Contributors JO: conceived the original idea and drafted the manuscript. SRW: contributed to the conceptual framework and assisted in revisions for content and style.

  • Competing interests None.

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