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How doctors conceptualise P values: A mixed methods study

Chun Wah Michael Tam1, Abeer Hasan Khan2, Andrew Knight3

  • 1BSc (Med), MBBS, MMH (GP), FRACGP, Staff Specialist, Academic Primary and Integrated Care Unit, South Western Sydney Local Health District and Ingham Institute of Applied Medical Research; Conjoint Senior Lecturer, School of Public Health and Community Medicine, UNSW Sydney, NSW. m.tam@unsw.edu.au

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This summary is machine-generated.

General practitioners frequently misunderstand P values, often incorrectly interpreting them as real-world probabilities. Enhanced statistics education is needed to address these pervasive misconceptions in clinical practice.

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Area of Science:

  • Medical Statistics
  • Clinical Research Interpretation

Background:

  • P values are frequently misinterpreted and misused by researchers and clinicians.
  • Understanding how general practitioners (GPs) conceptualize P values is crucial for accurate medical literature interpretation.

Purpose of the Study:

  • To investigate the conceptualization of P values among Australian and New Zealand GPs.
  • To assess GPs' interpretation ability and confidence regarding P values in a clinical context.

Main Methods:

  • A mixed-methods online questionnaire was administered to GPs in Australia and New Zealand.
  • The study included quantitative and qualitative questions assessing P value conceptualization and interpretation skills.
  • Data were collected in 2017 via a GP-specific Facebook group.

Main Results:

  • 247 GPs completed the questionnaire.
  • The most common misconception was viewing P values as direct 'real-world probabilities'.
  • No demographic factors, including research experience, correlated with improved P value interpretation; a confidence-ability gap was observed.

Conclusions:

  • P value misunderstanding is widespread among clinicians, driven by key misconceptions.
  • Statistics education for healthcare professionals must directly address prevalent misunderstandings of P values.
  • Targeted educational interventions are necessary to improve the accurate interpretation of statistical significance in medical research.