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The researcher and the consultant: from testing to probability statements

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Abstract

In the first instalment of this series, Stang and Poole provided an overview of Fisher significance testing (ST), Neyman–Pearson null hypothesis testing (NHT), and their unfortunate and unintended offspring, null hypothesis significance testing. In addition to elucidating the distinction between the first two and the evolution of the third, the authors alluded to alternative models of statistical inference; namely, Bayesian statistics. Bayesian inference has experienced a revival in recent decades, with many researchers advocating for its use as both a complement and an alternative to NHT and ST. This article will continue in the direction of the first instalment, providing practicing researchers with an introduction to Bayesian inference. Our work will draw on the examples and discussion of the previous dialogue.

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Authors and Affiliations

  1. Department of Environmental and Occupational Health, Drexel University School of Public Health, 3215 Market St., Philadelphia, PA, 19104, USA

    Ghassan B. Hamra

  2. Center of Clinical Epidemiology, Institute of Medical Informatics, Biometry and Epidemiology, University Hospital Essen, Essen, Germany

    Andreas Stang

  3. Department of Epidemiology, School of Public Health, Boston University, Boston, MA, USA

    Andreas Stang

  4. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA

    Charles Poole

Authors
  1. Ghassan B. Hamra
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  2. Andreas Stang
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  3. Charles Poole
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Correspondence to Ghassan B. Hamra.

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Hamra, G.B., Stang, A. & Poole, C. The researcher and the consultant: from testing to probability statements. Eur J Epidemiol 30, 1003–1008 (2015). https://doi.org/10.1007/s10654-015-0054-1

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  • DOI: https://doi.org/10.1007/s10654-015-0054-1

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