title: The researcher and the consultant: from testing to probability statements.
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.
authors.affiliation: Department of Environmental and Occupational Health, Drexel University School of Public Health, 3215 Market St., Philadelphia, PA, 19104, USA. gbh28@drexel.edu.
authors.forename: Ghassan B
authors.initials: GB
authors.lastname: Hamra
authors.affiliation: Center of Clinical Epidemiology, Institute of Medical Informatics, Biometry and Epidemiology, University Hospital Essen, Essen, Germany.|Department of Epidemiology, School of Public Health, Boston University, Boston, MA, USA.
authors.forename: Andreas
authors.initials: A
authors.lastname: Stang
authors.affiliation: Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.
authors.forename: Charles
authors.initials: C
authors.lastname: Poole
mesh_terms: D001499:Bayes Theorem; D003253:Consultants; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D011336:Probability; D012107:Research Design; D012108:Research Personnel
keywords: Bayesian inference; Confidence intervals; Probability statements; Significance testing