Multiple perspectives on inference for two simple statistical scenarios

Noah N.N. van Dongen (Corresponding author), Johnny B. van Doorn, Quentin F. Gronau, Don van Ravenzwaaij, Rink Hoekstra, Matthias N. Haucke, Daniel Lakens, Christian Hennig, Richard D. Morey, Saskia Homer, Andrew Gelman, Jan Sprenger, Eric Jan Wagenmakers (Corresponding author)

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When data analysts operate within different statistical frameworks (e.g., frequentist versus Bayesian, emphasis on estimation versus emphasis on testing), how does this impact the qualitative conclusions that are drawn for real data? To study this question empirically we selected from the literature two simple scenarios—involving a comparison of two proportions and a Pearson correlation—and asked four teams of statisticians to provide a concise analysis and a qualitative interpretation of the outcome. The results showed considerable overall agreement; nevertheless, this agreement did not appear to diminish the intensity of the subsequent debate over which statistical framework is more appropriate to address the questions at hand.

Original languageEnglish
Pages (from-to)328-339
Number of pages12
JournalAmerican Statistician
Issue numbersup1
Publication statusPublished - 29 Mar 2019



  • Frequentist or Bayesian
  • Multilab analysis
  • Statistical paradigms
  • Testing or estimation

Cite this

van Dongen, N. N. N., van Doorn, J. B., Gronau, Q. F., van Ravenzwaaij, D., Hoekstra, R., Haucke, M. N., ... Wagenmakers, E. J. (2019). Multiple perspectives on inference for two simple statistical scenarios. American Statistician, 73(sup1), 328-339.