Comparing Bayesian Statistics and Frequentist Statistics in Serious Games Research

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Abstract

This article presents three empirical studies on the effectiveness of serious games for learning and motivation, while it compares the results arising from Frequentist (classical) Statistics with those from Bayesian Statistics. For a long time it has been technically impracticable to apply Bayesian Statistics and benefit from its conceptual superiority, but the emergence of automated sampling algorithms and user-friendly tools has radically simplified its usage. The three studies include two within-subjects designs and one between-subjects design. Unpaired t-tests, mixed factorial ANOVAs and multiple linear regression are used for the analyses. Overall, the games are found to have clear positive effects on learning and motivation, be it that the results from Bayesian Statistics are more strict and more informative, and possess several conceptual advantages. Accordingly, the paper calls for more emphasis on Bayesian Statistics in serious games research and beyond, as to reduce the present domination by the Frequentist Paradigm.
Original languageEnglish
Pages (from-to)27-44
Number of pages18
JournalInternational Journal of Serious Games
Volume8
Issue number1
DOIs
Publication statusPublished - 9 Mar 2021

Keywords

  • Hypothesis testing
  • methodology
  • statistics
  • Bayesian statistics
  • motivation
  • learning
  • games
  • empirical research
  • misconceptions
  • data
  • testing
  • paradigm

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