Wednesday 9 February 2022
- Title: Statistics in the Service of Science: Don't let the Tail Wag the Dog
- Speaker: Henrik Singmann, University College London
Thanks to the wide availability of 'default Bayes factors' that can be applied without the need to specify problem-specific prior distributions, Bayes factors are becoming increasingly popular. Default Bayes factors achieve this by formulating the prior distribution on a standardised effect size scale. Whereas the idea of default Bayes factors is that the application is easy, this normalisation can have unintended consequences. For example, van Doorn et al. (in press, CBaB) showed that Rouder’s default Bayes factor is not invariant to the level of data aggregation in a mixed model setting with repeated measures. This feature can allow researchers to strategically manipulate their Bayes factor results. We show that this is solely a feature of the default Bayes factor that relies on a normalised effect size. Neither the frequentist p-value nor a Bayesian approach relying on the un-normalised effect size (using either Bayes factor or other inferential approach) suffer from this problem. We conclude that in a mixed-model setting, in which usually multiple variance components are estimated, formulating standardised effect sizes measures is a non-trivial endeavour. In general, researchers should try to formulate their hypotheses on inherently meaningful units instead of relying on the deceiving convenience provided by standardised effect sizes.
Wednesday 23 February 2022
- Speaker: Fritz Breithaupt, Indiana University Bloomington
- Title: To be announced