Abstract: A tradition that goes back to Karl R. Popper assesses the value of a
statistical test primarily by its severity: was it a honest and
stringent attempt to prove the theory wrong? For “error statisticians”
such as Deborah Mayo (1996, 2018), and frequentists more generally,
severity is a key virtue in hypothesis tests. Conversely, failure to
incorporate severity into statistical inference, as it allegedly happens
in Bayesian inference, counts as a major methodological shortcoming. Our
paper pursues a double goal: First, we argue that the error-statistical
explication of severity has substantive drawbacks; specifically, the
neglect of research context and the specificity of the predictions of
the hypothesis. Second, we argue that severity matters for Bayesian
inference via the value of specific, risky predictions: severity boosts
the expected evidential value of a Bayesian hypothesis test. We
illustrate severity-based reasoning in Bayesian statistics by means of a
practical example and discuss its advantages and potential drawbacks.

The talk is based on a joint work with Noah van Dongen and Eric-Jan
Wagenmakers and will be forthcoming in /Psychonomic Bulletin & Review/.

Link: https://eu.bbcollab.com/guest/420e2e5688db46a6a13ebbcb615cf019