In:
Psychonomic Bulletin & Review, Springer Science and Business Media LLC, Vol. 30, No. 2 ( 2023-04), p. 516-533
Abstract:
A tradition that goes back to Sir Karl R. Popper assesses the value of a statistical test primarily by its severity : was there an honest and stringent attempt to prove the tested hypothesis wrong? For “error statisticians” such as Mayo (1996, 2018), and frequentists more generally, severity is a key virtue in hypothesis tests. Conversely, failure to incorporate severity into statistical inference, as 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.
Type of Medium:
Online Resource
ISSN:
1069-9384
,
1531-5320
DOI:
10.3758/s13423-022-02069-1
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2023
detail.hit.zdb_id:
2031311-1
SSG:
5,2
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