In:
Journal for General Philosophy of Science, Springer Science and Business Media LLC, Vol. 53, No. 4 ( 2022-12), p. 381-402
Abstract:
We analyse the issue of using prior information in frequentist statistical inference. For that purpose, we scrutinise different kinds of sampling designs in Jerzy Neyman’s theory to reveal a variety of ways to explicitly and objectively engage with prior information. Further, we turn to the debate on sampling paradigms (design-based vs. model-based approaches) to argue that Neyman’s theory supports an argument for the intermediate approach in the frequentism vs. Bayesianism debate. We also demonstrate that Neyman’s theory, by allowing non-epistemic values to influence evidence collection and formulation of statistical conclusions, does not compromise the epistemic reliability of the procedures and may improve it. This undermines the value-free ideal of scientific inference.
Type of Medium:
Online Resource
ISSN:
0925-4560
,
1572-8587
DOI:
10.1007/s10838-022-09600-x
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2022
detail.hit.zdb_id:
200721-6
detail.hit.zdb_id:
2016653-9
detail.hit.zdb_id:
1048887-X
SSG:
24
SSG:
19,2
SSG:
5,1
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