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Some Thoughts About Statistical Inference In The 21st Century

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  • , Hirschauer

Abstract

This paper shows that the debate on p-values and statistical significance testing addresses one of the most fundamental question of the data-based sciences: how can we learn from data and come to the most reasonable belief (judgement, proposition) regarding a real-world state of interest – given the available evidence (data) and the remaining uncertainty? Answering this question of inductive inference and understanding when and how statistical measures (i.e., summary statistics of the given dataset) can help us evaluate the knowledge gain that can be obtained from a particular sample of data is extremely important in any field of science.

Suggested Citation

  • , Hirschauer, 2022. "Some Thoughts About Statistical Inference In The 21st Century," SocArXiv exdfg, Center for Open Science.
    • Handle: RePEc:osf:socarx:exdfg
    DOI: 10.31219/osf.io/exdfg
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    References listed on IDEAS

    as
    1. Guido W. Imbens, 2021. "Statistical Significance, p-Values, and the Reporting of Uncertainty," Journal of Economic Perspectives, American Economic Association, vol. 35(3), pages 157-174, Summer.
    2. Hirschauer, Norbert & Grüner, Sven & Mußhoff, Oliver & Becker, Claudia & Jantsch, Antje, 2020. "Can p-values be meaningfully interpreted without random sampling?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 14, pages 71-91.
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