Twenty steps towards an adequate inferential interpretation of p-values
1
Institute of Agricultural and Nutritional Sciences, Chair of Agribusiness Management, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
2
Department for Agricultural Economics and Rural Development, Farm Management, Georg August University Goettingen, Goettingen, Germany
3
Institute of Business Studies, Chair of Statistics, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
- Published
- Accepted
- Subject Areas
- Science Policy, Statistics
- Keywords
- p-value, statistical inference, inverse probability error, multiple testing
- Copyright
- © 2019 Hirschauer et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2019. Twenty steps towards an adequate inferential interpretation of p-values. PeerJ Preprints 7:e3482v4 https://doi.org/10.7287/peerj.preprints.3482v4
Abstract
We suggest twenty immediately actionable steps to reduce widespread inferential errors related to “statistical significance testing.” Our propositions refer first to the theoretical preconditions for using p-values. They furthermore include wording guidelines as well as structural and operative advice on how to present results, especially in research based on multiple regression analysis, the working horse of empirical economists. Our propositions aim at fostering the logical consistency of inferential arguments by avoiding false categorical reasoning. They are not aimed at dispensing with p-values or completely replacing frequentist approaches by Bayesian statistics.
Author Comment
We improved suggestion 6 and added details on our survey (footnote 9).