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
- statistical inference, p-value
- Copyright
- © 2018 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
- 2018. Twenty steps towards an adequate inferential interpretation of p-values. PeerJ Preprints 6:e3482v3 https://doi.org/10.7287/peerj.preprints.3482v3
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 of how to present results, especially in multiple regression analysis. 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 added some references, made improvements in recommendation two and three, and some changes in the footnotes.