Null Hypothesis Significance Testing: a short tutorial
- Published
- Accepted
- Subject Areas
- Science and Medical Education, Statistics
- Keywords
- NHST, p-value, confidence intervals, effect size, reporting
- Copyright
- © 2015 Pernet
- 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
- 2015. Null Hypothesis Significance Testing: a short tutorial. PeerJ PrePrints 3:e1050v1 https://doi.org/10.7287/peerj.preprints.1050v1
Abstract
Although thoroughly criticized, null hypothesis significance testing is the statistical method of choice in biological, biomedical and social sciences to investigate if an effect is likely. In this short tutorial, I first summarize the concepts behind the method while pointing to common interpretation errors. I then present the related concepts of confidence intervals, effect size, and Bayesian factor, and discuss what should be reported in which context. The goal is to clarify concepts, present statistical issues that researchers face using the NHST framework and highlight good practices.
Author Comment
This article is my attempt to pack into a short paper all the major issues in interpreting and using NHST and also proposing a 'new' way to report statistical results.
