| Literature DB >> 31596231 |
Tamar R Makin1, Jean-Jacques Orban de Xivry2,3.
Abstract
Inspired by broader efforts to make the conclusions of scientific research more robust, we have compiled a list of some of the most common statistical mistakes that appear in the scientific literature. The mistakes have their origins in ineffective experimental designs, inappropriate analyses and/or flawed reasoning. We provide advice on how authors, reviewers and readers can identify and resolve these mistakes and, we hope, avoid them in the future.Entities:
Keywords: analysis; causality; neuroscience; none; null results; p-hacking; power; statistics
Mesh:
Year: 2019 PMID: 31596231 PMCID: PMC6785265 DOI: 10.7554/eLife.48175
Source DB: PubMed Journal: Elife ISSN: 2050-084X Impact factor: 8.140