Bayesian hypothesis testing presents an attractive alternative to p value hypothesis testing. Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability to quantify evidence and the ability to monitor and …
This document is summarised in the table below. It shows the linear models underlying common parametric and “non-parametric” tests. Formulating all the tests in the same language highlights the many similarities between them.
We have empirically assessed the distribution of published effect sizes and estimated power by analyzing 26,841 statistical records from 3,801 cognitive neuroscience and psychology papers published recently. The reported median effect size was D = …
Scientists should be able to provide support for the absence of a meaningful effect. Currently, researchers often incorrectly conclude an effect is absent based a nonsignificant result. A widely recommended approach within a frequentist framework is …
Meta-analysis synthesizes a body of research investigating a common research question. Outcomes from meta-analyses provide a more objective and transparent summary of a research area than traditional narrative reviews. Moreover, they are often used …
This video will introduce how to calculate confidence intervals around effect sizes using the MBESS package in R. All materials shown in the video, as well as content from our other videos, can be found here: https://osf.io/7gqsi/
The book is associated with the lsr package on CRAN and GitHub. The package is probably okay for many introductory teaching purposes, but some care is required. The package does have some limitations (e.g., the etaSquared function does strange things …
Non-significant results are less likely to be reported by authors and, when submitted for peer review, are less likely to be published by journal editors. This phenomenon, known collectively as publication bias, is seen in a variety of scientific …