Main content

Date created: | Last Updated:


Creating DOI. Please wait...

Create DOI

Category: Project

Description: Across the social sciences, researchers have overwhelmingly used the classical statistical paradigm to draw conclusions from data, often focusing heavily on a single number: p. Recent years, however, have witnessed a surge of interest in an alternative statistical paradigm: Bayesian inference, in which probabilities are attached to parameters and models. We feel it is informative to provide statistical conclusions that go beyond a single number, and --regardless of one's statistical preference-- it can be prudent to report the results from both the classical and the Bayesian paradigm. In order to promote a more inclusive and insightful approach to statistical inference we show how the open-source software program JASP ( provides a set of comprehensive Bayesian reanalyses from just a few commonly-reported summary statistics such as t and N. These Bayesian reanalyses allow researchers --and also editors, reviewers, readers, and reporters-- to quantify evidence on a continuous scale, assess the robustness of that evidence to changes in the prior distribution, and gauge which posterior parameter ranges are more credible than others. The procedure is illustrated using the seminal Festinger and Carlsmith (1959) study on cognitive dissonance.

License: CC-By Attribution 4.0 International

Has supplemental materials for Bayesian Reanalyses from Summary Statistics: A Guide for Academic Consumers on OSF Preprints


Loading files...



Recent Activity

Loading logs...

OSF does not support the use of Internet Explorer. For optimal performance, please switch to another browser.
This website relies on cookies to help provide a better user experience. By clicking Accept or continuing to use the site, you agree. For more information, see our Privacy Policy and information on cookie use.

Start managing your projects on the OSF today.

Free and easy to use, the Open Science Framework supports the entire research lifecycle: planning, execution, reporting, archiving, and discovery.