# Main content

Contributors:

Date created: | Last Updated:

: DOI | ARK

Creating DOI. Please wait...

Category: Project

Description: Being able to interpret null effects' is important for cumulative knowledge generation in science. To draw informative conclusions from null-effects, researchers need to move beyond the incorrect interpretation of a non-significant result in a null-hypothesis significance test as evidence of the absence of an effect. We explain how to statistically evaluate null-results using equivalence tests, Bayesian estimation, and Bayes factors. A worked example demonstrates how to apply these statistical tools and interpret the results. Finally, we explain how no statistical approach can actually prove that the null-hypothesis is true, and briefly discuss the philosophical differences between statistical approaches to examine null-effects. The increasing availability of easy-to-use software and online tools to perform equivalence tests, Bayesian estimation, and calculate Bayes factors make it timely and feasible to complement or move beyond traditional null-hypothesis tests, and allow researchers to draw more informative conclusions about null-effects.

License: GNU General Public License (GPL) 2.0

Loading files...

### Components

#### Making 'Null Effects' Informative: Statistical Techniques and Inferential Frameworks

Being able to interpret null effects' is important for cumulative knowledge generation in science. To draw informative conclusions from null-effects,...

Recent Activity

Loading logs...

Loading logs...