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

Date created: | Last Updated:

: DOI | ARK

Creating DOI. Please wait...

Create DOI

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

Files

Loading files...

Citation

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...

Recent Activity

Loading logs...

OSF does not support the use of Internet Explorer. For optimal performance, please switch to another browser.
Accept
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.
Accept
×

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.