Publication bias, in which nonsignificant results are less likely to be published, is a considerable issue for many areas of psychology. At least within a hypotheticodeductive framework, the capacity to falsify a hypothesis is critical for testing predictions. Thus, one contributing factor to publication bias is that traditional frequentist pvalues cannot be used to provide evidence for the absence of an effect, as a nonsignificant pvalue (regardless of its size) could either be consistent with the absence of an effect or that the test lacked statistical power.

In this talk, two alternative approaches for making null effects informative will be presented: Equivalence tests and Bayesian hypothesis tests. These methods will be demonstrated in a nontechnical manner using JASP and JAMOVI, which are open source pointandclick software packages. In sum, this talk will be useful for anyone that wants to expand their statistical toolbox with methods that can accurately evaluate the absence of effects.

This talk was delivered and recorded at the Department of Psychology, University of Oslo, as part of its open science dropin series, on 11 June, 2024.

Slidedeck: https://osf.io/7bdhk
Data: https://osf.io/fa7w6