Making 'null effects' informative: statistical techniques and inferential frameworks

Christopher Harms (Corresponding author), Daniël Lakens

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Abstract

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.
Original languageEnglish
Pages (from-to)382–393
Number of pages12
JournalJournal of Clinical and Translational Research
Volume3
Issue numberS2
DOIs
Publication statusPublished - Jul 2018

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