Using Bayes factor hypothesis testing in neuroscience to establish evidence of absence

Most neuroscientists would agree that for brain research to progress, we have to know which experimental manipulations have no effect as much as we must identify those that do have an effect. The dominant statistical approaches used in neuroscience rely on P values and can establish the latter but n...

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Bibliographic Details
Published in:Nature neuroscience 2020-07, Vol.23 (7), p.788-799
Main Authors: Keysers, Christian, Gazzola, Valeria, Wagenmakers, Eric-Jan
Format: Article
Language:English
Subjects:
631/378/2645/1458
706/648/479
Animal Genetics and Genomics
Animals
Bayes factors
Bayes Theorem
Bayesian analysis
Behavioral Sciences
Biological Techniques
Biomedical and Life Sciences
Biomedicine
Brain - physiology
Brain research
Humans
Hypotheses
Hypothesis
Hypothesis testing
Methods
Nervous system
Neurobiology
Neurosciences
Neurosciences - methods
Null hypothesis
Research Design
Review Article
Variance analysis
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Summary:Most neuroscientists would agree that for brain research to progress, we have to know which experimental manipulations have no effect as much as we must identify those that do have an effect. The dominant statistical approaches used in neuroscience rely on P values and can establish the latter but not the former. This makes non-significant findings difficult to interpret: do they support the null hypothesis or are they simply not informative? Here we show how Bayesian hypothesis testing can be used in neuroscience studies to establish both whether there is evidence of absence and whether there is absence of evidence. Through simple tutorial-style examples of Bayesian t -tests and ANOVA using the open-source project JASP, this article aims to empower neuroscientists to use this approach to provide compelling and rigorous evidence for the absence of an effect. Keysers et al. show why P values do not differentiate inconclusive null findings from those that provide important evidence for the absence of an effect. They provide a tutorial on how to use Bayesian hypothesis testing to overcome this issue.
ISSN:1097-6256
1546-1726
1546-1726
DOI:10.1038/s41593-020-0660-4