RT Journal Article
SR Electronic
T1 Evidence for the null hypothesis in functional magnetic resonance imaging using group-level Bayesian inference
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.06.02.446711
DO 10.1101/2021.06.02.446711
A1 Ruslan Masharipov
A1 Yaroslav Nikolaev
A1 Alexander Korotkov
A1 Michael Didur
A1 Denis Cherednichenko
A1 Maxim Kireev
YR 2021
UL http://biorxiv.org/content/early/2021/06/02/2021.06.02.446711.abstract
AB Classical null hypothesis significance testing is limited to the rejection of the point-null hypothesis; it does not allow the interpretation of non-significant results. Moreover, studies with a sufficiently large sample size will find statistically significant results even when the effect is negligible and may be considered practically equivalent to the ‘null effect’. This leads to a publication bias against the null hypothesis. There are two main approaches to assess ‘null effects’: shifting from the point-null to the interval-null hypothesis and considering the practical significance in the frequentist approach; using the Bayesian parameter inference based on posterior probabilities, or the Bayesian model inference based on Bayes factors. Herein, we discuss these statistical methods with particular focus on the application of the Bayesian parameter inference, as it is conceptually connected to both frequentist and Bayesian model inferences. Although Bayesian methods have been theoretically elaborated and implemented in commonly used neuroimaging software, they are not widely used for ‘null effect’ assessment. To demonstrate the advantages of using the Bayesian parameter inference, we compared it with classical null hypothesis significance testing for fMRI data group analysis. We also consider the problem of choosing a threshold for a practically significant effect and discuss possible applications of Bayesian parameter inference in fMRI studies. We argue that Bayesian inference, which directly provides evidence for both the null and alternative hypotheses, may be more intuitive and convenient for practical use than frequentist inference, which only provides evidence against the null hypothesis. Moreover, it may indicate that the obtained data are not sufficient to make a confident inference. Because interim analysis is easy to perform using Bayesian inference, one can evaluate the data as the sample size increases and decide to terminate the experiment if the obtained data are sufficient to make a confident inference. To facilitate the application of the Bayesian parameter inference to ‘null effect’ assessment, scripts with a simple GUI were developed.Competing Interest StatementThe authors have declared no competing interest.