Providing Evidence for the Null Hypothesis in Functional Magnetic Resonance Imaging Using Group-Level Bayesian Inference.

ABSTRACT: 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. This leads to a bias against the null hypothesis. Herein, we discuss statistical approaches to 'null effect' assessment focusing on the Bayesian parameter inference (BPI). Although Bayesian methods have been theoretically elaborated and implemented in common neuroimaging software packages, they are not widely used for 'null effect' assessment. BPI considers the posterior probability of finding the effect within or outside the region of practical equivalence to the null value. It can be used to find both 'activated/deactivated' and 'not activated' voxels or to indicate that the obtained data are not sufficient using a singl

SUBMITTER: Masharipov R 

PROVIDER: S-EPMC8674455 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature