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Influence of multiple hypothesis testing on reproducibility in neuroimaging research: A simulation study and Python-based software by Puoliväli, Tuomas; Palva, Satu; Palva, J. Matias is a Neuroscience article available to read on EtoBox.

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## Background: Reproducibility of research findings has been recently questioned in many fields of science, including psychology and neurosciences. one factor influencing reproducibility is the simultaneous testing of multiple hypotheses, which entails false positive findings unless the analyzed p-values are carefully corrected. while this multiple testing problem is well known and studied, it continues to be both a theoretical and practical problem. ## New method: Here we assess reproducibility in simulated experiments in the context of multiple testing. we consider methods that control either the family-wise error rate (fwer) or false discovery rate (fdr), including techniques based on random field theory (rft), cluster-mass based permutation testing, and adaptive fdr. several classical methods are also considered. the performance of these methods is investigated under two different models. ## Results: We found that permutation testing is the most powerful method among the considered approaches to multiple testing, and that grouping hypotheses based on prior knowledge can improve power. we also found that emphasizing primary and follow-up studies equally produced most reproducibl

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Author

Puoliväli, Tuomas; Palva, Satu; Palva, J. Matias

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0165-0270)

Published

2020

Language

EN

Field

Neuroscience (Life Sciences)