Dichotomous thinking and informational waste in neuroimaging

Neuroimaging relies on separate statistical inferences at tens of thousands of spatial locations. Such massively univariate analysis typically requires adjustment for multiple testing in an attempt to maintain the family-wise error rate at a nominal level of 5%. We discuss how this approach is associated with substantial information loss because of an implicit but questionable assumption about the effect distribution across spatial units. To improve inference efficiency, predictive accuracy, and generalizability, we propose a Bayesian multilevel modeling framework. In addition, we make four actionable suggestions to alleviate information waste and to improve reproducibility: (1) abandon strict dichotomization; (2) report full results; (3) quantify effects, and (4) model data hierarchy.