The field of statistics makes valuable contributions to functional neuroimaging research by establishing procedures for the design and conduct of neuroimaging experiments and providing tools for objectively quantifying and measuring the strength of scientific evidence provided by the data. Two common functional neuroimaging research objectives include detecting brain regions that reveal task-related alterations in measured brain activity (activations) and identifying highly correlated brain regions that exhibit similar patterns of activity over time (functional connectivity). This article highlights various statistical procedures for analyzing data from activation studies and functional connectivity studies, focusing on functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) data. Also discussed are emerging statistical methods for prediction using fMRI and PET data, which stand to increase the translational significance of functional neuroimaging data to clinical practice.