  Neuroscience is undergoing faster changes than ever before. Over 100 years
our field qualitatively described and invasively manipulated single or few
organisms to gain anatomical, physiological, and pharmacological insights. In
the last 10 years neuroscience spawned quantitative big-sample datasets on
microanatomy, synaptic connections, optogenetic brain-behavior assays, and
high-level cognition. While growing data availability and information
granularity have been amply discussed, we direct attention to a routinely
neglected question: How will the unprecedented data richness shape data
analysis practices? Statistical reasoning is becoming more central to distill
neurobiological knowledge from healthy and pathological brain recordings. We
believe that large-scale data analysis will use more models that are
non-parametric, generative, mixing frequentist and Bayesian aspects, and
grounded in different statistical inferences.
