  The last decades saw dramatic progress in brain research. These advances were
often buttressed by probing single variables to make circumscribed discoveries,
typically through null hypothesis significance testing. New ways for generating
massive data fueled tension between the traditional methodology, used to infer
statistically relevant effects in carefully-chosen variables, and
pattern-learning algorithms, used to identify predictive signatures by
searching through abundant information. In this article, we detail the
antagonistic philosophies behind two quantitative approaches: certifying robust
effects in understandable variables, and evaluating how accurately a built
model can forecast future outcomes. We discourage choosing analysis tools via
categories like 'statistics' or 'machine learning'. Rather, to establish
reproducible knowledge about the brain, we advocate prioritizing tools in view
of the core motivation of each quantitative analysis: aiming towards
mechanistic insight, or optimizing predictive accuracy.
