Randomization-based statistical inference: A resampling and simulation infrastructure

Statistical inference involves drawing scientifically-based conclusions describing natural processes or observable phenomena from datasets with intrinsic random variation. We designed, implemented, and validated a new portable randomization-based statistical inference infrastructure (http://socr.umich.edu/HTML5/Resampling_Webapp) that blends research-driven data analytics and interactive learning, and provides a backend computational library for managing large amounts of simulated or user-provided data.