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. There are parametric and non-parametric approaches for studying the data or sampling distributions, yet few resources are available to provide integrated views of data (observed or simulated), theoretical concepts, computational mechanisms and hands-on utilization via flexible graphical user interfaces. 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. The core of this framework is a modern randomization webapp, which may be invoked on any device supporting a JavaScript-enabled web-browser. We demonstrate the use of these resources to analyze proportion, mean, and other statistics using simulated (virtual experiments) and observed (e.g., Acute Myocardial Infarction, Job Rankings) data. Finally, we draw parallels between parametric inference methods and their distribution-free alternatives. The Randomization and Resampling webapp can be used for data analytics, as well as for formal, in-class and informal, out-of-the-classroom learning and teaching of different scientific concepts. Such concepts include sampling, random variation, computational statistical inference and data-driven analytics. The entire scientific community may utilize, test, expand, modify or embed these resources (data, source-code, learning activity, webapp) without any restrictions.