AB testing aids business operators with their deci-sion making, and is considered the gold standard method for learning from data to improve digital user experiences. However, there is usually a gap between the requirements of practitioners, and the constraints imposed by the statistical hypothesis testing methodologies commonly used for analysis of AB tests. These include the lack of statistical power in multivariate designs with many factors, correlations between these factors, the need of sequential testing for early stopping, and the inability to pool knowledge from past tests. Here, we propose a solution that applies hierarchical Bayesian estimation to address the above limitations. In comparison to current sequential AB testing methodology, we increase statistical power by exploiting correlations between factors, enabling sequential testing and progressive early stopping, without incurring excessive false positive risk. We also demonstrate how this methodology can be extended to enable the extraction of composite global learnings from past AB tests, to accelerate future tests. We underpin our work with a solid theoretical framework that articulates the value of hierarchical estimation. We demonstrate its utility using both numerical simulations and a large set of real-world AB tests. Together, these results highlight the practical value of our approach for statistical inference in the technology industry.

Interferometric synthetic aperture radar (InSAR) data from RADARSAT-1 have been examined to assess their potential for mapping terrain and changes in snow cover characteristics, relative to the limiting effects of snow on foraging by endangered Peary caribou (Rangifer tarandus). Radar is one of the few observational tools that can provide information on the changing snow pack during the dark winter months. The goal of this research is to characterize the general ensemble of terrain characteristics that may affect winter foraging patterns of Peary caribou and the inter-annual consistency of snow cover patterns within our specific study area.