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.

Large document collections containing multiple topics can be overwhelming to understand, requiring librarians and archivists significant time and efforts to develop access points. Efficient computational methods can aid this process by uncovering groups of documents that can be described for access. We investigate the use of density based clustering with document segmentation to identify points of access as dense clusters of information. The method returns stories and classes of cohesive clusters that can be described as precise points of access. We found that our method performs more efficiently than K-means clustering and topic model using Latent Dirichlet Allocation (LDA). We use Hadoop to process a large document collection.