OASIS: An interpretable, finite-sample valid alternative to Pearson's X 2 for scientific discovery.

Contingency tables are pervasive across quantitative research and data-science applications. Existing statistical tests fall short, however; none provide robust, computationally efficient inference and control Type I error. In this work, motivated by a recent advance in reference-free inference for genomics, we propose a family of tests on contingency tables called OASIS. OASIS utilizes a linear test-statistic, enabling the computation of closed form p-value bounds, as well as a standard asymptotic normality result. OASIS provides a partitioning of the table for rejected hypotheses, lending interpretability to its rejection of the null. In genomic applications, OASIS performs reference-free and metadata-free variant detection in SARS-CoV-2 and M. Tuberculosis, and demonstrates robust performance for single cell RNA-sequencing, all tasks without existing solutions.