  In this paper, we demonstrate that a new measure of evidence we developed
called the Dempster-Shafer p-value which allow for insights and interpretations
which retain most of the structure of the p-value while covering for some of
the disadvantages that traditional p- values face. Moreover, we show through
classical large-sample bounds and simulations that there exists a close
connection between our form of DS hypothesis testing and the classical
frequentist testing paradigm. We also demonstrate how our approach gives unique
insights into the dimensionality of a hypothesis test, as well as models the
effects of adversarial attacks on multinomial data. Finally, we demonstrate how
these insights can be used to analyze text data for public health through an
analysis of the Population Health Metrics Research Consortium dataset for
verbal autopsies.
