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.

翻译：本文证明了我们开发的一种名为“邓普斯特-谢弗p值”的新型证据度量，能够提供见解与解释，既保留了p值的大部分结构，又弥补了传统p值的一些缺陷。此外，通过经典的大样本界值和模拟研究，我们展示了DS假设检验形式与传统频率学派检验范式之间的紧密联系。我们还证明了该方法如何为假设检验的维度提供独特见解，并模拟对多项数据的对抗攻击效应。最后，通过分析人口健康指标研究联盟的口头尸检数据集，展示了这些见解如何用于公共卫生领域的文本数据分析。