This article outlines a broadly-applicable new method of statistical analysis for situations involving two competing hypotheses. Hypotheses assessment is a frequentist procedure designed to answer the question: Given the sample evidence (and assumed model), what is the relative plausibility of each hypothesis? Our aim is to determine frequentist confidences in the hypotheses that are relevant to the data at hand and are as powerful as the particular application allows. Hypotheses assessments complement significance tests because providing confidences in the hypotheses in addition to test results can better inform applied researchers about the strength of evidence provided by the data. For simple hypotheses, the method produces minimum and maximum confidences in each hypothesis. The composite case is more complex, and we introduce two conventions to aid with understanding the strength of evidence. Assessments are qualitatively different from significance test and confidence interval outcomes, and thus fill a gap in the statistician's toolkit.

翻译：本文概述了一种适用于两种竞争假设情境的、具有广泛适用性的新型统计分析方法。假设评估是一种频率学派程序，旨在回答以下问题：基于样本证据（及假定模型），每个假设的相对可信度如何？我们的目标是确定与当前数据相关且特定应用允许范围内尽可能有力的频率学派假设置信度。假设评估对显著性检验形成补充，因为在检验结果之外提供假设置信度，能更好地告知应用研究人员数据所提供证据的强度。对于简单假设，该方法能得出每个假设的最小及最大置信度。复合假设情况更为复杂，我们引入两种约定以帮助理解证据强度。这些评估在性质上不同于显著性检验和置信区间结果，从而填补了统计学家工具包中的空白。