Statistical evidence and surprise unified under possibility theory
本文在可能性理论框架下统一了统计证据与惊奇感，将惊奇度（surprise）定义为假设空间子集上的函数，并展示了其在复制危机、p值调整和理论比较中的应用。
Abstract Sander Greenland argues that reported results of hypothesis tests should include the surprisal , the base‐2 logarithm of the reciprocal of a p ‐value. The surprisal measures how many bits of evidence in the data warrant rejecting the null hypothesis. A generalization of surprisal also can measure how much the evidence justifies rejecting a composite hypothesis such as the complement of a confidence interval. That extended surprisal, called surprise , quantifies how many bits of astonishment an agent believing a hypothesis would experience upon observing the data. While surprisal is a function of a point in hypothesis space, surprise is a function of a subset of hypothesis space. Satisfying the conditions of conditional min‐plus probability, surprise inherits a wealth of tools from possibility theory. The equivalent compatibility function has been recently applied to the replication crisis, to adjusting p ‐values for prior information, and to comparing scientific theories.