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.

中文翻译：

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统计证据和意外在可能性理论下统一

Sander Greenland 认为，假设检验的报告结果应包括意外值，即p值倒数的以 2 为底的对数。令人惊讶的是数据中有多少证据可以拒绝原假设。惊讶的概括还可以衡量有多少证据证明拒绝复合假设（例如置信区间的补充）是合理的。那种延长的惊喜，叫做惊喜，量化了相信某个假设的代理人在观察数据时会经历多少位惊讶。虽然惊奇是假设空间中点的函数，但惊奇是假设空间子集的函数。满足条件最小加概率的条件，惊喜继承了可能性理论的丰富工具。等效兼容性函数最近已应用于复制危机、调整先验信息的p值以及比较科学理论。