有自然的分数：充分评论谢弗，“通过投注测试：统计和科学交流的策略”
There are natural scores: Full comment on Shafer, "Testing by betting: A strategy for statistical and scientific communication"
摘要：
Shafer（2021）提供了一个关于统计检验的赌博观点，这可能对基础辩论有用，因为关于这种检验的争议仍然很激烈。然而，为了对研究人员有所帮助，这种观点需要使用真实的例子进行更多的阐述，其中（a）投注分数在研究目标方面具有合理性和解释，将其与不可数的数学可能性区分开来，以及（b）抽样模型中的假设是不确定的。关于合理性，Shafer说，“没有人对从P值得出的分数的任何特定选择提出令人信服的理由”，然后指出“选择基本上是任意的”。然而，一些（但不是大多数）分数可以由学习目标（例如，信息测量;决策）。我在信息统计和数据挖掘中反复看到的是对数值、对数值或S值s = -log（p），其中对数基数决定了尺度。本评论解释了这一选择的理由。
英文摘要：
Shafer (2021) offers a betting perspective on statistical testing which may be useful for foundational debates, given that disputes over such testing continue to be intense. To be helpful for researchers, however, this perspective will need more elaboration using real examples in which (a) the betting score has a justification and interpretation in terms of study goals that distinguishes it from the uncountable mathematical possibilities, and (b) the assumptions in the sampling model are uncertain. On justification, Shafer says 'No one has made a convincing case for any particular choice' of a score derived from a P-value and then states that 'the choice is fundamentally arbitrary'. Yet some (but not most) scores can be motivated by study goals (e.g., information measurement; decision making). The one I have seen repeatedly in information statistics and data mining is the surprisal, logworth or S-value s = -log(p), where the log base determines the scale. The present comment explains the rationale for this choice.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
