关于使用e值进行数据分割的说明：我对Glenn Shafer的评论的在线附录“通过投注进行测试”
A note on data splitting with e-values: online appendix to my comment on Glenn Shafer's "Testing by betting"
摘要：
本文重新分析了Cox使用e值（Shafer的投注分数）进行数据分割的理想化测试示例。考克斯令人兴奋的发现是，数据分割的方法，同时允许灵活的数据分析，实现了相当高的效率，约80%。对该方法最严重的反对意见是，它涉及随机分割数据，因此不同的人分析相同的数据可能会得到非常不同的答案。使用e值而不是p值弥补了这一缺点。
英文摘要：
This note reanalyzes Cox's idealized example of testing with data splitting using e-values (Shafer's betting scores). Cox's exciting finding was that the method of data splitting, while allowing flexible data analysis, achieves quite high efficiencies, of about 80%. The most serious objection to the method was that it involves splitting data at random, and so different people analyzing the same data may get very different answers. Using e-values instead of p-values remedies this disadvantage.
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。
