Game-theoretic statistics and safe anytime-valid inference · HackerPapers discussions

Game-theoretic statistics and safe anytime-valid inference — mentioned by dccsillag

Wed, 01 Jan 2025 12:35:41 GMT

If thresholding of P-values is the issue, E-values -- a recent, much more elegant, easier to work with, and more robust alternative to P-values -- solve this. https://arxiv.org/abs/2312.08040 https://arxiv.org/abs/2205.00901 https://arxiv.org/abs/2210.01948 https://arxiv.org/abs/2410.23614

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Game-theoretic statistics and safe anytime-valid inference — mentioned by gatopingado

Sat, 10 Aug 2024 20:08:19 GMT

Thanks for noting! This is the right link for [1]: https://arxiv.org/abs/2210.01948

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Game-theoretic statistics and safe anytime-valid inference — mentioned by kimukasetsu

Fri, 16 Jun 2023 13:37:01 GMT

The biggest mistake engineers make is determining sample sizes. It is not trivial to determine the sample size for a trial without prior knowledge of effect sizes. Instead of waiting for a fixed sample size, I would recommend using a sequential testing framework: set a stopping condition and perform a test for each new batch of sample units. This is called optional stopping and it is not possible using a classic t-test, since Type I and II errors are only valid at a determined sample size. However, other tests make it possible: see safe anytime-valid statistics [1, 2] or, simply, bayesian…

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