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    $\begingroup$ Thank you for this perspective. If I can try to boil down your answer, you're saying that any time we use a Normal prior in Bayesian Inference we're standing on the shoulders of the CLT. Makes sense. On one hand, much of classical/frequentist inference would not exist were it not for the CLT; the CLT is directly making the inference possible. On the other hand, in Bayesian Inference, only in the particular case that we assume the prior to be Normal, does the CLT come into play; and it does so very indirectly, reminding us that the Normal distribution phenomenon is inherently linked to the CLT. $\endgroup$ Commented Jan 10, 2023 at 18:35
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    $\begingroup$ @SextusEmpiricus but bootstrap's guarantees are asymptotic; I thought you were talking about model-based simulation of some sort. $\endgroup$ Commented Jan 10, 2023 at 19:48
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    $\begingroup$ "The central limit theorem (CLT) is central to any field of statistics. That's why it is called central!" << No, it's called the "central limit" theorem because it literally says that the limit of a sum of independent variables will always be centered. $\endgroup$ Commented Jan 11, 2023 at 10:33
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    $\begingroup$ @Stef I agree that there can be many interpretations to the term 'central' but at least the inventor of the term George Pólya who used it in 1920 intended the term 'central' to refer to the importance and general application of the limit. $\endgroup$ Commented Jan 11, 2023 at 11:47
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    $\begingroup$ @SextusEmpiricus Oh I think I finally understand why we're talking past each other. In my answer, by "The Frequentist needs asymptotics for reason XYZ" I meant "The Frequentist can benefit from asymptotics" rather than "The Frequentist is obligated to use asymptotics for reason XYZ". $\endgroup$ Commented Jan 11, 2023 at 13:53