What's more surprising is how little the central limit theorem holds and is useful, yet is still used all the time to justify poor analysis, usually a/b testing

What's more surprising is how little the central limit theorem holds and is useful, yet is still used all the time to justify poor analysis, usually a/b testing. When the underlying distribution has high variance, as many metrics I've come across with extreme long tailed behavior, the aggregates need large N before they adhere to Gaussian