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Bayesian A/B Testing

Data Skeptic

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The Law of Large Numbers Is Better Than the CLT

The law of large numbers is so trustworthy, absolutely until it fails. So i'm curious to hear your thoughts on how long you want to run an a b test for. Do you have any rules of thumb for how to decide the appropriate length to run an experiment for and if you should make any midstream changes? I tend to avoid mid stream changes, just because it's just one other factor you have to think about when you do the analysis.

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