In any A/B test, we use the data we collect from variants A and B to compute some metric for each variant (e.g. the rate at which a button is clicked). Then, we use a statistical method to determin...

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In Bayesian A/B testing, we model the metric for each variant as a random variable with some probability distribution.

By accepting variants that offer a small improvement, Bayesian A/B testi...

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Critics of a Bayesian analysis might argue that the choice of a prior distribution was not sufficiently justified and had a significant impact on the experiment. In fact, the simulation presented i...

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Using relevant prior information makes experiments conclude faster. Every piece of information that we embed into the prior is a piece of information that we do not need to learn from the data. By ...

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