The Power of Bayesian A/B Testing - Deepstash

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## Experimentation Background

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 determine which variant is better.

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## Bayesian Methods

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 testing asserts that the false positive rate — the proportion of times we accept the treatment when the treatment is not actually better — is not very important.

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## Justifying Prior Choice

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 in the previous section assumed that we used the perfect prior distribution.

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## Reaching Conclusions Faster

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 leveraging priors, Bayesian A/B testing often needs fewer data points to reach a conclusion than other methods.

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