Making Bayesian A/B testing more accessible
Data Science and Beyond
JUNE 19, 2016
Much has been written in recent years on the pitfalls of using traditional hypothesis testing with online A/B tests. A key issue is that you’re likely to end up with many false positives if you repeatedly check your results and stop as soon as you reach statistical significance. One way of dealing with this issue is by following a Bayesian approach to deciding when the experiment should be stopped.
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