## Metadata - Author: [[itamar-faran|Itamar Faran]] - Full Title:: Why You Should Switch to Bayesian a/B Testing - Category:: #🗞️Articles - Document Tags:: [[Bayesian testing|Bayesian testing]], - URL:: https://towardsdatascience.com/why-you-should-switch-to-bayesian-a-b-testing-364557e0af1a - Finished date:: [[2024-05-06]] ## Highlights > The Bayesian framework has a completely different point of view. We start by saying “*the click rate of the red and green buttons can be any rate between 0% and 100%, with equal chance*” (this is what we call *the prior*). This means that each button initially has a 50% chance to be better than the other. As we start gathering data, we update our knowledge, and we can say things like “*Given the data I have observed, I now think there is a 70% chance that the green button is better*”. We call this *the posterior* ([View Highlight](https://read.readwise.io/read/01hx2dzz3zkr1s438j4hgxfh73)) > However, Bayesian A/B testing does have a metric that does not have a parallel in the frequentist framework: The *Risk*. We calculate the risk for both A and B, and it’s interpretation is: “*If I chose B when B is actually worse than A, how much will I lose?*”. This metric is also used as a decision rule in the A/B test. ([View Highlight](https://read.readwise.io/read/01hx2e0cassg9w76czps8h4vpz))