Bayesian A/B testing with uniform prior

The advantage of Bayesian A/B testing is that it considers our prior belief about the distribution. However, sometimes we do not have this, so we set prior to be uniform and work with visits and conversion counts. Why would one still use Bayesian A/B testing rather than a frequentist testing?

• Why do you assume it's better in the first place? – Kodiologist Jul 4 '17 at 14:24
• @Kodiologist I edited my question. However, I assume it is better for my question as "what is the probability that A is better than B". Bayesian can answer me that like " 60% sure that A is better than B", whereas a non-bayesian A/B testing can tell me that there is no difference between A and B. So, in fact, my question can be answered by a bayesian a/b testing. – Alina Jul 4 '17 at 15:08
• And also, because I do not know the real effect size, so I do not know what should be the size of my groups to be in order to detect my unknown effect size. – Alina Jul 4 '17 at 15:14

1. The posterior distribution $$\mathbb{P}(\theta|D)$$ gives us a detailed view of our knowledge about the conversation rate, $$\theta$$. It allows us to visualize our knowledge about the parameter's value, compare the similarity of two test variants, and use decision theory to estimate the expected value under each test.