I created an open source A/B test framework[0], which also uses Bayesian analysis on the dashboard. IANAS(tatistician), but from what I understand it’s still be

I created an open source A/B test framework[0], which also uses Bayesian analysis on the dashboard. IANAS(tatistician), but from what I understand it’s still better to plan the check point in advance, rather than stop when reaching significance. A couple of articles worth reading [1] [2] (can’t exactly vouch for their validity but seem to make some good arguments that appear thought out) [0] https://github.com/Alephbet/gimel https://github.com/Alephbet/gimel [1] http://varianceexplained.org/r/bayesian-ab-testing/ http://varianceexplained.org/r/bayesian-ab-testing/ [2] http://blog.analytics-toolkit.com/2017/the-bane-of-ab-testing-reaching-statistical-significance/ http://blog.analytics-toolkit.com/2017/the-bane-of-ab-testin...