This _looks_ like a very interesting take on sample sizes, but I'm not qualified to evaluate its technical merit.<p>However, as someone more on the creative, A/B testing-adjacent side of things, it's always boggled my mind that this isn't a settled issue.<p>Given the amount of money poured into testing these days, can anyone on the data science side give their 2c on why there are still novel approaches across tools and the industry with regards to samples sizes and significance?<p>How come there isn't a settled best practice?