Hi, this is Leo, Optimizely's statistician. If you're looking for a more scientific paper, maybe take a look at this one we wrote recently: http://arxiv.org/abs

Hi, this is Leo, Optimizely's statistician. If you're looking for a more scientific paper, maybe take a look at this one we wrote recently: http://arxiv.org/abs/1512.04922 http://arxiv.org/abs/1512.04922 Should have everything you would ever want to know about the method. I agree with you that the problem of inference and interpretation between A/B data, algorithms, and the people who make decisions from them is a hard one and worth working on. That said, I do think the two sources of error our stats engine addresses - repeatedly checking results, and cherry picking from many metrics and variations - did make progress in having folks correctly interpret A/B Tests. This did result in more conservative results, but the benefit was that the variations that do become significant are more trustworthy. I think this was absolutely the right tradeoff to make for our customers, and trustworthyness is a pretty important aspiration for stats/ML/data science in general. Of course I did write the thing, so I'm not very impartial.