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I'm a data scientist and I actually think this is a smart way to go.

The author could have implemented a simple Chi-square test and gotten CIs. The problem is that conversion rates are usually < 6% and that means you'd have to have a MASSIVE sample size to detect a difference.

Our basically Type II error is much more important than typical statistical applications. Our statistical power is super important.

The author could implement Bayesian statistics with a Beta distribution prior initialized with alpha = 3, beta = 100 (mimicking a 3% conversion rate). The results would be robust to this prior information. The problem is that there is no closed-forum likelihood solution. This means you need to use Markov Chain Monte Carlo simulation. Web servers don't like that.

In my experience, if you see a nice 10% boost in conversion rate (conv. b / conv. a) after some representative period of time like a few days, you should just go with that result.

In that way, you don't ignore what's smacking you in the head. "The implementation had a higher conv. rate or not over a few days." Detecting small differences really well with stats is fairly pointless in this space.

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