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Hi. I passed on these questions to our data science team. Here's how they responded:

Great questions! 

1. The central limit theorem applies for proportion metrics and for continuous data.  So even if the underlying metric is not normally distributed, its sample mean is.  We are comparing means, which is why the t-test is appropriate. 2. The variance of an individual observation is ​`p(1-p)`​.  Summing ​`n`​ independent random variables, the variance of the sum is ​`np(1-p)`​.  Taking their mean, the variance is ​`p(1-p)/n`​.

Thank you for fast reply which is really helpful to me. anyway, how about supplementing these math basis in document? if you guys do so, it is so helpful to many growthbook users.

We can do that - but we're also open source, you're welcome to edit the documentation as well

oh, there is way of contributing to documents. thanks!