I'm running an A/B testing in a website and I'm capturing the number of clicks in a given area of a page.
I am calculating the average of clicks per user's session and its standard deviation. The data process is quite heavy so my plan was to run it nightly with the data of each day and then at the end of a given period aggregate those data and use a t-test calculator to check the statistic relevance.
For each day's calculus of standard deviation I'm using pyspark sql stdev_sample function but I'm struggling to find a way to aggregate those values at the end. I have come across to this question and the oldest answer doesn't seem to work with the tests I did (not only with my A/B testing data), so not sure if it's valid. The newest answer though works for the data I've tested it with, but when I use population standard deviation.
So my question is, it's fine if I use population standard deviation (at the end of the day I am using every datapoint for each feature for calculating it), I've read here that
Therefore, you would normally calculate the population standard deviation if: (1) you have the entire population or (2) you have a sample of a larger population, but you are only interested in this sample and do not wish to generalize your findings to the population
so wonder if my case fits in that point 2.
Otherwise, how could I aggregate with the sample standard deviation?