Generally, experimentalists make decisions by comparing means and using standard deviation to assess spread. There's exceptions, like using percentile metrics, but the vast majority of comparisons are done in this way.
It's effective, but it's also well known that means mask a lot of information. To help experimentalists on Statsig understand what's going on behind the scenes, we're adding an easy interface to dig into the distributions behind results.
Here, we can see a pulse result showing a statistically significant lift in revenue for both of our experimental variants.
By opening the histogram view (found in the statistics details), we can easily see that this lift is mostly driven by more users moving from the lowest-spend bucket into higher buckets
This is available today on Warehouse Native - and we're scoping out Statsig Cloud.