Stratified Sampling
What is Stratified Sampling
Stratified sampling involves dividing the entire population into homogeneous groups called strata (plural for stratum). Random samples are then selected from each stratum. e.g. If you had XS and XL customers and randomized them into two groups - Control and Test, you'd want both Control and Test to be balanced across XS and XL customers. You can also stratify based on a metric like Revenue/User.
With large numbers, randomization typically solves this. However in B2B scenarios and other relatively low volume or high variance scenarios, stratified sampling is useful to ensure this balance. Statsig supports both automated and manual stratified sampling. On tests where a tail-end of power users drive a large portion of an overall metric value, stratified sampling meaningfully reduces false positive rates and makes your results more consistent and trustworthy. In our simulations, we saw around a 50% decrease in the variance of reported results.