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Manual assignment for Stratified Sampling

Statsig Product Updates
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4/17/2023
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Manual assignment for Stratified Sampling

Layers in a Cake

Statsig manages randomization during experiment assignment. In some B2B (or low scale, high variance cases) the law of large numbers doesn’t work. Here it is helpful to manually assign users to test and control to ensure both groups are comparable. Statsig now lets you do this. Learn More

What is Stratified Sampling?

Stratified sampling is a sampling method that ensures specific groups of data (or users) are properly represented. You can think of this like slicing a birthday cake. If sliced recklessly, some people may get too much frosting and others will get too little. But when sliced carefully, each slice is a proper representation of the whole. In Data Science, we commonly trust random sampling. The Law of Large Numbers ensures that a sufficiently-sized sample will be representative of the entire population. However, in some cases, this may not be true, such as:

  • When the sample size is small

  • When the samples are heterogeneous


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At OpenAI, we want to iterate as fast as possible. Statsig enables us to grow, scale, and learn efficiently. Integrating experimentation with product analytics and feature flagging has been crucial for quickly understanding and addressing our users' top priorities.
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