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A curated summary of the top questions asked on our Slack community, often relating to implementation, functionality, and building better products generally.
How can I decrease variance in A/B tests using CUPED and stratification methods?
Date of slack thread: 5/22/24
Anonymous: Hi, I am working on decreasing the variance in our A/B tests. Could you advise on methods to achieve this, such as adding or modifying covariates in the CUBED model? Additionally, would stratification be a viable option? Thank you!
Timothy Chan (Statsig): There are a few methods, but your degree of success will depend on the specifics of your situation:
- Running experiments on existing users? CUPED will likely be a good option as it leverages the prior 7-day history of a user to reduce variance.
- Stratification works well when you are experimenting on existing users as well, and when the metrics are particularly skewed and predictable. This makes sure the “whales” in your experiment are properly balanced.
- Finding alternative or proxy metrics is a good strategy as well. Some metrics are really hard to get statistical signal on due to their high variability and low frequency. Revenue is a good example. Some good alternatives are:
- number of purchases
- proportion of users who make a purchase
- Both of those are statistically easier to work with and both metrics are likely correlated to revenue.
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