Platform
Resources
Frequently Asked Questions
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.
Join the #1 experimentation community
Connect with like-minded product leaders, data scientists,
and engineers to share the latest in product experimentation.
What builders love about us
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.
OpenAI
Dave Cummings
Engineering Manager, ChatGPT
Brex's mission is to help businesses move fast. Statsig is now helping our engineers move fast. It has been a game changer to automate the manual lift typical to running experiments and has helped product teams ship the right features to their users quickly.
Brex
Karandeep Anand
President
At Notion, we're continuously learning what our users value and want every team to run experiments to learn more. It’s also critical to maintain speed as a habit. Statsig's experimentation platform enables both this speed and learning for us.
Notion
Mengying Li
Data Science Manager
We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration. We wanted a complete solution rather than a partial one, including everything from the stats engine to data ingestion.
SoundCloud
Don Browning
SVP, Data & Platform Engineering
We only had so many analysts. Statsig provided the necessary tools to remove the bottleneck. I know that we are able to impact our key business metrics in a positive way with Statsig. We are definitely heading in the right direction with Statsig.
Ancestry
Partha Sarathi
Director of Engineering