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.
Statsig FAQs

How and when does user allocation to a holdout group occur in Statsig?

In Statsig, user allocation to a holdout group is a predetermined process that occurs automatically when the holdout group is created. This allocation is based on a cryptographic technique using a salt, which is applied to the unit_id (userID or stableID) of the user being evaluated.

As a result, the assignment to the holdout group is stable as long as the user retains the same unit_id. There is no specific trigger required for this assignment to take place. However, it is important to note that while the assignment is predetermined, the actual exposure of a user to a holdout group only occurs when the user encounters a feature gate or experiment that is part of the holdout.

This means that Statsig only becomes aware of the users when they are exposed to a gate or experiment. It is also crucial to understand that if you are managing the assignment for holdout yourself, you will need to manage exposure logging. This approach is not commonly recommended for holdouts due to the risk of polluting the holdout data.

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What builders love about us

OpenAI OpenAI
Brex Brex
Notion Notion
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Ancestry Ancestry
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.
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.
Karandeep Anand
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.
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.
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.
Partha Sarathi
Director of Engineering
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