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
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How to exclude employee IP ranges from Statsig events and A/B tests

To ensure that employee IP ranges are excluded from all Statsig events and A/B tests, you can utilize holdouts. Holdouts are a feature that allows you to compare a group of users who are not exposed to any experiments against the broader population that is.

By creating a targeting gate with a regex that encapsulates the IP addresses of your employees, you can then use this targeting gate to establish a holdout. Setting this holdout to global will effectively exclude the specified IP ranges from any experiments, gates, or other features.

Additionally, if you maintain a list of user IDs for your employees, you can create a segment using these IDs instead of relying on IP addresses. This segment can then be referenced in the holdout to achieve the same exclusion effect.

It is important to note that holdouts are subject to analysis, which may lead to additional billing due to the insights they provide on the overall impact of experimentation efforts.

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What builders love about us

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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.
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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