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 does Statsig differentiate data from different environments and can non-production data be used in experiments?

Statsig differentiates data from different environments by the environment tier you specify during the SDK initialization. You can set the environment tier to "staging", "development", or "production". By default, all checks and event logs are considered "production" data if the environment tier is unset.

Experiments and metrics primarily factor in production data. Non-production events are visible in diagnostics, but they are not included in Pulse results. This is because most companies do not want non-production test data being included. If you want to include these, you can log them as regular events. However, non-production data is filtered out of the warehouse and there is no other way to include it.

When initializing, if you add { environment: { tier: "production" } }, this would set your environment to "production", not "staging" or "development". If you want to set your environment to "staging" or "development", you should replace "production" with the desired environment tier.

Pulse results are only computed for “Production” tier events. To see Pulse results, you need: • An experiment that is “Started” (aka, enabled in production) • Exposures & events in production tier • Exposures for users in all test groups • Events/metrics associated to users in test groups

As long as you have initialized using { environment: { tier: "production" } }, your Pulse will compute. This means that even if your code is deployed to staging, as long as you initialize with the production tier, you will be able to see Pulse results.

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What builders love about us

OpenAI OpenAI
Brex Brex
Notion Notion
SoundCloud SoundCloud
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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