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 to change the default value of a parameter in a layer in Statsig?

In Statsig, you can manually change the default value of a parameter in a layer. This can be done from the layer's homepage. Here's how:

1. Navigate to the layer's homepage.

2. Locate the parameter whose default value you want to change.

3. Update the default value as needed.

Please note that parameters in a layer are designed to exist across multiple experiments in the layer. Therefore, the layer's homepage is the place where they live.

Remember, the ability to change the default value of a parameter in a layer allows you to manage your Statsig integration code more efficiently. It's one of the benefits of using default parameters in Statsig.

If you're looking for a way to change the default value at the level of an experiment in the layer, where the parameter is first defined, please note that this is not currently possible. The parameters are defined at the layer level, not at the experiment level.

We hope this information helps you in managing your parameters in Statsig. If you have any more questions, feel free to reach out.

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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
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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.
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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
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