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

Is there a difference between running experiments in the same layer vs different layers?

When conducting multiple experiments, the decision to run them in the same layer versus different layers has significant implications.

Placing experiments in the same layer ensures that there is no overlap between participants in different experiments. This is beneficial for eliminating interaction effects between experiments, as no user will be part of more than one experiment at a time. However, a critical consideration is that using layers divides the user base, which can substantially reduce the experimental power and sample size.

This division of the user base means that, at a minimum, the number of participants in each experiment is halved. Consequently, this reduction can limit the number of experiments that can be conducted simultaneously and may prolong the duration required to achieve statistically significant results.

When experiments are run in a layer and thus have a smaller sample size, any effects observed while the experiment is running will also be smaller.

For a more in-depth discussion on the topic, including the trade-offs between isolating experiments and embracing overlapping A/B tests, refer to the article Embracing Overlapping A/B Tests and the Danger of Isolating Experiments.

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What builders love about us

OpenAI OpenAI
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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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