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

Confusion about the A/B/n test tutorial on Statsig's website

The tutorial on setting up an A/B/n test using Statsig's Experiments feature uses two examples to illustrate different variants you might test in an experiment.

The first example, enable feature, is a boolean type feature gate that can be either true or false. This represents the feature being on or off and is a simple A/B test scenario where you're testing the impact of a feature being enabled vs. not enabled.

The second example, Circle/Square, is a multi-variant scenario where you're testing more than two variants. In this case, you're testing the impact of showing a circle vs. a square shape in your product.

The screenshots in the tutorial are meant to illustrate the process of setting up these tests in the Statsig console. If you're finding the tutorial confusing, it is recommended to follow along in the Statsig console as you read. This should help clarify the process.

The tutorial is designed to use an A/A test as an example to show you how to set up any A/B test easily, of which A/A test is a special case. However, we acknowledge that it can seem a little confusing and we are committed to improving it for the future.

If you have any specific questions about the tutorial or any other aspect of using Statsig, feel free to ask. We're here to help.

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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.
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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.
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