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 interpret pre-experiment results in experimentation data

When reviewing experimentation results, it is crucial to understand the significance of pre-experiment data. This data serves to highlight any potential pre-existing differences between the groups involved in the experiment. Such differences, if not accounted for, could lead to skewed results by attributing these inherent discrepancies to the experimental intervention.

To mitigate this issue, a technique known as CUPED (Controlled-Experiment Using Pre Experiment Data) is employed.

CUPED is instrumental in reducing variance and pre-exposure bias, thereby enhancing the accuracy of the experiment results. It is important to recognize, however, that CUPED has its limitations and cannot completely eliminate bias. Certain metrics, particularly those like retention, do not lend themselves well to CUPED adjustments.

In instances where bias is detected, users are promptly notified, and a warning is issued on the relevant Pulse results. The use of pre-experiment data is thus integral to the process of identifying and adjusting for pre-existing group differences, ensuring the integrity of the experimental outcomes.

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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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