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
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Does including non-interacting users in an A/B test sample size affect p-values?

In A/B testing, the inclusion of non-interacting users in the sample size does not inherently affect the p-values, provided that the metric being evaluated is relevant to the feature being tested. When non-interacting users are included in the sample, it can dilute the experiment results by adding a number of zeros to both the control and test groups.

This dilution can decrease the experimental power, meaning that a larger sample size or a longer experiment duration may be required to reach statistical significance, given the underlying effect size.

However, the directionality of the results should remain correct, and the zeros should be evenly distributed between the groups, maintaining the integrity of the experiment results.

It is recommended to expose users to the experiment as close as possible to the point where their experiences diverge, to avoid overexposure and to increase the efficiency of the experiment.

f the overexposed users do not contribute to the metric, they do not introduce any noise and therefore do not affect the experimental power.

It is important to note that if the treatment affects the rate at which people reach the relevant step in the process, manual filtering to users who reached that step could introduce bias and should be approached with caution.

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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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