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 resalting a feature gate recommended to remove preexperimental bias?

When considering the removal of preexperimental bias from a feature that was rolled out with limited initial data, resalting the feature gate is not specifically recommended for this purpose.

Pre-experimental bias can occur by chance and may not always significantly impact the results of an experiment. If the overall difference in metrics due to this bias is small, it might not meaningfully affect the interpretation of the results.

In such instances, the bias can be acknowledged as a cautionary note, and the affected metrics should be treated with a degree of skepticism. To address concerns about pre-experimental bias, it is advisable to utilize the 'Days Since Exposure' view.

This tool assists in identifying any novelty effects and pre-existing experiment effects. Should bias be detected, users will receive a notification, and a warning will be displayed on the relevant Pulse results. This approach allows for a more informed analysis of the feature's impact without the need to resalt the feature gate.

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What builders love about us

OpenAI OpenAI
Brex Brex
Notion Notion
SoundCloud SoundCloud
Ancestry Ancestry
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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