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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Can I force run updating metrics on an ongoing experiment in Statsig?

When updating log events for a feature gate in Statsig, there is no need to restart the feature gate to see the revised metrics data, as changes should take effect immediately. However, if the updated metrics are not reflecting as expected, it is advisable to verify that the events are being logged correctly.

To obtain cleaner data for metrics lifts after modifying the code for a log event, you can adjust the distribution of users between the control and experiment groups or 'resalt' the feature to reshuffle users without changing the percentage distribution.

For experiments with delayed events, setting the experiment allocation to 0% after the desired period ensures that delayed events still count towards the analytics. It is important to note that the sizes of variants cannot be adjusted during an ongoing experiment to maintain the integrity of the results.

To increase the exposure of a variant, the current experiment must be stopped, and a new one with the desired percentage split should be started.

In the context of managing experiments with Terraform, the status field can be updated to reflect one of four possible values: setup, active, decision_made, and abandoned, aiding in the management of the experiment's lifecycle.

For those utilizing Statsig Warehouse Native, creating an Assignment Source and a new experiment allows for the definition of the experiment timeline and subsequent calculation of results.

Statsig pipelines typically run in PST, with data landing by 9am PST, although enterprise companies have the option to switch this to UTC.

Statsig Cloud calculates new or changed metrics going forward, and Statsig Warehouse Native offers the flexibility to create metrics after experiments have started or finished and to reanalyze data retrospectively.

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What builders love about us

OpenAI OpenAI
Brex Brex
Notion Notion
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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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