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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
GENERAL

Understanding the difference between daily participation rate and one time event metrics in Statsig

In Statsig, the Daily Participation Rate and One Time Event metrics are used to track user behavior in experiments. The choice between these two metrics depends on the nature of the event you're tracking.

1. Daily Participation Rate: This metric is calculated as the total number of days that a user has the selected event, divided by the number of days the user is in the experiment. This is done for each user in the experiment. The mean event_dau, or the average active days per user, is then calculated by aggregating this average event_dau for each user in the experiment, with each user weighted equally. This metric is more suitable for events that are expected to occur repeatedly for a given user.

2. One Time Event: This metric is ideal for events that are only expected once per user, such as booking events. If the event is expected to occur only once per user during the experiment or holdout period, then the One Time Event metric would be suitable.

For longer experiments and holdouts, the choice of metric would still depend on the frequency of the event. If the event is expected to occur approximately once a month or less frequently, the One Time Event metric should be appropriate. However, if the event is expected to occur approximately weekly or more frequently, the Daily Participation Rate metric might be more appropriate as it captures recurring behavior.

When reviewing experiments, consider all related metrics:

- One-time events best capture the number of unique users who participated in the event. - Daily participation rate is an effective proxy for "how much" people are participating in the event. - Total events (event_count) is a better proxy for revenue or downstream metrics.

For holdouts, it can be helpful to use different rollups. For example, looking at one-time metrics for the 7-day or 28-day rollup would tell you what % of users participated (at all) within the last 7-day or 28-day window. This can be an effective way to get past the history issue.

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What builders love about us

OpenAI OpenAI
Brex Brex
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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.
OpenAI
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.
Brex
Karandeep Anand
President
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.
Notion
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.
SoundCloud
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.
Ancestry
Partha Sarathi
Director of Engineering
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