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GENERAL

Can we run several parallel and overlapping experiments within a single layer allocation for different teams?

Date of slack thread: 7/30/24

Anonymous: Hey Statbot, can we run several parallel tests using experiment ID within a single layer ID?

Anonymous: Hey! Thanks! Sorry if I was not clear. but the question is if we can run several experiments within a layer in parallel and overlapping? Here is the scenario I am trying to achieve:

  1. Layer Allocation between teams: I allocate 70% of the total traffic to Team A and 30% of the total traffic to Team B.
  2. Overlapping Nested Experiments: Within their respective allocations, I want to allow both Team A and Team B to run their own overlapping experiments. This means that within the 70% traffic allocated to Team A, multiple experiments (Experiments A1, A2, A3) can run in parallel and overlap. Similarly, within the 30% traffic allocated to Team B, multiple experiments (Experiments B1, B2, B3) can run in parallel and overlap. My goal is to enable different teams to have their own allocation of traffic, ensuring they can run multiple overlapping experiments safely and independently within their respective allocated traffic segments.

Is there a way to achieve this?

Tore (Statsig): Use getLayer with the layerID instead. Layers are best for running multiple experiments on the same set of parameters.

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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.
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Dave Cummings
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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.
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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.
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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.
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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.
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Partha Sarathi
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