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If you enable streaming for your SDK, the changes to the feature flag rules will propagate instantly

My concern with splitting the entire user base is that our experiment only affects a small subset of users based on a rare scenario. Therefore, we risk unbalanced sample sizes for the users who are actually affected by the split.

Hi Aaron, I'm going to speak with our Data Scientist about your most recent questions and I'll follow up once I have more info 🙂

This really depends on what you mean by segment. Do you mean an attribute that is set in the SDK that you maintain ownership of? If you: • set an attribute in the SDK whenever that event happens near them • target your experiment only if they have that attribute Then your experiment sample will only be made of people for who: • You set that attribute in your app based on whatever criterion you wanted, who then, while still having that attribute, hit the feature flag in the code path These people will stay in the experiment for data purposes even if their attribute value changes (e.g. you turn it off in the app because it has been X time since the event you care about). That is the desired behavior from an analysis perspective, but to make sure you're measuring the treatment effect, you might want to design your metrics to use "conversion windows" so that you only measure behavior in the X hours after experiment exposure.

Hi @helpful-application-7107, thanks for your thorough response. Could users end up in both groups in that case? Does the metric need to be mapped to the attribute instead of the user? See my question here: https://growthbookusers.slack.com/archives/C01T6Q1SVFV/p1700666364635739