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🧪 Exposure Imbalance / Sample Ratio Mismatch (SRM) Debugging in Metrics Explorer
9/24/2025
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🧪 Exposure Imbalance / Sample Ratio Mismatch (SRM) Debugging in Metrics Explorer
What is SRM?
Sample Ratio Mismatch (SRM) happens when the share of users in experiment groups is different from what you expected. For example, if you set up a 50/50 split between control and treatment but the actual traffic is 60/40, that’s an SRM.
What is the SRM p-value?
The SRM p-value is a statistical measure that tells you whether the observed imbalance could have happened by chance.
A p-value above 0.01 generally means the imbalance is within expected random variation.
A p-value below 0.01 suggests the imbalance is unlikely due to chance and may warrant investigation.
What You Can Do Now
View SRM results and p-values across experiment groups in Metrics Explorer
Group results by different properties to identify potential causes of imbalance
Start from experiment exposure diagnostics and click on suggested properties to pre-apply them as group-bys in Metrics Explorer
How It Works
Metrics Explorer applies the SRM formula across experiment groups and shows the resulting p-value. From there, you can add group-bys (such as country, platform, or custom properties) to spot where imbalance is happening.
Experiment diagnostics also highlight properties that may be driving the imbalance. Clicking the icon next to one of these properties takes you into Metrics Explorer with that property already grouped, so you can continue the investigation seamlessly.
Impact on Your Analysis
This workflow makes it faster to detect and understand exposure imbalances. By moving directly from diagnostics to group-by analysis, you save time and get clearer visibility into which properties are linked to the imbalance.
Sample Ratio Mismatch debugging is available now across Cloud and Warehouse Native.
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