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What sample-ratio mismatch means
Sample-ratio mismatch occurs when the observed number of randomized units in experiment arms differs from the allocation configured for those arms by more than ordinary random variation would explain. The expected split need not be 50/50: a 70/30 test, for example, must be checked against 70/30.
SRM is a warning about experiment integrity, not proof that the treatment caused harm or that the result is automatically unusable. The imbalance may originate in assignment, product execution, event logging, data processing, or analysis. Microsoft Research describes SRM checks as a safeguard before effect analysis: “To prevent that harm, at Microsoft, every A/B test must first pass this Sample Ratio Mismatch (SRM) test before being analyzed for its effects.” The sentence appears in its September 14, 2020 article, “Diagnosing Sample Ratio Mismatch in A/B Testing.”
Choose the randomization unit
Randomize the entity that matches the product journey and the outcome you plan to measure. The identifier examples below are platform-specific examples described by Statsig, not a universal rule about which ID every experiment should use.
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| Assignment unit | Useful when | Trade-off to account for |
|---|---|---|
| Signed-in user ID | The outcome is user-level and the experiment can assign people after sign-in. | A user ID can persist across sessions and devices after sign-in, but it cannot assign a visitor before sign-in. |
| Device-level stable ID | The experiment needs to include anonymous or first-time visitors on a device. | The ID is device-bound, so the same person may be treated as separate units on different devices. |
| Session ID | The outcome is contained within one visit and independent sessions are a reasonable basis for the experiment. | A person can receive different variants in different sessions; this is unsuitable when cross-visit behavior or outcomes matter. |
Before choosing, check whether the ID persists for the required duration, whether anonymous reach matters, whether the outcome is measured per user, device, or session, and whether IDs can be null, duplicated, or regenerated. Document any fallback for missing IDs; silently switching identifiers can change who is randomized and how the data should be counted.
Set up assignment and measurement before launch
- Define eligibility and allocation. Record the population eligible for the test, exclusions and targeting rules, and each arm’s intended proportion. Keep these rules stable while the test runs. If you ramp traffic or change allocation, record when and how the proportions changed so the expected counts for each period are clear.
- Assign consistently at the chosen unit. A returning unit should keep its variant unless the design deliberately specifies another policy. Validate that the bucketing method uses the intended ID and that IDs do not churn, collide, or disappear without a documented fallback.
- Separate assignment from exposure. Assignment means a unit was allocated to an arm; exposure means it encountered the treatment or control experience under your defined exposure rule. Not every assigned unit necessarily sees the experience. Record the assigned variant and a clear exposure event so the analysis can answer the intended question.
- Make both arms observable. Verify that both variants can emit their assignment and exposure events, and that joins retain the same randomized unit. Automatic exposure logging can help, but it does not by itself validate the event pipeline.
- Check the integration end to end. Before relying on results, confirm that assignment is recorded, the intended variant renders, exposure is logged, identity is consistent, and arm-specific data collection works. Validate the counts and event flow for both arms, not just whether the experiment appears to be running.
Monitor the configured split, not an assumed 50/50 split
Count unique randomized units at the same level used for assignment, then compare the observed counts with the configured proportions. For a 70/30 allocation, use 70/30 as the expectation; a 50/50 check would be the wrong test. A chi-squared check is one documented way to assess whether observed counts are consistent with the configured split. Platforms may differ in how they display the check, calculate or alert on it, so there is no single vendor threshold established here as a universal standard.
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Statsig’s 2025 product update gives an illustrative example in which a configured 50/50 split appears as 60/40, and describes p-values and segment breakdowns as investigation aids. That example is not a general alert cutoff. Check the trend over time as well as the overall result: a transient fluctuation and a persistent or growing imbalance call for different investigation priorities.
Diagnose an alert across the data path
Start by confirming that the configured allocation, eligibility rules, and analyzed unit match the experiment’s actual setup. Then locate where the observed counts first diverge. Break the counts down by recorded dimensions such as platform, OS or browser, SDK version, region, and bot status; a mismatch concentrated in one segment can narrow the search.
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| Stage | What to investigate |
|---|---|
| Assignment | Incorrect bucketing, null or faulty IDs, identity churn, overlapping tests, manual overrides, or a traffic ramp that does not match the expected ratio. Microsoft Research identifies incorrect bucketing, faulty IDs, and carry-over effects as possible assignment-stage causes. |
| Product execution | Whether the treatment changes behavior in a way that affects which units remain observable, redirects users, or triggers client failures that prevent exposure logging. |
| Logging and processing | Variant-specific event loss, truncation, duplicates, mismatched joins, or inconsistent inclusion windows that undercount or overcount one arm. |
| Analysis | Filtering, segment definitions, or conditioning on behavior that occurs after assignment and selects the arms differently. |
Use assignment records, exposure events, and downstream data to identify the first point where counts stop agreeing; an aggregate alert alone usually cannot identify the root cause. Statsig documents time trends and segment breakdowns as diagnostic views. Microsoft Research describes differential diagnosis as synthesizing symptoms and eliminating implausible causes.
What to do when the mismatch persists
- Verify the comparison. Confirm that the alert uses the actual configured allocation, correct test period, eligible population, and randomized unit. Check that assignment counts are not being compared with exposure counts as if they were the same measure.
- Check whether the signal is sustained. Review the time trend and segment breakdowns, then inspect assignment and event-pipeline evidence for the affected units.
- Fix the identified cause. Correct the assignment, identity, execution, logging, processing, or analysis problem that explains the imbalance. If the cause is not identifiable, do not treat the effect estimate as trustworthy merely because a dashboard has produced one.
- Decide whether to restart or exclude. Statsig recommends investigating and commonly restarting after a fix. Excluding a segment may sometimes be considered when a problem is clearly isolated, but exclusion changes the population the result describes; document why it is defensible and how it changes the estimand.
- Report the decision and evidence. Record what caused the alert, what was changed, and whether the data used for the final analysis pass the team’s checks. Microsoft PlayFab guidance says analyses with unresolved SRM should not be used to make decisions. Optimizely cautions that imbalance alone does not automatically make an experiment unusable, so treat an alert as a reason to investigate rather than as a standalone verdict.
When stratification may help
Stratification balances the experiment across chosen characteristics before assignment. Statsig recommends considering it for low-volume or high-variance settings, such as B2B populations where a small number of large accounts can dominate a metric. For large consumer populations, its guidance says ordinary random assignment generally suffices.
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Statsig reports around 50% lower variance in its stratification simulations for the described settings. This is a vendor-reported simulation result, not an independent benchmark or a guarantee for a particular experiment. Stratification adds compute and setup work, and a lower allocation can reintroduce imbalance, so use it when the population and outcome justify that added complexity.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Further reading
For a broader treatment of experiment reliability, Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing by Ron Kohavi, Diane Tang, and Ya Xu (Cambridge University Press, 2020) includes a chapter titled “Sample Ratio Mismatch and Other Trust-Related Guardrail Metrics.” It is optional background, not a prerequisite for implementing assignment checks.
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