Sample Ratio Mismatch Calculator

Introduction to Sample Ratio Mismatch (SRM)

Sample Ratio Mismatch (SRM) is a critical concept in experimental design, particularly in A/B testing and data analysis. It occurs when the observed ratio of samples in different experimental groups significantly deviates from the expected ratio. This phenomenon serves as an early warning system that something may be wrong with your experiment design, implementation, or data collection process.

Why SRM Matters in Experimental Design

The importance of SRM cannot be overstated in the context of experimental validity. When you encounter an SRM, it typically indicates that:

• Your randomization process might be flawed - Proper randomization is essential for valid experimental conclusions.
• Selection bias may be present - Certain types of users might be systematically excluded from one variant.
• Technical issues could exist - Implementation errors might be affecting how users are assigned or tracked.
• Data collection might be inconsistent - Issues in logging or tracking could be creating discrepancies.

SRM in A/B Testing

In A/B testing, SRM is particularly concerning because it can invalidate your entire experiment. Consider a scenario where you're testing a new website design:

This 60/40 split instead of the intended 50/50 could indicate that some users are systematically being excluded from Variant B, perhaps due to browser compatibility issues or page load failures. If this is the case, any conversion rate differences might be due to the selection bias rather than the actual design changes.

Statistical Framework for SRM Detection

Detecting SRM requires a statistical approach, most commonly using the Chi-Square test of independence. This test helps determine if the observed allocation differences are due to random chance or if they indicate a systematic issue.

Common Causes of Sample Ratio Mismatch

Best Practices for Handling SRM

1. Early detection - Check for SRM as soon as your experiment starts running
2. Regular monitoring - Continue checking throughout the experiment duration
3. Segment analysis - Determine if SRM affects specific user segments (browsers, devices)
4. Root cause investigation - Systematically examine potential causes from the table above
5. Document findings - Keep records of SRM incidents and resolutions for future reference

SRM vs. Natural Variation

It's important to distinguish between statistically significant SRM and natural variation in sample distribution:

Impact on Business Decisions

Ignoring SRM can lead to costly business mistakes. Consider these scenarios:

• False positives - Incorrectly concluding a variation is better when it's not
• False negatives - Missing actual improvements due to biased data
• Wasted resources - Making changes based on invalid test results
• Repeated errors - Propagating flawed experiment designs in future tests

Advanced SRM Considerations

For more complex experimental designs, consider these additional factors:

• Users vs. Sessions - Always check SRM at the user level first, as session-level analysis can be misleading
• Multi-variant testing - Apply SRM checks to all variants individually
• Time-based analysis - Track SRM patterns over time to detect issues that might appear after experiment launch
• Cross-platform consistency - Ensure consistent assignment across different platforms and devices

Conclusion

Sample Ratio Mismatch is more than just a statistical anomaly—it's a critical indicator of experiment health. By understanding, detecting, and addressing SRM, you can ensure the validity of your experiments and the reliability of your business decisions. Remember that while some level of SRM occurs naturally in experiments, persistent or significant SRM requires investigation and resolution to maintain data integrity.

Sample Ratio Mismatch (SRM) occurs when the observed ratio of samples in different groups significantly differs from the expected ratio. This can indicate issues with randomization or data collection in experiments.