Check traffic distribution balance in A/B tests. Online tool for fast and accurate SRM diagnostics. Just enter the numbers and get the result.
Chi-Square Value
1.2195
P-value
p = 0.2695
Uses chi-squared statistics to calculate the degree of mismatch between expected and actual traffic distribution in A/B tests.
Instantly detects when traffic distribution deviates from planned proportions, helping maintain test integrity.
Provides color-coded status indicators (normal, warning, critical) for quick assessment of distribution issues.
The SRM calculator helps detect issues in A/B testing experiments by analyzing traffic distribution between test groups. It uses chi-squared statistics to identify statistical anomalies that may affect test validity.
This tool is essential for data specialists, analysts, and marketers who need to ensure the integrity of their A/B tests. It helps identify when traffic distribution deviates from expected proportions.
The calculator provides clear interpretation of results with color-coded status indicators, making it easy to understand when intervention is needed to maintain test quality.
Sample Ratio Mismatch (SRM) is a statistical error that occurs when samples in A/B tests do not match each other. This can lead to inaccurate results and incorrect conclusions.
The SRM calculator uses statistical methods to estimate SRM based on sample size, baseline conversion, and significance level. It shows when SRM can be critical.
Use the calculator to estimate SRM in A/B tests. Set the sample size, baseline conversion, and significance level, and the tool will indicate when SRM can be critical.
Yes, the calculator is suitable not only for classic A/B tests but also for multivariate experiments. It will help identify an imbalance in distribution among multiple groups.
The Sample Ratio Mismatch (SRM) calculator is ideal for A/B tests with high observation costs, where it is important to save resources. It is also useful for quickly obtaining results in marketing campaigns.
Common causes include errors in traffic splitting implementation (bugs in the code), redirection issues, incorrect cookie exclusion, bots or crawlers distorting data, and user exclusion from certain groups.
Yes, a significant SRM (indicated by a low p-value) is a strong sign that something is wrong with how your A/B test was set up or implemented. Ignoring SRM can lead to invalid test results and faulty business decisions.
Fixing SRM typically involves a deep investigation into the A/B test implementation code, checking traffic splitting logic, ensuring user exclusions are properly applied, and analyzing traffic for anomalies (e.g., bot traffic).
