Sample Ratio Mismatch Calculator (A/B Test SRM Check)
You planned a 50/50 test, but 12,000 users saw A and 12,700 saw B. Is that random noise or a bug in your assignment? A sample ratio mismatch (SRM) is a split imbalance too large to be chance, and when it happens the whole experiment is usually compromised: something is filtering, redirecting or mis-bucketing users before they reach one variant. Enter your intended split and the users each variant actually received, and this runs a chi-square goodness-of-fit test to tell you whether the result is safe to trust.
Check your traffic split
Expected split is your intended weighting (it does not need to add up to exactly 100; it will be normalised). Users seen is the raw count of visitors bucketed into each variant.
The convention among experimentation teams is to flag an SRM when the chi-square p-value drops below 0.001. That threshold is deliberately strict: a genuine mismatch points to a data or assignment fault, not a marginal effect, so you want very few false alarms. If this tool flags a mismatch, do not read the conversion numbers. Find the cause first: bot filtering hitting one variant, a redirect that loses users, uneven caching, a broken randomisation seed, or an analytics tag that fires on only one side.
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