A/B Testing Significance Calculator

A/B testing significance estimates whether the difference between a control and variant is likely to be real rather than random noise.

Most calculators need visitors or sample size and conversions for each variant. Some also include confidence level, test direction, or power settings.

The p-value is the probability of seeing a result at least this extreme if there were no true difference between variants. Lower p-values provide stronger evidence against the no-difference assumption.

No. Statistical significance should be reviewed with sample size, test power, business impact, test duration, and whether the result matches the original hypothesis.

Power is the chance that a test detects a real effect when one exists. Low power can make a useful lift look inconclusive.

Stopping early can inflate false positives. Define the minimum sample size, duration, and decision rules before reading the result.