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A/B Test Significance Calculator

Calculate if your email A/B test results are statistically significant. Enter your sample sizes and conversion rates to know if you can trust your test results or need more data.

A/B Test Significance Calculator

Determine if your test results are statistically significant

A
Control Variation

B
Test Variation

Understanding the results

  • 95% confidence means 5% chance results are due to random chance
  • P-value < 0.05 indicates statistical significance
  • Z-score measures standard deviations from the mean
  • Larger sample sizes give more reliable results
  • Don't end tests early - wait for significance

About this tool

Not all improvements are real—randomness can make weaker options look better by chance. This calculator tells you if your test results are statistically significant before you make major decisions. Before running tests, optimize your subject lines and preheaders for baseline improvement. Track results with our UTM parameter builder and analyze overall campaign performance with our email calculator to contextualize your test wins.

Frequently Asked Questions

Statistical significance tells you the probability that your A/B test results are real, not due to random chance. A 95% significance level means there's only a 5% chance the results occurred randomly.

95% (p-value < 0.05) is the standard for most email tests. For high-stakes decisions (like changing your entire email strategy), use 99%. For quick iterations, 90% may be acceptable.

It depends on your baseline conversion rate and the minimum difference you want to detect. Generally, you need at least 1,000 recipients per variation for open rate tests and more for click/conversion tests.

Common reasons: sample size too small, the true difference between variations is tiny, or high variance in your data. Either run the test longer, test bigger changes, or accept that the variations perform similarly.

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