The P-Value represents the probability of obtaining test results at least as extreme as the results actually observed, under the assumption that the null hypothesis is correct. A lower P-Value indicates stronger evidence in favor of the alternative hypothesis.

Should I use a 90%, 95%, or 99% confidence level?

The industry standard for digital A/B testing is usually 95%. However, if you are testing highly risky or costly changes, you might want to use 99% to be absolutely certain.

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Free A/B Testing Significance Calculator

Our A/B Testing Calculator helps digital marketers, product managers, and CRO experts accurately determine if their experiments have reached statistical significance. By comparing the conversion rates of your Control Group (A) and Variation Group (B), the tool instantly calculates the Z-Score and P-Value using a standard two-tailed hypothesis test.

Why is Statistical Significance Important?

When running A/B tests on your website or app, it is crucial to ensure that any observed uplift in conversions is due to actual changes in user behavior and not just random chance. A confidence level of 95% means there is only a 5% probability that the results occurred purely by chance.