A/B Test Significance Calculator
Calculate the statistical significance of your A/B test results. Determine if your variation is a real winner or just random chance.
Control (A) - Original
Conversion Rate
3.00%
Variation (B) - Challenger
Conversion Rate
3.70%
Test Results
Statistical Confidence
94.8%
Relative Lift
+23.33%
p-value
0.0518
How to Interpret
- •A result is statistically significant when confidence is 95% or higher (p-value < 0.05).
- •A positive lift means Variation B outperformed the Control A.
- •If not significant, you need more data or the difference is too small to detect.
- •Aim for at least 1,000 visitors per variation for reliable results.
Frequently Asked Questions
What is statistical significance in A/B testing?
Statistical significance tells you whether the difference between your control and variation is likely due to your changes rather than random chance. A result is typically considered significant when the confidence level is 95% or higher (p-value < 0.05).
How long should I run an A/B test?
Run your A/B test until you reach statistical significance (95% confidence) or for at least 1-2 weeks to account for day-of-week variations. You need sufficient sample size - typically at least 1,000 visitors per variation for reliable results.
What can I A/B test on social media?
You can A/B test many elements: post copy, images vs videos, posting times, hashtag strategies, call-to-action text, ad creative, landing pages, caption length, and content formats. Test one variable at a time for clear results.
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