A/B Test Significance Calculator
Calculate statistical significance, p-values, and get AI-powered recommendations for your A/B tests
🧪 A/B Test Significance Calculator
Enter your test data to calculate statistical significance and determine your winner
AControl (Original)
Conversion Rate
2.00%
BVariation (Test)
Conversion Rate
2.50%
Understanding A/B Test Statistical Significance
A/B testing is the gold standard for conversion optimization, but only 1 in 7 tests produces a winner. Understanding statistical significance is crucial to avoid false positives and wasted development effort.
Statistical significance tells you whether observed differences are real or just random chance. A 95% confidence level means there's only a 5% probability your results are due to luck. Never stop a test early - peeking at results mid-test inflates your error rate and leads to bad decisions.
Key Metrics Explained:
The probability that your results are due to chance. Lower is better. P-value < 0.05 means statistically significant at 95% confidence.
Measures how many standard deviations your result is from the mean. Higher absolute values indicate stronger significance.
The range where the true conversion rate likely falls. Narrower intervals = more precise estimates.
Need at least 1,000+ conversions per variation for reliable results. Small samples lead to false conclusions.
A/B Testing Social Proof Elements
Customer reviews and testimonials are among the highest-impact elements to test
High-Impact Test Ideas
Case Study: Fine Fit Sisters
Test: Added social proof (customer photos, reviews, metrics) to first fold on mobile
"People trust products that have been tried and approved by others. Social proof is a key element for building credibility."
Want to Test Social Proof on Your Site?
Spokk makes it easy to collect customer reviews and video testimonials, then display them optimally on your site
Start Collecting Reviews - FreeFrequently Asked Questions
What is statistical significance in A/B testing?
Statistical significance quantifies whether observed differences between variations are likely due to real effects rather than random chance. A 95% significance level means you can be 95% confident the results are not due to chance.
How long should I run an A/B test?
Run your test for at least 1-2 weeks to account for weekly behavior patterns. The exact duration depends on your traffic and required sample size (typically 1,000+ conversions per variation), but tests should continue until reaching statistical significance or predetermined sample size.
What is a p-value and how do I interpret it?
A p-value represents the probability of observing your results (or more extreme) if there's truly no difference between variations. A p-value below 0.05 indicates statistical significance at the 95% confidence level. Lower p-values mean stronger evidence of a real difference.
Can I stop my test early if I see significant results?
No. Stopping tests early when you see significance leads to false positives. This practice, called 'peeking,' inflates your error rate and leads to bad decisions. Always wait for your predetermined sample size or duration.
What's the difference between one-sided and two-sided tests?
Two-sided tests check for any difference (better OR worse), while one-sided tests only check for improvement. Two-sided is recommended as default because it accounts for the possibility of negative impact and provides more reliable results.
How many visitors do I need for an A/B test?
It depends on your baseline conversion rate and minimum detectable effect. Generally, aim for at least 350-1,000+ users per variation, with at least 100 conversions per variation. Use our sample size calculator for precise recommendations.
What should I A/B test on my website?
Start with high-impact elements: headlines, CTAs, pricing display, and social proof (reviews/testimonials). Social proof tests typically show 25-80% improvements. Test one element at a time for clear attribution.
What's a Sample Ratio Mismatch (SRM)?
SRM occurs when your traffic split deviates significantly from the expected ratio (e.g., 45/55 instead of 50/50). This indicates a potential technical issue with your test implementation that could invalidate results. Always investigate SRM warnings before trusting test outcomes.
Ready to Run Winning A/B Tests?
Start by testing the #1 conversion driver: social proof. Spokk makes it effortless to collect and display customer reviews and video testimonials.