A/B Test Significance & Lift Calculator
Calculate statistical significance and lift for A/B tests. Compare baseline vs variant using conversion rates or continuous metrics, compute p-values, confidence intervals, and determine if your experiment shows meaningful results.
A/B Test Significance Calculator
Enter your experiment data to calculate statistical significance, lift, p-values, and confidence intervals. Supports both conversion rate tests and continuous metric comparisons.
Understanding A/B Test Significance
What is A/B Testing?
A/B testing (also called split testing) is a method of comparing two versions of something to determine which performs better. In digital experimentation, you randomly assign users to either a control group (baseline/original) or a treatment group (variant/new version) and measure a key metric like conversion rate or revenue.
The goal is to determine whether any observed difference between the groups is statistically significant—meaning it's unlikely to have occurred by random chance alone.
Proportion Tests (Conversion)
Used when measuring binary outcomes: did the user convert or not?
- • Click-through rates
- • Sign-up rates
- • Purchase conversion
- • Form completion rates
Mean Tests (Continuous)
Used when measuring continuous numeric outcomes.
- • Revenue per user
- • Time on page
- • Number of items purchased
- • Session duration
Key Statistical Concepts
p-value
The probability of observing a difference as extreme as the one measured, assuming there is actually no true difference (the null hypothesis). A small p-value (typically < 0.05) suggests the observed difference is unlikely due to chance alone.
Confidence Interval
A range of values that likely contains the true difference between groups. A 95% confidence interval means if we repeated the experiment many times, about 95% of the intervals would contain the true effect.
Statistical Significance
When the p-value is below your chosen threshold (alpha, often 0.05), the result is "statistically significant." This means you can reject the hypothesis that there's no difference. It does NOT mean the effect is large or practically important.
Lift
The relative improvement of the variant over the baseline, expressed as a percentage. For example, a 10% lift means the variant performs 10% better than the baseline on the measured metric.
One-sided vs Two-sided Tests
Two-sided Test
Tests whether there's any difference (positive or negative) between groups. More conservative and commonly used.
Use when: You want to detect changes in either direction.
One-sided Test
Tests whether the variant is specifically better than the baseline. More powerful for detecting effects in one direction.
Use when: You only care if the variant improves performance.
Common Pitfalls to Avoid
- 1.Peeking: Checking results repeatedly and stopping early when you see significance inflates false positive rates.
- 2.Multiple Comparisons: Testing many metrics or variants without correction increases the chance of false positives.
- 3.Under-powered Tests: Small sample sizes may fail to detect real effects (false negatives).
- 4.Confusing Significance: Statistical significance doesn't mean practical importance—always consider effect size.
- 5.Selection Bias: Non-random assignment or user self-selection can invalidate results.
When to Consult a Statistician
This calculator provides educational intuition about A/B test results. For high-stakes decisions, complex experimental designs, or regulatory requirements, always consult with qualified professionals:
- • Multi-arm or multi-factor experiments
- • Sequential or adaptive testing designs
- • Clinical trials or medical interventions
- • Financial or regulatory decision-making
- • Long-term effect estimation
Frequently Asked Questions
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