A/B Test Significance Calculator
Calculate statistical significance for your pricing tests. Determine if your results are due to strategy or just random chance.
Control (A)
Variant (B)
Not Significant Yet
You can be 92.5% confident that the result is a consequence of the changes you made.
Interpreting the Z-Score
The Z-Score tells you how many standard deviations the result is from the mean.
Z > 1.96
This corresponds to >95% Confidence. The result is significant. You can declare a winner.
Z < 1.65
This corresponds to <90% Confidence. The result is likely noise. Do not make business decisions based on this.
The Lift
Even if significant, check the Lift %. A 0.1% lift might be statistically significant with enough traffic, but practically useless for your bottom line.
Execution Steps
Run your A/B test until you have at least 100 conversions per variation.
Enter the total visitors and total conversions for your Control (original price).
Enter the same data for your Variant (new price).
Check the 'Confidence Level'. If it is above 95%, your result is statistically significant.
Pro Strategy
- Never stop a test early just because you see a 'trend'. Wait for significance.
- If you have low traffic, test dramatic price changes (e.g., 20% vs 30%) rather than subtle ones (e.g., 20% vs 21%) to reach significance faster.
- Ensure you are tracking 'Unique Visitors', not 'Total Sessions', to avoid skewing data.
Core Concepts
Statistical Significance
The probability that the difference between your groups is not due to random error. In science and business, 95% is the standard threshold.
Confidence Interval
A range of values so defined that there is a specified probability that the value of a parameter lies within it.
Conversion Rate (CR)
The percentage of visitors who complete a desired action (purchase). Formula: (Conversions / Visitors) * 100.
Z-Score
A statistical measurement that describes a value's relationship to the mean of a group of values. Higher Z-scores indicate higher significance.
What is A/B Test Significance Calculator?
This calculator uses a Two-Tailed Z-Test for comparing two independent proportions (Conversion Rates). It assumes a normal distribution of data, which is standard for website traffic analysis.
Best For
- • Comparing conversion rates between two different price points.
- • Analyzing the results of landing page split tests.
- • Determining if a marketing campaign lift is real or noise.
Limitations
- • Requires a minimum sample size (usually 30+ conversions) to be accurate.
- • Does not account for 'Novelty Effect' (users clicking just because it looks different).
- • Assumes traffic quality is identical between both groups (randomized).
Alternative Methods
Bayesian Testing
Provides a probability of being the best option (e.g., 'B is 80% likely to be better'), which is often more intuitive than Frequentist P-values.
Multi-Armed Bandit
Dynamically routes traffic to the winning variation in real-time, maximizing revenue during the test.
Industry Applications
See how this methodology generates real revenue uplift in different sectors.
Ecommerce Checkout Page
High abandonment at shipping selection.
Tested 'Free Shipping on orders over $50' (A) vs 'Flat Rate $5' (B).
SaaS Free Trial Button
Low click-through on 'Start Trial'.
Tested 'Start Free Trial' vs 'Get Started for Free'.