A/B Test Statistical Significance Calculator Calculate statistical significance for A/B tests with p-value, confidence level, and lift.

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A/B Test Calculator

Calculate statistical significance for A/B tests with p-value, confidence level, and lift.

Enter Variant A data

Enter visitors and conversions for the control variant.

Enter Variant B data

Enter visitors and conversions for the treatment variant.

View results

See confidence level, p-value, lift, and the winning variant.

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What Is A/B Test Calculator?

The A/B Test Calculator determines whether the difference between two test variants is statistically significant or due to random chance. Enter the visitor count and conversions for each variant to get: statistical confidence level, p-value, relative lift, z-score, and a clear verdict on whether you have a significant winner. The tool uses a two-proportion z-test, the standard statistical method for comparing conversion rates. A result is considered significant at the 95% confidence level (p < 0.05), meaning there's less than a 5% probability the difference occurred by chance.

Why Use A/B Test Calculator?

Standard two-proportion z-test methodology
Clear significance verdict at 95% confidence
Shows p-value, z-score, and conversion rates
Relative lift calculation between variants
Visual confidence indicator

Common Use Cases

Landing Page Tests

Compare conversion rates between page variations.

Email Marketing

Test subject lines, CTAs, and email designs.

Ad Creative

Determine which ad creative drives more conversions.

Social Media

Measure engagement differences across post formats.

Technical Guide

The calculator uses the two-proportion z-test, which compares the conversion rates of two independent groups. The test statistic z = (p1 - p2) / sqrt(p_pooled × (1 - p_pooled) × (1/n1 + 1/n2)), where p_pooled = (x1 + x2) / (n1 + n2). The p-value is calculated from the z-score using a normal distribution approximation. For reliable results, aim for at least 1,000 visitors per variant and let tests run for at least 1-2 full business cycles (typically 1-2 weeks). Pre-calculate required sample size before starting a test to ensure adequate statistical power.

Tips & Best Practices

1
Aim for at least 1,000 visitors per variant for reliable results
2
Let tests run for at least 1-2 full weeks to account for daily and weekly patterns
3
Only change one variable at a time for clear causal attribution
4
Don't peek at results early — this inflates false positive rates
5
A 95% confidence level means there's still a 5% chance the result is random

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Frequently Asked Questions

Q What does statistically significant mean?

It means the observed difference is unlikely to be due to random chance. At 95% confidence, there's less than a 5% probability the difference is coincidental.

Q How many visitors do I need?

Aim for at least 1,000 per variant. The smaller the expected difference, the larger the sample size needed.

Q What is a good p-value?

A p-value below 0.05 (5%) is the industry standard for statistical significance.

About This Tool

A/B Test Calculator is a free online tool by FreeToolkit.ai. All processing happens directly in your browser — your data never leaves your device. No registration or installation required.