A/B Test Calculator

Stop Guessing. Start Testing.

Perform rigorous statistical significance testing for your conversion experiments. Know with mathematical certainty whether your changes actually improve performance.

P-Value Statistical significance

Confidence Intervals Range of true effect

Sample Size Required visitors

Power Analysis Detection probability

Control A

3.5%

Variant B

4.2%

+20% Lift (p < 0.05)

⚡ How It Works

📊

Input

Enter test data

→

📈

Analyze

Statistical tests

→

🎯

Decide

Clear winner

Companies using A/B testing see 25% higher conversion rates

Source: VWO Industry Report

🛠 Key Benefits

Avoid False Positives: Stop implementing changes that don't actually help
Detect Real Winners: Identify improvements with statistical confidence
Right-Size Tests: Calculate exact sample sizes needed
Save Resources: Don't run tests longer than necessary
Professional Reports: Export results for stakeholder presentations

🚀 Statistical Methods

Z-Test: For comparing conversion proportions
Chi-Square: Test of independence
Confidence Intervals: Range of true effect size
Power Analysis: Probability of detecting effects
Bonferroni Correction: Multiple comparison adjustment

🏢 Who Uses This

E-commerce SaaS Marketing Teams Product Managers Growth Teams UX Researchers

📦 What You Get

Web-Based Tool User Manual Sample Data Export Options

Ready to Test Smarter?

Dr. Koray Cosguner

Founder & Principal Consultant
Associate Professor of Marketing

miaow.consulting@gmail.com

Kelley School of Business

#1 Online MBA · #4 UG Marketing (U.S. News)

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