Analytics & Event Tracking

Sample Size Calculator

Calculate the required sample size for your A/B tests to reach statistical significance.

Test Parameters

Enter your experiment details to calculate the required sample size.

Baseline Conversion Rate (%)

Your current conversion rate

Minimum Detectable Effect (%)

Smallest improvement you want to detect

Confidence Level (%)

Typically 95%

Statistical Power (%)

Typically 80%

Results

Per Variation

8,161

users needed

Total Sample Size

16,322

across both variations

Target Conversion Rate

6.00%

(20.0% relative lift)

Interpretation

•You need 8,161 users per variation (Control and Variant) to detect a 20% improvement.
•This assumes your baseline conversion rate is 5% and you want to detect if it improves to 6.00%.
•With 95% confidence and 80% power, you have a good chance of detecting this effect if it exists.
•Recommendation: Run the test for at least 1-2 full weeks to account for weekly traffic patterns.

Understanding the Metrics

Baseline Conversion Rate: Your current conversion rate before making any changes. This is your Control group's expected performance.
Minimum Detectable Effect (MDE): The smallest relative improvement you want to reliably detect. A 20% MDE means you want to detect if your variant improves conversion by 20% (e.g., from 5% to 6%).
Confidence Level: The probability that your result is not due to random chance. 95% is standard, meaning there's only a 5% chance of a false positive.
Statistical Power: The probability of detecting an effect when it actually exists. 80% power means you have an 80% chance of detecting the MDE if it's real.

Learn More in The Course

This tool is derived from Module 1 of "The Ultimate Growth Engineering Course." Learn the complete experimentation framework, how to design better tests, and interpret results like a pro.

Explore the Course