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A/B Test Sample Size Calculator

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

About This Tool

When designing an A/B test, one of the most critical decisions is determining how many users you need in each variant to detect a meaningful difference. This calculator helps you estimate the required sample size based on your baseline conversion rate, minimum detectable effect, and desired statistical power.

Calculator

Baseline Conversion Rate (%) Current conversion rate of your control group

Minimum Detectable Effect (%) Relative change you want to detect (e.g., 10% means detecting 5% → 5.5%)

Significance Level (α) 0.01 (99% confidence) 0.05 (95% confidence) 0.10 (90% confidence) Probability of false positive (Type I error)

Statistical Power (1-β) 0.70 (70% power) 0.80 (80% power) 0.90 (90% power) 0.95 (95% power) Probability of detecting a real effect (1 - Type II error)

Results

Understanding the Parameters

• Baseline Conversion Rate: The current conversion rate of your control group (e.g., 5% of users complete a purchase)
• Minimum Detectable Effect (MDE): The smallest relative change you want to be able to detect. For example, if your baseline is 5% and MDE is 10%, you want to detect a change to 5.5%
• Significance Level (α): The probability of incorrectly concluding there's a difference when there isn't one (false positive). Common value: 0.05
• Statistical Power (1-β): The probability of correctly detecting a real difference. Common value: 0.80 (80% power)

Key Insights

• Higher baseline conversion rates require smaller sample sizes
• Detecting smaller effects requires larger sample sizes
• Higher power or lower significance levels require larger sample sizes
• Plan for longer test durations or higher traffic if your calculated sample size is large

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