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A/B Test Duration Calculator
Running tests too short? Making decisions on noise? Calculate exactly how long your A/B test needs to run for reliable results.
Your Test Requirements
Total Sample Needed
-
users across both variants
Minimum Test Duration
-
days to reach significance
📊 What This Means
• Your baseline: -
• Detectable lift: -
• If variant wins, conversion will be at least: -
• Statistical power: 80% (industry standard)
⚠️ Common A/B Testing Mistakes
- • Stopping early: "It's winning!" - wait for full duration
- • Peeking: Each peek increases false positive risk
- • Small traffic: You may need to accept larger MDE
- • Ignoring segments: Overall win may hide segment losses
Need help designing experiments that actually move the needle?
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The Math
This calculator uses the standard sample size formula for two-proportion tests with:
- Two-tailed test (detecting improvements OR declines)
- 80% statistical power (industry standard)
- Equal traffic split between control and variant
Why Sample Size Matters
Smaller effects need larger samples to detect reliably. A 5% → 5.5% lift is real, but you need thousands of users to distinguish it from random noise. A 5% → 6.5% lift is easier to spot with fewer users.
When to Adjust
- • Low traffic? Accept a larger MDE (15-20%) or run longer
- • High stakes? Use 99% confidence to reduce false positives
- • Exploratory? 90% confidence is fine for initial tests