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A/B Test Duration Calculator

Calculate how long you need to run your A/B test to achieve statistical significance. Avoid ending tests too early or wasting time on inconclusive experiments.

%

Your current conversion rate

%

Relative improvement you want to detect

Average daily visitors to the test page

Including control (A/B = 2, A/B/C = 3)

Confidence that results are not due to chance

Probability of detecting a true effect

Test Duration Required

Based on your parameters

56
days minimum
(8 weeks)
Sample Per Variant
27,791
Total Sample Needed
55,582
Traffic Per Variant/Day
500
Target Conversion Rate
3.60%

Expected Results at Test Completion

Control
834 conversions
at 3.00% rate
Variant (if winning)
1,000 conversions
at 3.60% rate (+20%)
How this is calculated:

The sample size is calculated using the formula for comparing two proportions, accounting for your baseline rate (3.00%), minimum detectable effect (20%), significance level (95%), and statistical power (80%).

Understanding A/B Test Duration

One of the most common mistakes in A/B testing is ending tests too early. This "peeking problem" leads to false positives and poor business decisions. Use this calculator to determine the proper test duration before you start.

Key Concepts

Minimum Detectable Effect (MDE)

The smallest relative change you want to be able to detect. Smaller effects require larger sample sizes. If your baseline is 3% and you set MDE to 20%, you're looking to detect a change to 3.6% or higher.

Statistical Significance

The probability that your results are not due to random chance. 95% is the industry standard, meaning there's only a 5% chance of a false positive.

Statistical Power

The probability of detecting a real effect when one exists. 80% power means you have an 80% chance of detecting a true winner.

Common A/B Testing Mistakes

  • Stopping too early: Never end a test just because you see significance. Complete the full duration.
  • Running too many variants: Each additional variant increases required sample size. Start with A/B before A/B/C.
  • Ignoring business cycles: Run tests for full weeks to capture day-of-week effects.
  • Too small MDE: Detecting a 1% improvement requires massive sample sizes. Focus on bigger wins.
  • Not pre-calculating duration: Always calculate required duration before starting a test.

When Your Test Duration is Too Long

  • Increase your MDE: Focus on larger, more impactful changes.
  • Increase traffic: Promote the test page or run the test on a higher-traffic page.
  • Reduce variants: Test A vs B before adding more variants.
  • Lower significance: 90% significance may be acceptable for some decisions.
  • Use a different metric: Higher-frequency metrics (like clicks) need smaller samples than conversions.

Need More Traffic to Test?

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