MRR Lab

A/B Test Duration Calculator

Calculate how long your A/B test needs to run to reach statistical significance

Test Duration

Days Required

16

Daily Traffic

1,000

Sample Size Requirements

Per Variant

3,839

Total

7,678

Traffic Split

Control Group

50%

Variant Group

50%

Test Parameters

Minimum Detectable Effect

20.0%

Statistical Power

80.0%

Significance Level

95.0%

%
%
50% / 50%
Defaults are suitable for most tests

How It Works

  1. Enter your daily traffic volume
  2. Specify your baseline conversion rate
  3. Set your minimum detectable effect
  4. Adjust traffic split ratio (default 50/50)
  5. Get estimated test duration and sample size requirements

Understanding the Results

The calculator provides:

  • Required test duration in days
  • Sample size requirements per variant
  • Traffic allocation breakdown
  • Statistical parameters and their impact

Statistical Parameters

  • Significance Level: Default 95% confidence (α = 0.05)
  • Statistical Power: Default 80% (β = 0.2)
  • Effect Size: Your minimum detectable effect
  • Traffic Split: How traffic is divided between control and variant

Common Use Cases

  1. Website Optimization: Test new layouts, CTAs, or content
  2. Pricing Tests: Compare conversion rates between pricing plans
  3. Feature Launches: Test new features with a subset of users

Best Practices

  1. Consider Business Cycles:

    • For B2B, include full business weeks
    • For B2C, include full weeks to account for weekend patterns
    • Account for holidays and seasonal variations

  2. Traffic Allocation:

    • 50/50 split is most common and efficient
    • Uneven splits (e.g., 90/10) require longer duration
    • Consider impact on user experience

  3. Test Duration:

    • Minimum 2 weeks for most tests
    • Include full business cycles
    • Account for seasonality
    • Don't stop early based on initial results

  4. Sample Size Requirements:

    • Larger sample sizes for smaller effects
    • Consider your traffic constraints
    • Balance duration vs. effect size

Limitations

  • Assumes consistent daily traffic
  • Does not account for traffic variations
  • Best for binary outcomes (conversion/no-conversion)
  • Assumes normal distribution

Tips for Success

  1. Plan for Variations: Add buffer for traffic fluctuations
  2. Consider Seasonality: Account for known traffic patterns
  3. Be Patient: Don't stop tests early
  4. Monitor Health: Check for test validity regularly

Related Calculators

A/B Test Sample Size Calculator

Before determining test duration, you may want to calculate the required sample size first. The A/B Test Sample Size Calculator helps you determine how many total visitors you need to reach statistical significance, which is a key input for estimating test duration.

The Sample Size Calculator is particularly useful when:

  • You want to know if you have enough traffic to detect your desired effect
  • You need to determine feasibility before planning test duration
  • You're deciding between different effect sizes based on sample size requirements