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A/B Test Duration Calculator
Calculate exactly how long to run your A/B test for statistically significant results. Built for CRO professionals, marketers, and product teams who need reliable test timing.
Calculate Your Test Duration
Average daily visitors to your test page
Including control (minimum 2)
%
Your baseline conversion rate
%
Smallest change you want to detect
What is an A/B Test Duration Calculator?
An A/B test duration calculator determines how long you need to run your split test to achieve statistically significant results. It's a critical tool that prevents you from ending tests too early (missing real improvements) or running them too long (wasting time and resources).
Why Test Duration Matters in A/B Testing
- Statistical Validity: Ensures your test has enough data to detect meaningful differences
- Resource Optimization: Prevents wasting time on tests that have already reached significance
- Reliable Results: Avoids false positives from stopping tests too early
- Business Impact: Helps you implement winning variations faster with confidence
Key Factors That Affect Test Duration
Our calculator considers all critical factors that impact how long your A/B test should run:
- Traffic Volume: Higher traffic means faster results
- Baseline Conversion Rate: Lower rates require more visitors
- Minimum Detectable Effect: Smaller changes need longer tests
- Number of Variations: More variations require larger sample sizes
How to Use This A/B Test Duration Calculator
1. Enter Your Traffic Data
Input your average daily visitors and current conversion rate from your analytics tool.
2. Set Test Parameters
Choose your desired minimum detectable effect and statistical confidence level.
3. Get Your Timeline
Receive your recommended test duration with detailed sample size calculations.
A/B Test Duration Best Practices
Following these best practices ensures your A/B tests produce reliable, actionable results:
1. Never Stop Tests Early
Even if your test shows 'significant' results after a few days, continue running until you reach the calculated duration. Early results often show false positives due to novelty effects, random variance, and incomplete weekly cycles.
2. Account for Weekly Patterns
User behavior varies by day of the week. Always run tests for complete weeks (multiples of 7 days) to capture: Weekend vs. weekday traffic differences, B2B Monday morning spikes, Consumer weekend shopping patterns.
3. Consider Seasonality
Avoid running tests during unusual periods that might skew results: Major holidays or sales events, Industry-specific busy seasons, Marketing campaign launches, Technical issues or outages.
4. Monitor Test Health
While waiting for test completion, regularly check: Sample ratio mismatch (uneven traffic split), Technical implementation issues, Unusual traffic patterns, Data collection accuracy.
Advanced Test Duration Considerations
Multiple Testing Correction
When running multiple tests simultaneously or testing multiple metrics, you may need to adjust your significance level to avoid false positives. Common approaches include:
- Bonferroni Correction: Divide your significance level by the number of tests
- False Discovery Rate (FDR): Control the expected proportion of false positives
- Sequential Testing: Use methods that allow for continuous monitoring
Sample Size vs. Test Duration Trade-offs
Larger Sample Size
- ✓ Detect smaller effects
- ✓ Higher statistical power
- ✗ Longer test duration
- ✗ More resources required
Smaller Sample Size
- ✓ Faster results
- ✓ Lower resource usage
- ✗ Only detects large effects
- ✗ Risk of false negatives
When to Extend Test Duration
Consider running your test longer than the minimum calculated duration when:
- You're testing during a transition period (new feature launch, seasonality)
- Your traffic has high variance day-to-day
- You want to observe long-term user behavior changes
- The cost of a false positive is very high
Frequently Asked Questions
Our calculator uses industry-standard statistical formulas based on power analysis for two-proportion tests. The accuracy depends on your inputs being representative of actual traffic patterns. The calculations assume random sampling and stable traffic over time.
MDE is the smallest relative change in conversion rate that you want to reliably detect. For example, if your current conversion rate is 2% and you set MDE to 20%, the test will be powered to detect changes of 0.4% or larger (20% of 2%). Smaller MDEs require longer tests.
This calculator uses 95% statistical significance, which is the industry standard. This means there's only a 5% chance of a false positive (detecting a difference when there isn't one). For most A/B tests, this provides a good balance between accuracy and practical test duration.
This calculator uses 80% statistical power, which is the recommended standard. This means you have an 80% chance of detecting your MDE if it's real. Higher power would require larger sample sizes and longer test durations.
No, stopping early leads to inflated false positive rates. This is called "peeking" and invalidates your statistical calculations. Always run tests for the full calculated duration unless you use specialized sequential testing methods.
When testing multiple variations against a control, the calculator automatically adjusts the required sample size. Each additional variation increases the total sample needed, extending test duration. Consider limiting variations to 3-4 for practical test lengths.
Use your average daily traffic, but be aware that high variance can affect results. If weekday traffic is 5x weekend traffic, consider running tests in complete weekly cycles and potentially extending duration to ensure adequate weekend samples.
Include only visitors who could realistically convert. For example, if testing a checkout page change, only count visitors who reach the checkout, not all site visitors. This gives more accurate duration estimates.
Holidays and seasonal events can skew results. Either exclude these periods from your test or ensure your test runs long enough to include comparable periods (e.g., multiple weekends or pay cycles) for balanced data.
This calculator uses two-tailed tests (recommended), which detect both positive and negative changes. One-tailed tests only detect changes in one direction but require smaller samples. Use two-tailed unless you're certain about direction.