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A/B test sample size &
duration calculator

Wondering how long your A/B test should run on Wingify? Estimate required duration and sample sizes for different statistical configurations with Wingify's free calculator.

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Metrics
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Testing Objective
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Estimated campaign duration ~ days

It is recommended to run your campaigns for at least 7 days to capture natural fluctuations in metric performance across weekdays and weekends. Learn more
Want to shorten the duration?

Try increasing the target to conclude in 4 weeks or less.

Total visitors required

Required MDE:

To detect a change in conversion rate from 5% to a target of 5%, your campaign needs to run for approximately 21 days with your current traffic and statistical settings.

To detect a change in average revenue from 5% to a target of 5%, your campaign needs to run for approximately 21 days with your current traffic and statistical settings.

Review and correct errors to calculate total visitors required and campaign duration accurately.

Minimum Detectable Effect (MDE) over time

Adjust your campaign duration using the table below to see how MDE and required visitors change over time.

Note: The calculator is designed for Wingify's enhanced SmartStats engine.
To get estimates using the classic stats engine, please use this calculator instead.

Peek inside Wingify's testing brain

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See how to automate significance & run tests you can trust

Explore advanced statistical parameters such as ROPE, MDR, FPR

Dive deeper into Sequential Testing, Bonferroni Correction, Fixed Horizon Test

Say hello to enhanced SmartStats - our
Bayesian-powered sequential testing engine

Get clear recommendations from reports
Configure statistical parameters according to your needs
Configure metric types that suit your testing objectives

Get clear insights on whether your variations outperform, underperform, or match the baseline. Our engine automatically recommends disabling underperforming variations to speed up results with fewer visitors. Plus, SmartStats tracks your experiment’s health—monitoring minimum run-time, data tracking, and more—while alerting you to errors for quick corrective action. Ensure reliable, faster results with confidence.

No more one-size-fits-all experiments. With enhanced SmartStats, you can fine-tune statistical parameters—like Region of Practical Equivalence, Statistical Power, False Positive Rate, and Minimum Detectable Effect—based on your priorities. Set higher precision for critical experiments, such as revenue tests, and lower precision for low-priority ones like design tweaks. Tailor your approach for smarter, more efficient experimentation.

Choose your testing objective based on your needs: "strict improvement" for guaranteed uplifts, or "improvement or equivalence" to maintain your guardrails. For accurate, error-free results, use the Fixed horizon approach, which only calculates statistics after the full sample size is reached. Alternatively, the Sequential testing approach adjusts for peeking errors, ensuring your reports are always accurate, no matter when you check them.

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