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A/B Test Sample Size & Duration Calculator

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

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Metrics
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Objetivo de la prueba
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Estimated campaign duration ~ días

Se recomienda ejecutar tus campañas al menos 7 días para reflejar los cambios del rendimiento entre semana y fin de semana. Más información Learn more
¿Quieres acortar la duración?

Aumenta el objetivo para terminar en 4 semanas o menos.

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 días 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 días with your current traffic and statistical settings.

Revisa y corrige los errores para calcular con precisión el número total de visitantes y la duración de la campaña.

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 VWO's enhanced SmartStats engine.
To get estimates using the classic stats engine, please use this calculator instead.

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

Get actionable results faster

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.

Get clear recommendations from reports

Take complete control of statistical parameters

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.

Configure statistical parameters according to your needs

Pick between improvement and non-inferiority modes

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.

Configure metric types that suit your testing objectives

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