When it comes to running an A/B test, determining the right sample size is crucial. This helps ensure that the experiment has enough statistical power to accurately detect any meaningful differences between the control and treatment groups.

If the sample size isn’t properly calculated, the A/B test may not have enough participants to spot any statistically significant differences, which can lead to incorrect conclusions or inconclusive results. On the other hand, an overly large sample size can waste resources and drag out the duration of the experiment unnecessarily.




Required number of tested visitors per variation

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