Several factors can influence the statistical power of an email marketing test:
Sample Size: Larger sample sizes generally increase statistical power. In email marketing, having a substantial list of recipients can enhance the reliability of A/B test results. Effect Size: The magnitude of the difference between the two test groups. A larger effect size increases the likelihood of detecting a difference. Significance Level: The probability of rejecting the null hypothesis when it is true. Lower significance levels (such as 0.01 instead of 0.05) decrease the chance of Type I errors but also require a higher power to detect an effect. Variance: Lower variability within sample data enhances the power of a test. Consistency in the email content and audience can reduce variance.