What statistical method is best suited to determine if personalized content significantly increases user engagement?

A/B testing with appropriate statistical analysis, such as a t-test or ANOVA, is best suited to determine if personalized content significantly increases user engagement. A/B testing, also known as split testing, involves randomly assigning users to two or more groups: a control group that receives standard (non-personalized) content and a treatment group that receives personalized content. User engagement is measured using key metrics such as click-through rates, time spent on page, conversion rates, or other relevant actions. A t-test is used to compare the means of two groups (control vs. treatment) to determine if there is a statistically significant difference in engagement metrics. ANOVA (Analysis of Variance) is used when comparing more than two groups. Before the test, a hypothesis is established: Personalized content will increase user engagement. The null hypothesis is that there will be no difference. Statistical significance is determined by the p-value; a p-value less than a predetermined significance level (usually 0.05) indicates that the observed difference is unlikely to have occurred by chance, and the null hypothesis is rejected. If the A/B test shows a statistically significant increase in engagement metrics for the personalized content group, it provides evidence that personalization is effective. It is crucial to ensure sufficient sample size to achieve statistical power, meaning the test has a high probability of detecting a real effect if one exists.