When performing A/B testing on ad creative, what statistical method would be utilized to validate the significance of results?

To validate the significance of A/B testing results on ad creative, a statistical significance test, such as a Chi-squared test or a t-test, would be utilized. A Chi-squared test is appropriate when comparing categorical data, such as conversion rates, while a t-test is used when comparing continuous data, like cost per click. These tests determine whether the observed differences between the ad creative variations are statistically significant or simply due to random chance. The results are typically expressed as a p-value. A p-value below a pre-determined significance level (usually 0.05) indicates that the results are statistically significant, meaning the observed difference is unlikely to have occurred by chance, thus validating that one ad creative performs significantly better than the other. For instance, if you are testing two different ad headlines and the Chi-squared test yields a p-value of 0.02, this suggests a statistically significant difference in conversion rates between the two headlines, indicating that one headline is genuinely more effective.