In A/B testing metadata, what statistical approach determines if observed click-through rate differences are significant, not random?

A statistical significance test, such as a chi-squared test or a t-test, determines if observed click-through rate differences in A/B testing are significant, not random. A/B testing involves comparing two versions of a metadata element (e.g., title, synopsis) to see which performs better, typically measured by click-through rate (CTR). However, the observed differences in CTR between the two versions might simply be due to chance. A statistical significance test assesses the probability that the observed difference occurred randomly. The chi-squared test is commonly used for comparing categorical data, like click-through rates, to determine if there is a statistically significant association between the metadata version and user clicks. A t-test is used for comparing the means of two groups. The result of the test is a p-value, which represents the probability of observing the obtained results (or more extreme results) if there is truly no difference between the groups being compared. If the p-value is below a pre-determined significance level (alpha, typically 0.05), then the observed difference is considered statistically significant, meaning it is unlikely to have occurred by chance and that the change in metadata likely caused the change in CTR. For example, if a chi-squared test yields a p-value of 0.01, it suggests there's only a 1% chance that the observed CTR difference is due to random variation, indicating a statistically significant result. Conversely, a p-value above 0.05 indicates that the observed difference is not statistically significant and could be due to random fluctuations.