When A/B testing headlines for sponsored content, what statistical measurement definitively confirms the superiority of one headline over another?

When A/B testing headlines for sponsored content, statistical significance definitively confirms the superiority of one headline over another. Statistical significance indicates that the observed difference in performance between the two headlines is unlikely to have occurred by random chance. It is typically expressed as a p-value, which represents the probability of observing the results if there is no actual difference between the headlines. A p-value below a predetermined threshold (usually 0.05) is considered statistically significant, meaning that there is strong evidence to reject the null hypothesis (that there is no difference between the headlines) and conclude that one headline is indeed superior. Simply observing a higher click-through rate for one headline is not sufficient to declare it the winner; statistical significance is necessary to ensure that the difference is real and not just due to random variation. For example, if headline A has a click-through rate of 2% and headline B has a click-through rate of 2.5%, statistical significance testing would determine whether this 0.5% difference is large enough to be considered a true improvement.