When conducting A/B testing on marketing messages, what statistical criterion is essential to determine a truly significant difference?

When conducting A/B testing on marketing messages, a statistically significant p-value is essential to determine a truly significant difference. A/B testing involves comparing two versions of a marketing message (A and B) to see which performs better. The p-value represents the probability of observing the results (or more extreme results) if there is actually no difference between the two versions. A p-value below a predetermined significance level (typically 0.05) indicates that the observed difference is unlikely to have occurred by chance and is therefore statistically significant. For example, a p-value of 0.03 means there is only a 3% chance that the observed difference is due to random variation. Therefore, the difference is likely real and not just a fluke. Without a statistically significant p-value, it is impossible to confidently conclude that one version of the message is truly superior to the other.