When performing A/B testing on ad creatives, what statistical measure must be achieved to confidently declare a 'winning' variation?

When performing A/B testing on ad creatives, a statistically significant p-value (typically less than 0.05) must be achieved to confidently declare a 'winning' variation. A/B testing involves comparing two or more versions of an ad creative to determine which one performs better. The p-value is a statistical measure that indicates the probability that the observed difference in performance between the variations is due to random chance rather than a genuine difference in effectiveness. A p-value of 0.05 means there is a 5% chance that the observed difference is due to random variation. Therefore, a p-value less than 0.05 is generally considered statistically significant, meaning that you can be 95% confident that the winning variation is genuinely better than the other variations. For example, if you're testing two ad creatives and one has a significantly higher click-through rate (CTR) with a p-value of 0.03, you can confidently conclude that the creative with the higher CTR is the 'winner'. It's important to also consider the sample size (the number of impressions or clicks) when evaluating the p-value. A larger sample size provides more statistical power, making it easier to detect significant differences. Relying on a statistically significant p-value ensures that you're making decisions based on real performance differences rather than random fluctuations, leading to more effective ad campaigns.