To determine whether the new advertising model is statistically improved, we calculate the Z-statistic. A large enough sample size allows us to assume a normal distribution of Z under the null hypothesis.
If we find that Z exceeds a previously defined threshold α, we conclude model B is superior. However, incorrect conclusions about model B being better could occur if Z < α when model B is indeed superior.
The p-value represents the probability of making a Type I error: rolling out model B when it is not actually better than model A. It's crucial for deciding on model deployment.
By determining an appropriate α based on an acceptable error probability p-value, we can make informed decisions regarding the deployment of the advertising model, balancing risk and reward.
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