Early Stopping Based on Repeated Significance

For a bucket test with a single criterion for success and a fixed number of samples or testing period, requiring a p-value less than a specified value of α for the success criterion produces statistical confidence at level 1 − α. For multiple criteria, a Bonferroni correction that partitions α among the criteria produces statistical confidence, at the cost of requiring lower p-values for each criterion. The same concept can be applied to decisions about early stopping, but that can lead to strict requirements for p-values. We show how to address that challenge by requiring criteria to be successful at multiple decision points.