[Question] Is there a valid and recognized approach to stopping rules that considers stability over time?

When you look at data over time, it's fairly common to see weird blips, even in cumulative mean effect calculations, that look like this: /\ | \ ________| \___________________ Or, while waiting for regression to the mean, it's very normal to see your cumulative mean effect, plotted over time, look like this: \ \ /\ \ / \ /\/\___________ \ / \/ \/ In a recent role advising a company on how to run experiments, I showed them examples of this type of data to demonstrate how stopping a test early could lead to misleading results. We had a great example of data where a bizarre blip on a single day,