You're monitoring a key product metric and notice a drop that could be normal weekly or monthly seasonality, or it could reflect a real issue in the product. You need to separate expected time-based variation from a true underlying change.
How would you use time-based trends to separate seasonality from a real product issue?
Recognizing recurring seasonal patterns in product metricsBuilding a counterfactual baseline from historical time trendsTesting whether a post-change shift remains after seasonal adjustmentDistinguishing statistical noise from a structural break