#  Introduction   Data is rarely static. Decisions are rarely risk-free. As a data scientist, you are frequently asked to stress-test business assumptions, explore distributional uncertainty, or simulate alternative realities. "What if our daily active user acquisition costs double?" "What if our server traffic spikes by 300% during a promotional event?" "What is the probability that our operational losses exceed $50,000 this quarter?" Answering these what-if questions requires moving from simple point estimates (like the simple mean) to robust, probabilistic thinking. While many practitioners may immediately jump to heavy simulation engines, the standard Python scientific stack already contains an underutilized workhorse for exactly this kind of modeling: scipy.stats .…