A lesson from a course on medical software development turns to the mathematics underlying reliable systems: probability and statistics. The instructor distinguishes deterministic from stochastic systems, then works through discrete and continuous probability density functions, cumulative distribution functions, and survival functions, building toward multivariate distributions. Two engineering applications anchor the theory: signal detection, where probability determines whether a measurement reflects a real event or noise, and the design of clinical trials and validation studies, where statistical rigor decides whether a medical device or piece of software can be trusted. The lesson treats probability not as abstract math but as the toolkit engineers need to reason about uncertainty in systems where errors have clinical consequences. It runs about twelve minutes and functions as a compact bridge lesson inside a longer software engineering course, aimed at students who need the statistical grounding before tackling validation methods later in the curriculum.