Nothing in this chapter is new mathematics. Percentages, the arithmetic mean, sigma notation, sets and events, probability as long-run frequency, random variables and expected value, area under a curve — every one of these was taught in an earlier course. What's new is the question they're all about to answer together: given a pile of data, how do we describe it honestly, and how far can we trust what it seems to say?
Statistics leans on this toolkit constantly. A confidence interval is built from a weighted-average-style formula. A p-value is an area under a curve. Every sampling distribution ahead is, underneath, a random variable with an expected value. This chapter doesn't re-teach any of that from scratch — it refreshes each tool quickly, in exactly the framing statistics will use it, so Chapter 1 can start immediately from a shared foundation instead of pausing to relearn probability mid-argument.
By the end, every section from here through the end of the course will be able to assume this toolkit is already in your hands.