How can you boil down an entire random process into a single, meaningful number? And once you have that number, how do you describe how much the outcomes bounce around it? This chapter introduces the expected value — the long-run average of a random variable — then builds variance and higher moments to capture spread and shape, and finally gives you Chebyshev’s inequality, a simple but powerful rule that works for almost any distribution.