In this lesson, we introduced discrete data and some common visual displays for illustrating its relevant characteristics. We briefly introduced several of the popular distributions used to model discrete data, focusing in particular on the binomial. And we saw how a random variable's distribution or likelihood function can be used to provide an intuitive estimate for an unknown population parameter.

In the next lesson, we'll continue the idea of parameter estimation to include margins of error in order to make inferences about the population in the form of confidence intervals and hypothesis tests. We'll also extend the binomial focus to cases involving more than two outcome categories.