Nonparametric inference discussed in previous chapters is performed under minimal assumptions on the underlying statistical model for the given data. On the other hand, usually, the more assumptions we make, more powerful methods are available for data analysis, and, as a result, the more information and insights we can infer from the data (provided that the model assumptions are correct). In this chapter, we will turn our attention to parametric inference that makes stronger assumptions about the data and introduce the method of moments, one of the first general parametric methods for estimating model parameters.