This course is an introduction to quantitative methodology in the social sciences. The course focuses on the general concepts that underly statistical inference rather than on specific techniques. These concepts are introduced and taught within the context of real-world examples. The course is organized around two broad, intersecting themes: the dimensionality of the data (univariate, bivariate, multivariate) and the inferential goal (summarization, descriptive inference, causal inference). Topics covered include: simple random sampling and unequal probability sampling, hypothesis testing, linear regression, logit and probit models, maximum likelihood estimation, the bootstrap, and an introduction to the Neyman-Rubin framework for causal inference.