Quantitative public policy analysis shares important foundations with a standard upper-division or graduate-level course on inferential statistics, but the objectives have important differences. A statistics class may make greater emphasis of statistical inference from sample statistics to population parameters under known assumptions (e.g. random sampling, central limit theorem). A quantitative public policy analysis course may care more about causal inference and the scope of treatment effects. Both inform each other, but speak to different audiences. This class will bring in some foundation components of a statistics class and tailor it for a public policy audience. It starts with rudimentary statistics, assuming a policy audience may not be accustomed to thinking of policy analysis quantitatively. It proceeds to basic tests of difference and association, like Chow tests and t-tests. It builds toward more sophisticated research designs, like regression discontinuities and instrumental variables. It then discusses what to do when data are not normally distributed. It concludes with making quantities of interest to communicate to a lay audience. This class aims to broadly prepare students for quantitative public policy analysis for research in and out the academy.