This chapter focuses on the implementing inferential statistics in a linear model framework. In particular, with any model, there is uncertainty around the parameters the model is estimating, as well as uncertainty about the predictions it makes. This chapter looks at how to interpret and report both of these uncertainties. Among other things, regression coefficients and their confidence intervals are visualized using dot-and-whisker plots, and the incredibly useful predict() function is used for generating predictions and their standard errors. The chapter also briefly introduces the reader to a way of implementing post-hoc multiple testing procedures using the emmeans package.