Null models are statistical tools that use randomization or simulation models to test and measure the effects of mechanisms behind empirical data. Null models have become prevalent in biodiversity research because they represent flexible tools for data analysis. The rise in popularity of null models has likely been liked to the rise in use of R, which allows scientists to easily use, develop and test null models. In this workshop, we will learn the basics of conducting null model analyses in R. We will learn (1) what a null model is, (2) how to interpret null model results, (3) how to use pre-existing functions in R for null model analysis, and (4) how to build your own null models. The universe of potential null models is infinite, so we will use some classic problems, like species co-occurrence, and some recent developments, like analysis of phylogenetic community structure, to understand concepts, develop examples and practice. By the end of the workshop, the participants will have an introductory exposure to null models and how they can be implemented in R.