Topological data analysis (TDA) is an emerging branch of data science that utilizes algebraic topology. Despite its wide range of applications, it has been challenging to apply statistical principles to the study of topological properties of datasets. In this thesis, we push the frontiers of statistical topology by developing a method to identify small topological features, and studying the topological properties of preferential attachment graphs, a class of inhomogeneous random graph models.