One common type of data used in spatial ecology and conservation is point data. Point data, or data that describe distinct locations in space, might reflect the locations of individual trees, nests of birds, or patchy disturbances. Often the focus of point pattern analysis is on quantifying spatial dispersion, determining if and how dispersion varies with spatial and temporal scale, and understanding the causes of these patterns. Understanding and quantifying point patterns is essential for several topics in ecology and conservation, such as mechanisms of coexistence and extinction risk in patchy populations. We introduce the use of spatial point pattern analysis for addressing ecological and conservation questions related to the spatial dispersion of species. Our goals are to describe common characteristics of point data and related point patterns, and introduce different types of statistical models used to identify spatial point patterns and the scale(s) at which they occur. We illustrate these models with data on distributions of Opuntia cactus and show how point data can be simulated to better interpret why point patterns occur in nature. Our example highlights how spatial pattern in point data can operate at different spatial scales and how models can provide hypotheses for the drivers of such patterns (e.g., limited dispersal). We also introduce point process models, which have been increasingly used in the analyses of species distributions. We end by discussing potential applications of spatial point pattern analysis to conservation problems.