In this talk, I will provide an overview of gravitational-wave (GW) population inference. I will begin by motivating the need to go beyond individual events and reconstructing the underlying source population. I will then introduce the framework of hierarchical Bayesian inference, with a focus on correcting for selection effects inherent in GW detectors. Finally, I will present the hierarchical likelihood, and present methods of computing it from the properties of individual events.