Bayesian methods have been adopted in Anthropology for their utility in resolving complex analytical problems. This includes the study of demographic processes using genetic data sets with either low information content or large genomic datasets, for which multiple loci with contradictory information makes consensus difficult. The main advantages of Bayesian methods include simple model comparison, describing results as a summary of combined probability distributions, and the explicit inclusion of prior information into analyses. In the field of anthropological genetics, for example, Bayesian skyline plots and approximate Bayesian computation methods have become ubiquitous as means to analyze genetic data for the purpose of demographic or historic inference. Correspondingly, there is a critical need to better understand the underlying assumptions, proper applications, and limitations of these two methods by the larger anthropological community. Here I present a summary of Bayesian skyline plots and approximate Bayesian computation applied to human demography. My goal is to describe their basic mechanics so as to demystify them for anthropologists, who are increasingly encountering these methods in the literature and applying them to answer fundamental questions on human history.