Bayes in biological anthropology
Article first published online: 4 NOV 2013
Copyright © 2013 Wiley Periodicals, Inc.
American Journal of Physical Anthropology
Volume 152, Issue Supplement S57, pages 153–184, December 2013
How to Cite
Konigsberg, L. W. and Frankenberg, S. R. (2013), Bayes in biological anthropology. Am. J. Phys. Anthropol., 152: 153–184. doi: 10.1002/ajpa.22397
- Issue published online: 19 NOV 2013
- Article first published online: 4 NOV 2013
- National Science Foundation. Grant Number: BCS97–27386
- Bayesian statistics;
In this article, we both contend and illustrate that biological anthropologists, particularly in the Americas, often think like Bayesians but act like frequentists when it comes to analyzing a wide variety of data. In other words, while our research goals and perspectives are rooted in probabilistic thinking and rest on prior knowledge, we often proceed to use statistical hypothesis tests and confidence interval methods unrelated (or tenuously related) to the research questions of interest. We advocate for applying Bayesian analyses to a number of different bioanthropological questions, especially since many of the programming and computational challenges to doing so have been overcome in the past two decades. To facilitate such applications, this article explains Bayesian principles and concepts, and provides concrete examples of Bayesian computer simulations and statistics that address questions relevant to biological anthropology, focusing particularly on bioarchaeology and forensic anthropology. It also simultaneously reviews the use of Bayesian methods and inference within the discipline to date. This article is intended to act as primer to Bayesian methods and inference in biological anthropology, explaining the relationships of various methods to likelihoods or probabilities and to classical statistical models. Our contention is not that traditional frequentist statistics should be rejected outright, but that there are many situations where biological anthropology is better served by taking a Bayesian approach. To this end it is hoped that the examples provided in this article will assist researchers in choosing from among the broad array of statistical methods currently available. Am J Phys Anthropol 57:153–184, 2013. © 2013 Wiley Periodicals, Inc.