The Bayesian Inferential Paradigm in Archaeology

Abstract

Archaeologists often use data and quantitative statistical methods to evaluate their ideas. Although there are various statistical frameworks for decision-making in archaeology and science in general, in this chapter, we provide a simple explanation of Bayesian statistics. To contextualize the Bayesian statistical framework, we briefly compare it to the more widespread null hypothesis significance testing (NHST) approach. We also provide a simple example to illustrate how archaeologists use data and the Bayesian framework to compare hypotheses and evaluate their uncertainty. We then review how archaeologists have applied Bayesian statistics to solve research problems related to radiocarbon dating and chronology, lithic, ceramic, zooarchaeological, bioarchaeological, and spatial analyses. Because recent work has reviewed Bayesian applications in archaeology from the 1990s up to 2017, this work considers the relevant literature published since 2017.

Beyond Chronology, Using Bayesian Inference to Evaluate Hypotheses in Archaeology

“…Below, we provide an overview of the central concepts of the two major probability paradigms to evaluate hypotheses: NHST and Bayesian inference. Whereas most scientists widely use NHST, the Bayesian approach is considered a modern data-driven learning system that has enjoyed increasing application to archaeology (Buck and Meson 2015; Buck et al 1996; Howson and Urbach 2006; Jaynes 2003; Otárola-Castillo and Torquato 2018; Otárola-Castillo et al 2022).…”

“…Using a fictional zooarchaeological example, this article provides a straightforward explanation of Bayesian inference and compares it to the more conventional null hypothesis significance testing (NHST). Although some have previously described and reviewed the application of these concepts elsewhere (e.g., Buck and Meson 2015; Buck et al 1996; Otárola-Castillo and Torquato 2018; Otárola-Castillo et al 2022; Wolfhagen 2019, 2020), this work is focused on presenting replicable step-by-step examples of the Bayesian framework for evaluating and discerning among competing hypotheses. R Markdown code to reproduce all materials presented here is available in an OpenScience Framework repository: .…”