How Many Dates Do I Need?

Jacob Holland-Lulewicz,Brandon T Ritchison

doi:10.1017/aap.2021.10

Abstract

AbstractAs the use of large-scale radiocarbon datasets becomes more common and applications of Bayesian chronological modeling become a standard aspect of archaeological practice, it is imperative that we grow a community of both effective users and consumers. Indeed, research proposals and publications now routinely employ Bayesian chronological modeling to estimate age ranges such as statistically informed starts, ends, and spans of archaeological phenomena. Although advances in interpretive techniques have been widely adopted, sampling strategies and determinations of appropriate sample sizes for radiocarbon data remain generally underdeveloped. As chronological models are only as robust as the information we feed into them, formal approaches to assessing the validity of model criteria and the appropriate number of radiocarbon dates deserve attention. In this article, through a series of commonly encountered scenarios, we present easy-to-follow instructions for running simulations that should be used to inform the design and construction of chronological models.

Highlights

As the use of large-scale radiocarbon datasets becomes more common and applications of Bayesian chronological modeling become a standard aspect of archaeological practice, it is imperative that we grow a community of both effective users and consumers
We offer a series of hypothetical case studies that mirror some of the most common modeling efforts undertaken by archaeologists
As the use of large-scale radiocarbon datasets increases and the use of Bayesian chronological modeling becomes more commonplace, it is imperative that we develop a community of practice within the field of archaeology

Summary

Introduction

As the use of large-scale radiocarbon datasets becomes more common and applications of Bayesian chronological modeling become a standard aspect of archaeological practice, it is imperative that we grow a community of both effective users and consumers. Bayesian models are routinely built to produce estimations of age ranges, including statistically informed starts, ends, and spans of archaeological phenomena. The purpose of using simulations is to formally assess the potential representativeness of a given set of radiocarbon dates within a particular suite of model parameters to address a research question (e.g., determining the end boundaries for a particular site or the beginning of the use of a particular diagnostic material across a region).

Results

Conclusion