Sensitivity of Radiocarbon Sum Calibration

Abstract

Sum calibration has become a standard tool for demographic studies, even though the methodology itself is far from uncontroversial. In addition to fundamental methodological criticism, questions are frequently raised about the sample size and data density required to detect large-scale changes in past populations. This article uses a simulation approach to determine the detection probabilities for events of varying intensity and with varying data density. At the same time, the effectiveness of Monte Carlo-based confidence envelopes as a countermeasure against false-positive results is tested. The results show that the detection of such events is not unlikely and that the Monte Carlo method is well suited to separate signal and noise. However, the nature of the events already observed in this way demands further assessment.