ABSTRACT. Currently, there is significant ongoing research into the temporal and spatial variability of marine radiocarbon reservoir effects (MREs) through quantification of R values. In turn, MRE studies often use large changes in R values as proxies for changes in ocean circulation. R values are published in a variety of formats with variations in how the errors on these values are calculated, making it difficult to identify trends or to compare values, unless the method of calculating the R is explicitly described or all of the data are made available in the publication. This paper demonstrates the large range in R values (+34 to -122) that can be obtained from a single, secure archaeological context when using the multiple paired sample approach, despite the fact that the terrestrial entities were of statistically indistinguishable 14 C ages, as were the marine samples. This demonstrates the inherent variability in the R calculations themselves and we propose that, together with calculation of mean R, the distribution of R values should be displayed, e.g. as histograms in order to illustrate the full data range. This spread is only apparent when employing a multiple paired sample approach as the uncertainty derived on a single pair of samples, taking account only of the errors on the individual 14 C ages, will never truly represent the overall variability in R that results from the intrinsic variability in the population of 14 C ages in samples that might have been used. Consequently, R values and the associated uncertainty calculated from single pairs should be treated with some caution. We propose that, where possible, when using paired archaeological samples, that a multiple paired approach should be employed as it will test the context security of the material used in the R calculations. When summarizing the values by the weighted average, we also propose that the standard error for predicted values should be employed as this will fully encompass the uncertainty of a future R calculation, using different samples for a similar time and location. Finally, we encourage future publishing of R values using the histogram format, making all of the data available. This will help ensure that R values are comparable across the literature and should provide a framework for standardization of publication methods.