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040 _aMSU
_cMSU
_erda
100 _aPearson, Jessica
_eauthor
245 _aCounting sheep: sample size and statistical inference in stable isotope analysis and palaeodietary reconstruction
_ccreated by Jessica Pearson and Matt Grove
264 _bTaylor & Francis
_c2013
336 _2rdacontent
_atext
_btxt
337 _2rdamedia
_aunmediated
_bn
338 _2rdacarrier
_avolume
_bnc
440 _vVolume , number ,
520 _aThe analysis of stable isotopes from biological remains has contributed greatly to archaeological knowledge in recent decades, with major contributions to areas such as prehistoric diet and mobility. The increasing importance of isotopic studies within archaeology has been paralleled by an increasing sophistication of laboratory techniques. At present, however, the relationship between sample size and the strength of the conclusions that can be drawn about the underlying population has received insufficient attention. In this paper we undertake to provide a series of statistical analyses that demonstrate the strength of inference in relation to sample size using a large corpus of carbon and nitrogen isotope measurements of bone collagen from a single species at one site (n = 174). We employ a sub-sampling approach to establish sample sizes at which estimated mean values asymptote, and derive confidence intervals accordingly. Our study quantifies the probability that means calculated from samples of various sizes would contain the estimated true value of the population mean with their 95 per cent confidence limits. Using these results, we suggest that large discrepancies should be expected where fewer than eight samples are used to estimate the population mean, but that sampling redundancy occurs with more than forty samples. We would encourage isotope specialists working with other types of material to undertake similar studies in order to better understand the strengths and limitations of stable isotope data.
650 _a Stable isotopes
650 _aSample size
650 _aAncient diet
856 _uhttps://doi.org/10.1080/00438243.2013.820646
942 _2lcc
_cJA
999 _c160533
_d160533