| Abstract: | Multivariate statistical analysis of artefact compositional data, usually undertaken to investigate structure in the data, often incidentally reveals the presence of multivariate outliers. Much statistical methodology dealing with the detection of such outliers is not well suited to archaeometric data that, in the event, consist of two or more groups. The paper provides examples to illustrate the importance of detecting and dealing with outliers, and critically examines a range of different approaches to outlier detection. The examples show that cluster analysis, the technique most widely used for this purpose, can fail to reveal outliers clearly identified by other methods. |
