SIPVADE: A New Computer Programme with Seventeen Statistical Tests for Outlier Detection in Evaluation of International Geochemical Reference Materials and its Application to Whin Sill Dolerite WS-E from England and Soil-5 from Peru

A new computer programme was written in programming language TURBOC, which enables us to apply a procedure involving seventeen statistical tests (a total of sixty five single or multiple outlier versions of these tests) for outlier detection in univariate sample at a high confidence level of 99% (significance level α= 0.01). The outlying observations should be evaluated first for technical reasons and then rejected manually from the data base until no more outliers are detected and the final statistical parameters are computed from the remaining data. This programme has been used successfully to process two reference material data bases: WS-E from England and Soil-5 from Peru. The final mean values for WS-E are more reliable (characterized by smaller standard deviations and narrower confidence limits) than those obtained earlier using a different statistical approach. The application of a large number of statistical tests to Soil-5 also resulted in smaller standard deviation values for most elements than the method involving a limited number of such tests. For WS-E, some laboratories seem to have produced multiple data that were detected as statistical outliers. A close analysis of the distribution of outliers as a function of laboratory, country and analytical method leads to a technical justification for these outlying observations, probably in terms of inadequate QA/QC practices. Use of geochemical criteria indicates that the new mean values in WS-E might be closer to the "true" concentrations. This procedure of outlier detection and elimination is therefore recommended in the study of the existing RM.