Statistical Identification of Outliers in Ore Gold Reference Material to Determine the Optimal Value

The methods of mathematical statistics were used to distinguish outliers, such as the Grubbs test and the Dixon test. These methods have not completely identified outliers for statistical tests of the analytical results for constant gold standard samples. An outlier statistical recognition method based on the multiple analysis of relative deviation for constant gold, including the arithmetic mean of gold in the samples, its relative deviation allowable limits in accordance with DZ/T 130.3—2006 and determination of qualified interval of the measurement results in order to identify and remove outliers has been established. After removing the furthest outliers, the whole process was conducted for the next round to remove less obvious outliers until no more outliers were found. The arithmetic mean and its fluctuation range of the gold measurement results were produced. 15 artificial ore gold standard samples were assigned to different laboratories and analyzed with passwords by using the standard sample analysis method. A total of 10 sets of independent analysis results were collected. According to the established method to remove outliers, the obtained arithmetic mean value was closer to the certified value than that by the Grubbs test and the Dixon test. The relative deviation of the quality fraction was 0.35, which was excellent. However, the relative deviations of the quality fraction by either Grubbs test or Dixon test and the median value method respectively were 0.42 and 0.40, which were also good. The quality fraction level has been significantly improved by the outlier statistical recognition method described in this paper, which enhanced the effectiveness of statistical analysis of data.