On normal and log-normal models imposed on results from proficiency tests for genetically modified organisms (GMO)

This work forms a background to the recent discussion about normal and log-normal distributions in the results of proficiency tests for GMO analysis. In order to clear up some common confusions, the paper first covers some basic principles, viz., a comparison of normally and log-normally distributed samples of results at various precisions, and a background to the determination of assigned values and scoring in proficiency tests. Then follows brief discussions on the identification of outliers and the use of 'tests for normality'. In conclusion, there is a broad outline of the steps that may assist a proficiency testing scheme in deciding on a suitable model of dispersion.

Automated summary

This paper provides a background for the recent discussion on normal and log-normal distributions in GMO analysis proficiency test results and clarifies common confusions. It covers basic principles, compares normally and log-normally distributed samples at various precisions, and discusses the determination of assigned values and scoring in proficiency tests to assist in deciding on a suitable dispersion model.