Nonparametric percentile estimate of clinical normal ranges

In this study, the nonparametric percentile estimate (PE), a statistical procedure requiring no previous assumption regarding the distribution of the underlying population, was used to determine adult normal limits for fasting plasma glucose, serum alkaline phosphatase, and urine amylase. Comparisons to the gaussian distribution were performed with histograms, symmetry calculations, plots on probability paper, and the chi-square test. The nonparametric and traditional PEs agreed perfectly when the glucose data fit the gaussian and log-gaussian curves. The nonparametric PE varied from the gaussian PE but was identical to the log-gaussian PE when the alkaline phosphatase activities followed the log-gaussian form. It differed sharply from the gaussian PE and was similar to the log-gaussian PE when the amylase values did not follow either the gaussian or log-gaussian model. The non-parametric PE was as efficient as its gaussian theory competitors when the assumed distributions were correct and, in most instances, was more accurate when the assumed distributions were incorrect.