Advantages of nonparametric procedures for analysis of water quality data

ABSTRACT Water quality data are usually analysed with parametric statistical procedures requiring the normality assumption for accuracy of their attained significance levels. However, these data are typically non-normally distributed. When applied to non-normal data, the power of parametric procedures is low, and their results may be in error. Three typical case studies are discussed: differentiation of water quality in streams using analysis of variance; discernment of water quality types using discriminant analysis; and t-tests on differences between two groups which include data below the detection limit. Five important advantages of nonparametric methods over commonly used parametric procedures are illustrated.