STATISTICAL-METHODS FOR CHARACTERIZING GROUNDWATER QUALITY

The benefits from ground-water quality monitoring ultimately depend on the statistical methods used to analyze data. The methods must match both the information expectations of users and the characteristics of the water quality variables to which they are applied. The primary objective of regulatory ground-water monitoring is detecting changes in quality. To select appropriate statistical tests for change, one must know whether the water quality variables of concern are seasonal, normally distributed, and serially dependent. This paper provides guidance in analyzing limited background data sets to determine these three characteristcs. Recommended procedures to detect seasonality were periodograms, Student's t-test, Mann-Whitney test, analysis of variance, and Kruskal-Wallis test. To test for normality, the skewness coefficient is recommended. To detect serial dependence, sample autocorrelation coefficients may be tested for significance.