Statistical procedures for corrosion studies

Abstract

Evaluation of treatment alternatives for corrosion by means of pipe loop systems requires a viable statistical basis for decision‐making.Decision‐makers evaluating corrosion treatment alternatives under the Lead and Copper Rule need the statistical tools to choose the optimum treatment from pipe loop studies. In many cases, the data generated by pipe loop studies do not follow a normal distribution; therefore, nonparametric statistics apply. Techniques that are discussed include determination of data normality using the Kolmogorov–Smirnov and chisquare tests, determination of stabilization using the Spearman coefficient, and comparison of treatments using the Wilcoxon signed ranks or rank sum test. Other data issues that are important to evaluating corrosion studies include determining sample size and frequency, determining the confidence and accuracy of results, and evaluating data outliers.