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6. Use of classical ANOVA was not justified. Nonparametric ANOVA on the within-block ranks rejected equality of treatment medians at a high level of significance ( P < 0.0001), while classical ANOVA showed only weak significance (P = 0.0478). (The Appendix contains the necessary SAS statements to compute a nonparametric RCBD ANOVA.) Comparison of the P values apparently illustrated the improved power of the nonparametric approach. Use of the LSD on Plant breeders developing disease-resistant horticultural crops need flexible and powerful statistical methods to compare treatments efficiently. The most commonly used statistical methods are those based on analysis of variance (ANOVA), regression, and correlation. The valid use of these methods requires that the data have 1) normally distributed responses, 2) constant error variance, 3) independently distributed errors, and 4) a correctly specified model. However, many plant disease experiments yield data that do not adhere to these standard assumptions. For example, in an experiment involving population levels of a pathogen, rarely will population levels be normally distributed. Field counts of the number of nondiseased individuals in a plot many times will have different variances for various treatments. Laboratory experiments measuring the amount of a particular chemical compound produced by a pathogen may have a few cultures with extremely high amounts of the compound, indicating the presence of outliers. Using standard methods (e.g., ANOVA) on data such as these is questionable for several reasons. Any conclusions based on unreasonable assumptions are suspect and may be wrong simply because the chosen method of analysis forces assumptions on the data that cannot be justified. More importantly, use of inappropriate statistical methods many times reduces the chances of correctly identifying differences that would have been found had the correct method been used. Nonparametric (NP) statistical methods can be useful alternatives to classical statistical methods. Nonparametric methods require fewer assumptions about the data, but in many cases, allow one to draw valid conclusions with considerably better chances of detecting differences between treatments. The objective of this paper is to demonstrate how nonparametric statistical methods can be simply applied to data likely to result from plant disease experiments.

999 STATISTICAL ANALYSIS OF DISEASE REACTION DATA USING NONPARAMETRIC METHODS