The Use of Non-Parametric Methods in the Statistical Analysis of a Complex Split Plot Experiment

Abstract

Occasionally statisticians are confronted with data arising from a complex experimental design and not necessarily satisfying the traditional assumptions of analysis of variance; namely those of independence, variance homogeneity, and normality. Sometimes the analyst handles such situations by adjusting the data with transformations or by discarding certain extreme observations, etc. Although this may often lead to a satisfactory analysis, the results may be questionable in some cases. The purpose of this paper is to show that non-parametric methods provide realistic alternatives in analyzing such data. The procedures to be discussed are based on ranks. As such, they are valid under fairly general conditions and have good power properties. Moreover, they involve reasonably simple calculations describable with algorithms, for which computer programs are available.

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