Abstract
A data-dependent analysis assumes that all the information available to a researcher is contained within the observed data. Data-dependent methods for the analysis of experimental designs are shown to provide significant advantages over conventional techniques such as an F test. Two versions of three data-dependent methods based on permutations of the data are described and compared. One version utilizes ordinary least squares regression, and the other version utilizes least absolute deviations regression to analyze experimental designs. Analyses of an unbalanced two-way experimental design illustrate the differences among the six data-dependent approaches and the classical ordinary least squares F test, which depends on the assumptions of normality, homogeneity, and independence.
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