Transformations often used to normalize non-normal data can be time consuming, cumbersome and confusing and common non-parametric tests are not appropriate for evaluating interactive effects common in horticultural research. While parametric tests are robust when the data ‘slightly’ deviate from normality, a significant departure from normality leads to reduced power and the probability of a type I error increases. Most statistical techniques commonly used in horticultural research are parametric tests that are valid only for normal data with homogeneous variances.
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