If your data violate the assumption of independence of observations (e.g., if observations are repeated over time), you may be able to perform a linear mixed-effects model that accounts for the additional structure in the data. Because the data violate the assumption of homoscedasticity, it doesn’t work for regression, but you perform a Spearman rank test instead. However, you find that much more data has been collected at high rates of meat consumption than at low rates of meat consumption, with the result that there is much more variation in the estimate of cancer rates at the low range than at the high range. Example: Data that doesn’t meet the assumptionsYou think there is a linear relationship between cured meat consumption and the incidence of colorectal cancer in the U.S. If your data do not meet the assumptions of homoscedasticity or normality, you may be able to use a nonparametric test instead, such as the Spearman rank test.
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