Non-parametric tests offer several advantages, including the ability to analyze data that do not meet the assumptions of parametric tests, such as normality or homogeneity of variances. They are also useful for ordinal data or when sample sizes are small. However, their disadvantages include generally lower statistical power compared to parametric tests, which may lead to less sensitive detection of true effects. Additionally, non-parametric tests often provide less specific information about the data compared to their parametric counterparts.
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