What is a nonparametric test? What is a parametric test? Identify an advantage of using rank correlation instead of linear correlation.
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A nonparametric test is a type of statistical test that does not assume any specific distribution for the population from which the sample is drawn. It does not require the data to follow a normal distribution and is often used when the assumptions of parametric tests are violated. Examples of nonparametric tests include the Mann-Whitney U test, Kruskal-Wallis test, and Spearman's rank correlation.
A parametric test, on the other hand, is a statistical test that assumes that the data comes from a type of probability distribution and makes inferences about the parameters of the distribution. Examples of parametric tests include the t-test, analysis of variance (ANOVA), and Pearson's correlation.
Rank correlation, such as Spearman's rank correlation, has an advantage over linear correlation in that it can identify monotonic relationships (either increasing or decreasing), not just linear ones. This means it can detect relationships where the variables increase or decrease together, but not necessarily at a constant rate. Additionally, rank correlation is less sensitive to outliers than linear correlation.
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