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Spearman Rank Correlation is particularly useful when the data does not meet the assumptions of Pearson correlation, such as when the relationship between variables is not linear, the data is ordinal, or when the data has outliers that could distort the results of Pearson correlation. It is also effective when analyzing ranked data or non-parametric data.
Spearman Rank Correlation is particularly useful when the data does not meet the assumptions of Pearson correlation, such as when the relationship between variables is not linear, the data is ordinal, or when the data has outliers that could distort the results of Pearson correlation. It is also effective when analyzing ranked data or non-parametric data.