In what scenarios might Spearman's rank correlation coefficient be a better choice than Pearson's?
- When both variables are normally distributed
- When the data contains outliers or is not normally distributed
- When the relationship between variables is linear
- When the relationship between variables is non-linear and non-monotonic
Spearman's rank correlation coefficient is a non-parametric measure of correlation, meaning it can be used when the data is not normally distributed. It is also less sensitive to outliers compared to Pearson's coefficient. Further, it can be used to measure monotonic relationships, whether they are linear or not.
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