Pearson vs Spearman vs Kendall correlation — when do you use each?

easy

Answer

$Pearson r: measures linear relationship, assumes approximately normal data, sensitive to outliers.$
$Spearman rho: rank-based Pearson — captures any monotonic relationship, robust to outliers.$
$Kendall tau: also rank-based, based on concordant/discordant pairs — more robust to small samples, less sensitive to outliers.$
$Rule: Pearson for linear + normal + no outliers; Spearman/Kendall for monotonic + rank-based data.$

Check yourself — multiple choice

Same
Pearson: linear + normal + outlier-sensitive; Spearman/Kendall: rank-based, monotonic, robust — pick per data shape
Only Pearson
Only rank-based

Pearson: linear; Spearman/Kendall: rank-based monotonic + robust.

#descriptive

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