Comparison between correlation types helps select appropriate method. Pearson: assumes linear relationship, continuous variables, interval/ratio data. Spearman: non-parametric, works with ranks, robust to outliers. Common traps: using Pearson with non-linear data, confusing when each applies. Exam tips: read problem carefully for data type and distribution. Time-saving: identify data characteristics first, choose method. Data transformation: log or square-root transformation may enable Pearson use. Robustness: Spearman resists outlier influence better. Interpretability: Pearson more standard in business contexts. Assumptions: Pearson requires normality, Spearman doesn't. Correlation matrix: shows all pairwise relationships. Statistical testing: both use t-tests for significance. Understanding comparison enables correct method selection. Practice identifying which correlation fits each scenario.