Rank correlation measures relationship between ranked variables. Spearman's rank correlation: ρ = 1 - [6Σd² / n(n²-1)] where d = rank difference. Key concepts: non-parametric, doesn't assume linear relationship. Common traps: miscalculating rank differences, forgetting the 6 in numerator. Exam tips: organize rank calculations carefully. Time-saving: use shortcut formula with table layout. Range: -1 (perfect negative) to +1 (perfect positive). Interpretation: similar to Pearson correlation but for ranks. Tied ranks: average ranks when values equal. Applications: comparing judge ratings, non-normal data analysis. Advantages: robust to outliers, works with ordinal data. Verification: result should match conceptual relationship direction. Understanding rank correlation essential for ordinal analysis. Practice with judge ratings and preference data.