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A rank-based correlation assessing monotonic relationships.
DESCRIPTION
Spearman correlation measures the strength and direction of a monotonic relationship by computing Pearson correlation on ranked variables. It is nonparametric and robust to outliers and non-linear monotonic trends. Unlike Pearson correlation, it does not assume linearity or normality. It is widely used in exploratory analysis and for ordinal data. Statistical tests provide significance via p-values under null independence.
ALTERNATIVE DEFINITIONS
Wikipedia
Spearman’s rank correlation coefficient is a nonparametric measure of rank correlation (statistical dependence between the rankings of two variables).
Stresses nonparametric rank-based dependence rather than linear association.
SciPy
The Spearman rank-order correlation coefficient is a non-parametric measure of the monotonicity of the relationship between two datasets.
Explicitly highlights monotonicity and non-parametric nature in applied statistics context.
RELATED TERMS
Spearman Rank Correlation Kendall’s Tau Monotonic Relationship Nonparametric Statistics Pearson Correlation Coefficient Hypothesis Testing p-value Robust Statistics Outliers
COMPETING TERMS
RELATIONS
- Broader: Correlation
KNOWLEDGE AREAS
TARGET AUDIENCE
Data Scientist Data Analyst Information Analyst Data Engineer
EXTERNAL SOURCES
- Wikipedia - Spearman’s rank correlation coefficient
- SciPy - API for computing Spearman correlation
- NIST - NIST treatment of rank correlation
TOP QUESTIONS
When should I use Spearman Correlation instead of Pearson Correlation?
Use Spearman when the relationship is monotonic but not linear or when data are ordinal or contain outliers that distort Pearson’s linear measure.
Category: Requirements
How is Spearman Correlation computed?
Rank-transform both variables and compute Pearson correlation on the ranks. Ties are handled by average ranks or specific tie corrections.
Category: Example
What does a Spearman Correlation of 0 mean?
It indicates no monotonic association, though non-monotonic relationships may still exist.
Category: Misunderstanding
Is Spearman Correlation robust to outliers?
More robust than Pearson because it uses ranks, reducing influence of extreme values.
Category: Opinion
Can Spearman Correlation be used on ordinal scales?
Yes. It is appropriate for ordinal data where rank order is meaningful but intervals aren’t equal.
Category: Requirements
How do I interpret Spearman Correlation magnitude?
Values near ±1 imply strong monotonic relationships; near 0 implies weak or no monotonic association.
Category: Example
How is significance tested for Spearman Correlation?
A hypothesis test compares the observed coefficient against the null of independence, producing a p-value.
Category: Example
What are common pitfalls using Spearman Correlation?
Assuming it captures non-monotonic patterns, ignoring ties, or overinterpreting small sample estimates.
Category: Mistakes
Does Spearman Correlation handle nonlinear relationships?
It captures monotonic nonlinear relationships but not non-monotonic patterns like U-shapes.
Category: Misunderstanding