Calculator Suite
Statistics guide
Pearson vs Spearman Correlation: Which One Should You Use?
Compare Pearson correlation for linear numeric relationships with Spearman correlation for monotonic rank relationships.
Open related calculatorPearson correlation measures how closely paired numeric values follow a straight-line relationship.
It is sensitive to outliers and can miss strong curved relationships.
Spearman correlation uses ranks, so it can capture relationships where values consistently rise or fall without forming a straight line.
It is often more robust when the scale is ordinal or outliers would dominate Pearson correlation.
A single coefficient cannot show clusters, curved patterns, outliers, or subgroups.
Plot the data first, then choose the coefficient that matches the visible pattern and your research question.
- Height and arm span often have a roughly linear relationship, making Pearson a natural first check.
- Customer rank and satisfaction rank may be monotonic but not linear, making Spearman more appropriate.
This guide is educational statistical context and does not validate a study design by itself.
Last updated: 2026-06-06. Reviewed by Calculator Suite editorial review.