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.
Pearson 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.
Spearman correlation can handle tied ranks, but many ties reduce the detail available to the coefficient. Pearson and Spearman calculations should use the same complete pairs; silently mixing different missing-value rules makes comparison misleading.
Repeated observations from the same subject are not independent. A simple correlation may overstate the amount of independent evidence when clusters or time-series structure are present.
A coefficient near zero can hide a strong curved pattern, while a high coefficient can be driven by a narrow range or a single influential point. Domain context and a plot are necessary before labeling a relationship weak or strong.
Report the sample size, coefficient type, uncertainty, plot, and any exclusions. Do not translate correlation into a causal effect without a design that supports causal reasoning.
- 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: 2026-09-02. Reviewed by Calculator Suite editorial review.