Correlation vs Causation: Pearson vs Spearman

Correlation measures how two variables move together — but it doesn't tell you why. And there's more than one way to measure it. Here's what you actually need to know.

A correlation coefficient runs from −1 to +1: near +1 the two variables rise together, near −1 one rises as the other falls, near 0 there's no linear relationship. Simple — but two cautions trip people up.

Pearson vs Spearman

Pearson's r measures linear association and assumes roughly normal data. Spearman's ρ works on the ranks instead, so it captures any monotonic relationship (consistently up or down, even if curved) and shrugs off outliers. Rule of thumb: reach for Pearson for clean linear data, Spearman when the relationship is curved, the data is skewed, or a few outliers would distort r.

A correlation result showing Pearson and Spearman coefficients in Stratum — Pearson for linear; Spearman for monotonic or outlier-prone data.

The trap: correlation isn't causation

Two variables can correlate strongly because one causes the other, because a third factor drives both, or by sheer coincidence. Ice-cream sales correlate with drownings — not because cones are dangerous, but because hot weather drives both. A correlation is a starting point for a question, never the answer to it.

Always plot it. Anscombe's quartet famously shows four datasets with the same correlation but wildly different shapes. A scatter plot catches the non-linear pattern, the outlier, or the cluster that a single number hides.

In Stratum

The Correlation analysis reports Pearson and Spearman with p-values and confidence intervals, and a correlation matrix that highlights the significant pairs so they stand out.

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Frequently asked questions

When should I use Spearman instead of Pearson?

Use Spearman when the relationship is monotonic but curved, when the data is skewed, or when outliers would distort Pearson's r. Spearman works on ranks, so it's more robust.

Does correlation imply causation?

No. A strong correlation means two variables move together; it doesn't say one causes the other. A lurking third variable or coincidence can produce the same pattern.

What is a strong correlation?

Loosely, |r| above ~0.7 is strong, ~0.3–0.7 moderate, below ~0.3 weak — but always read it alongside a scatter plot and your sample size.

Can two variables be related but have zero correlation?

Yes — correlation only measures linear (Pearson) or monotonic (Spearman) association. A U-shaped relationship can have near-zero correlation while being perfectly real.