Spearman correlation is the right choice when your data don’t meet the assumptions required by the more common Pearson correlation. Specifically, you should reach for it when your variables have a monotonic but non-linear relationship, when your data contain outliers, or when one or both variables are measured on an ordinal scale (like rankings or survey ratings). It works by converting your raw data into ranks and then measuring how consistently those ranks move together.
How Spearman Differs From Pearson
Pearson correlation measures the strength of a linear relationship between two variables. It assumes both variables are continuous, roughly normally distributed, and that the connection between them follows a straight line. When those assumptions hold, Pearson is the standard choice.
Spearman correlation is more flexible. Instead of working with the raw values, it ranks each data point from lowest to highest and then calculates a Pearson correlation on those ranks. This simple conversion means Spearman doesn’t care whether your relationship follows a straight line. It only needs the relationship to be monotonic: as one variable goes up, the other consistently goes up (or consistently goes down). A monotonic relationship can curve, accelerate, or flatten out. It just can’t reverse direction. Linear relationships are a special case of monotonic ones, so Spearman works on linear data too, though Pearson will be slightly more powerful in that scenario.
The Three Main Reasons to Choose Spearman
Your Relationship Is Monotonic but Not Linear
Imagine plotting study hours against exam scores and seeing a curve that rises steeply at first, then levels off. Both variables increase together, but not at a constant rate. Pearson would underestimate the strength of that association because it’s looking for a straight line. Spearman captures the full strength of the trend because it only asks whether the ranks stay in a consistent order. Any exponential, logarithmic, or otherwise curving relationship that doesn’t change direction qualifies.
Your Data Contain Outliers
Pearson correlation is highly sensitive to extreme values. A single outlier can dramatically inflate or deflate the coefficient, producing a misleading summary of your data. Simulations published in Frontiers in Psychology confirmed that when outliers contaminate a dataset, Spearman outperforms Pearson in both accuracy and statistical power. The ranking process is what provides this protection: an extreme value might be 10 times larger than the next closest point, but in ranked form it’s only one position away.
One or Both Variables Are Ordinal
Ordinal data have a meaningful order but no guaranteed equal spacing between categories. Think pain rated from 1 to 10, satisfaction surveys from “strongly disagree” to “strongly agree,” or competition placements like 1st, 2nd, 3rd. Pearson assumes the gap between each unit is identical, which isn’t true for ordinal scales. Spearman was designed for ranked data, so it handles ordinal variables naturally.
Assumptions You Still Need to Meet
Spearman is less demanding than Pearson, but it isn’t assumption-free. Three conditions need to hold:
- Monotonic relationship. The association must go in one direction. If your scatterplot shows a U-shape or an inverted U, where the second variable rises and then falls, Spearman will miss the pattern. Always check a scatterplot before running the test.
- Independent observations. Each data point should come from a separate, unrelated unit. Repeated measurements on the same person or time-series data with autocorrelation violate this assumption.
- Random sampling. Your data should represent the population you’re drawing conclusions about, not a hand-picked or convenience subset.
Notice what’s absent from this list: there’s no requirement for normal distribution and no requirement for continuous data. Those are Pearson constraints that Spearman sidesteps entirely.
How Many Data Points You Need
A general guideline from a 2024 study in Restorative Dentistry & Endodontics recommends a minimum of 149 observations to reliably detect at least a moderate correlation with acceptable precision using either Pearson or Spearman. That said, the actual number depends on how strong the correlation is. If you expect a strong association (around 0.9), you can get away with as few as 12 pairs. For a weaker correlation around 0.3, you may need well over a hundred. If you’re writing a study protocol, a useful starting formula is to set your expected correlation, choose a 95% confidence interval width of 0.3, and look up the required sample size. For example, expecting a Spearman coefficient of 0.7 with that confidence interval width calls for about 60 pairs of observations.
How Significance Is Tested
The p-value for a Spearman correlation answers a straightforward question: if there were truly no association between these two variables, how likely would you be to see a correlation coefficient this far from zero just by chance? For small datasets (roughly 17 or fewer pairs), statistical software can compute an exact p-value by examining every possible rearrangement of the data. For larger datasets, the software converts the Spearman coefficient into a t-value and approximates the p-value from that. In practice, you interpret the p-value the same way you would for any hypothesis test.
Interpreting the Coefficient
Spearman’s coefficient (often written as rₛ or ρ) ranges from -1 to +1. A value of +1 means a perfect monotonic increase: every time one variable goes up, the other goes up, with no exceptions in rank order. A value of -1 means a perfect monotonic decrease. Zero means no monotonic trend at all. The standard thresholds for interpreting strength are:
- 0.00 to 0.30: Negligible
- 0.30 to 0.50: Low
- 0.50 to 0.70: Moderate
- 0.70 to 0.90: High
- 0.90 to 1.00: Very high
The same ranges apply to negative values. These are rules of thumb, not hard boundaries, and what counts as “strong enough” depends on your field. A 0.4 correlation might be noteworthy in social science research but underwhelming in a physics experiment.
Quick Decision Guide: Spearman vs. Pearson
If both of these are true, use Pearson: your scatterplot shows a straight-line trend, and your data are continuous with no major outliers. If any of the following apply, use Spearman instead:
- The scatterplot curves but doesn’t reverse direction
- You have clear outliers pulling the data in one direction
- One or both variables are ordinal (rankings, Likert scales, ordered categories)
- Your data aren’t normally distributed and you want a safer, distribution-free option
When in doubt, running both and comparing results is a reasonable approach. If Pearson and Spearman give similar coefficients, the relationship is likely linear and outlier-free. If Spearman is noticeably stronger, that’s a signal your data have curvature or influential outliers that Pearson is struggling with.