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Statistics

Spearman Rank Correlation Calculator

Measure monotonic association between paired observations, including tied values assigned average ranks.

Data stays on this deviceConvention explained below
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Check the dataset and sample convention before interpreting results.

See the method

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Your statistical summary

Your result

Let’s calculate.

Use the Calculate button to see your result.

Review the method below for assumptions and conventions.

How to use this tool

  1. Enter sample a, sample b.
  2. Select Calculate to view the result.
  3. Check the method and assumptions below before using the result.

The method, explained

Rank both samples using average ranks for ties, then compute Pearson correlation of those ranks.

A WORKED EXAMPLE

Using sample a = 8, 10, 9, 12, 11, sample b = 5, 7, 6, 8, 9, the result is 0.9 probability. Change these example inputs to match your task; use the method above to check each step.

What to keep in mind

Paired measurements must refer to the same observations. Correlation does not establish causation. A descriptive or probability calculation under the stated assumptions. Check the sample design and model before interpreting results.

Reference: SciPy: Spearman rank correlation

Common questions

How are tied values ranked?

Tied observations share the average of the ranks they occupy. The calculator then correlates these average ranks, which works when the shortcut formula for untied ranks does not.

Why is there no significance p-value?

A quick large-sample approximation can be misleading for small samples or ties. This page reports the rank association itself; an appropriate permutation or other inference procedure is a separate task.

Methodology maintained by ClarityKit. How these tools are built and checked.