Spearman Rank Correlation Calculator

Calculate tie-aware Spearman rho from paired values, inspect ranks and p-values, compare Pearson correlation, and verify the full rank working.

Tie-aware rhoaverage ranks for tied values

Exact small-n ppermutation test for n ≤ 8

Full rank workingd, d² and shortcut audit

Visual cross-checkrank scatter + Pearson comparison

X values

Y values

—Rank correlation

—Approx. p-value

—t approximation

—df

—Pairs

#	X	Rank X	Y	Rank Y	Rank diff.

Ties receive average ranks. The displayed two-sided p-value uses a t approximation; it is not an exact small-sample permutation test.

Rank correlation workspace

Inspect Spearman rho from the ranks all the way to the significance check

Paste paired data, inspect average ranks and tie groups, compare the rank correlation with raw Pearson r, visualize rank alignment, and use an exact permutation p-value for small samples.

Tie-aware · exact small-n inference

X valuesY valuesSignificance level α

—Spearman ρ

—p-value

—Decision at α

—Pairs

—Rank association

—Raw Pearson r

—Σ d²

—Tie groups X · Y

Rank scatter

Formula cross-check. —

Spearman vs Pearson. —

Rank working table

#	X	Y	Rank X	Rank Y	d	d²

Ready.

Inference boundary. Spearman measures monotonic association, not causation. Ties use average ranks and rho is computed as Pearson correlation of the rank vectors. For n ≤ 8 this workspace enumerates all Y-rank permutations for a two-sided exact permutation p-value; for larger samples it reports the existing two-sided t approximation. Exact/permutation and asymptotic methods can differ, especially with small samples or many ties.

Core describe → test → interpret workflows

Use descriptive context first, choose the method that matches the design, then keep inputs, settings and result evidence reproducible instead of copying a p-value without its analysis recipe.

Start: Statistics Calculator Choose a statistical test

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Interpret statistical results in context

Use plots and descriptive summaries before formal tests. Check the sampling/design assumptions that matter for the chosen method, report effect size and uncertainty where available, and avoid treating a threshold such as p < .05 as proof of importance, causation, or truth. Browser calculations are educational/planning utilities, not domain-specific professional advice.