Calculate correlation coefficients to measure the relationship between two variables with visualization.
Enter your paired data and click "Calculate Correlation" to see results.
Correlation measures the strength and direction of the relationship between two variables. A correlation coefficient (r) ranges from -1 to +1, where -1 indicates perfect negative correlation, 0 means no correlation, and +1 shows perfect positive correlation.
Pearson (r): Measures linear relationships. Assumes both variables are continuous and normally distributed.
Spearman (ρ): Measures monotonic relationships using ranks. More robust to outliers and works with ordinal data.
Critical Warning: A strong correlation does not imply that one variable causes the other. There could be confounding factors, reverse causation, or the relationship could be coincidental. Always consider the context and other evidence before inferring causation.
| |r| Value | Strength | Interpretation |
|---|---|---|
| 1.0 | Perfect | Exact linear relationship |
| 0.8 - 0.99 | Very Strong | Strong predictive relationship |
| 0.6 - 0.79 | Strong | Notable relationship |
| 0.4 - 0.59 | Moderate | Meaningful but not dominant |
| 0.2 - 0.39 | Weak | Minor relationship |
| 0.0 - 0.19 | Very Weak | Little to no relationship |
p < 0.001: Highly significant - very strong evidence against no correlation
p < 0.05: Significant - standard threshold for rejecting null hypothesis
p ≥ 0.05: Not significant - insufficient evidence of correlation
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Pearson measures linear relationship and assumes interval data; Spearman is rank‑based and captures monotonic relations, robust to outliers and non‑normality.
r ranges −1 to +1. Sign indicates direction; magnitude indicates strength. Always visualize with a scatter plot to check nonlinearity/outliers.
Significance depends on n and r via a test (p‑value). Large samples can make small r significant; consider effect size and context.
Correlation does not imply causation. Confounding and reverse causality can produce correlations without causal links.