Enter your Variable X/Y data, choose Correlation Type, Significance Level (α), and Test Type, and this Correlation Calculator returns the Correlation Coefficient (r), r², P-value, T-statistic, and Sample Size.
Correlation Coefficient (r)
--
Coefficient of Determination (r²)
--
P-value
--
Sample Size (n)
--
T-statistic
--
Degrees of Freedom
--
Pearson correlation measures linear relationships between continuous variables, while Spearman's rank correlation assesses monotonic relationships and is suitable for ordinal data or non-normal distributions.
The correlation coefficient indicates the strength and direction of a relationship between biological variables. Values near +1 or -1 indicate strong relationships, while values near 0 suggest weak or no linear relationship.
The p-value indicates the probability of observing the correlation by chance. If p < α (significance level), the correlation is statistically significant and unlikely due to random variation.
You need at least 3 paired observations, but larger samples (n ≥ 30) provide more reliable results. Small samples may not detect weak correlations or may overestimate correlation strength.
Use Spearman correlation when data is ordinal, not normally distributed, contains outliers, or shows a monotonic but non-linear relationship. It's more robust to outliers than Pearson.
R² represents the proportion of variance in one variable that is predictable from the other variable. For example, r² = 0.64 means 64% of the variance is explained by the relationship.