Calculate Pearson and Spearman correlation coefficients for biological data with statistical significance testing and detailed results interpretation.
Correlation Coefficient (r)
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Coefficient of Determination (r²)
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P-value
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Sample Size (n)
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T-statistic
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Degrees of Freedom
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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.