Pearson
Best for straight-line relationships
Pearson measures linear association between numeric variables. It is sensitive to outliers and assumes the scatter plot is roughly line-shaped.
Pearson correlation calculatorMethod choice
Pearson vs Spearman Correlation
Use Pearson when the relationship is linear and numeric. Use Spearman when the data are ranks, ordinal, skewed, or monotonic but not cleanly linear.
Spearman
Best for ranks and monotonic patterns
Spearman converts values to ranks first, making it useful for ordinal data and relationships that curve but still move in one direction.
Spearman correlationQuick decision
If the scatter plot looks like a straight line, Pearson is usually the first choice. If the scatter plot bends but still rises or falls consistently, Spearman is safer.