Spearman’s correlation and Pearson’s correlation both measure the strength and direction of a relationship between two variables, but they do so in different ways. Pearson’s correlation assesses the linear relationship between two continuous variables and requires that the data be normally distributed. It measures how well the relationship can be described with a straight line. On the other hand, Spearman’s correlation evaluates the strength of a monotonic relationship (which can be either linear or non-linear) between two variables by ranking the data rather than using their raw values. This makes Spearman’s method more robust to outliers and suitable for ordinal data or non-normally distributed data. In summary, use Pearson for linear relationships with continuous data, and use Spearman for ranked or non-linear relationships.