Which statement correctly differentiates Pearson and Spearman correlations?

Which statement correctly differentiates Pearson and Spearman correlations?

• A. Pearson is non-parametric and uses ranks; Spearman is parametric and normally distributed.
• B. Pearson is parametric and assesses linear relationships on continuous, normally distributed data; Spearman is non-parametric and uses ranks on ordinal data.
• C. Both require normal distribution.
• D. They measure causation.

Answer: (B)

Pearson and Spearman differ in what they assume about the data and what kind of relationship they measure. Pearson is a parametric measure that looks at linear relationships between two continuous variables and relies on the data being approximately normally distributed (and the relationship being roughly linear with constant spread). It uses the actual data values, not their ranks, and its strength is highest when the relationship is linear and the data meet those distributional assumptions. Spearman, on the other hand, is non-parametric and uses the ranks of the data. It assesses monotonic relationships, where as one variable increases, the other tends to increase or decrease, but not necessarily at a constant linear rate. Because it works with ranks, it can be used with ordinal data or with continuous data that aren’t normally distributed, and it’s more robust to outliers. So the best description is that Pearson is parametric and evaluates linear relationships on continuous, normally distributed data, while Spearman is non-parametric and uses ranks to assess monotonic relationships. Correlation does not imply causation.

Pearson and Spearman differ in what they assume about the data and what kind of relationship they measure. Pearson is a parametric measure that looks at linear relationships between two continuous variables and relies on the data being approximately normally distributed (and the relationship being roughly linear with constant spread). It uses the actual data values, not their ranks, and its strength is highest when the relationship is linear and the data meet those distributional assumptions.

Spearman, on the other hand, is non-parametric and uses the ranks of the data. It assesses monotonic relationships, where as one variable increases, the other tends to increase or decrease, but not necessarily at a constant linear rate. Because it works with ranks, it can be used with ordinal data or with continuous data that aren’t normally distributed, and it’s more robust to outliers.

So the best description is that Pearson is parametric and evaluates linear relationships on continuous, normally distributed data, while Spearman is non-parametric and uses ranks to assess monotonic relationships. Correlation does not imply causation.