# What's the difference between Spearman and Pearson correlation?

**difference between**the

**Pearson correlation**and the

**Spearman correlation**is that the

**Pearson**is most appropriate for measurements taken from an interval scale, while the

**Spearman**is more appropriate for measurements taken from ordinal scales.

Also asked, what is the difference between Pearson Spearman and Kendall correlation?

It means that **Kendall correlation** is preferred when there are small samples or some outliers. **Kendall correlation** has a O(n^2) computation complexity comparing with O(n logn) of **Spearman correlation**, where n is the sample size. **Spearman's** rho usually is larger than **Kendall's tau**.

Subsequently, question is, what is the difference between the Pearson correlation and the Spearman correlation quizlet? A researcher evaluating the significance **of** a **Pearson correlation** obtains **r** = 0.78 and is able to conclude that t = 1.34. what is the **difference between the Pearson correlation and the Spearman correlation**? The **Spearman correlation** is the same as the **Pearson correlation**, but it is used on data from an ordinal scale.

Herein, why would you use Spearman's rank?

**Spearman's Rank** correlation coefficient **is** a technique which **can** be **used to** summarise the strength and direction (negative or positive) of a relationship between two variables. The result **will** always be between 1 and minus 1. Create a table from your data. **Rank** the two data sets.

How do you interpret Spearman correlation?

The **Spearman correlation** coefficient, r_{s}, can take values from +1 to -1. A r_{s} of +1 indicates a perfect association of ranks, a r_{s} of zero indicates no association between ranks and a r_{s} of -1 indicates a perfect negative association of ranks. The closer r_{s} is to zero, the weaker the association between the ranks.