Which measure of correlation should you use for your task? Learn all you need to know about Pearson and Spearman correlations

Riccardo Andreoni

Towards Data Science

Consider a symphony orchestra tuning their instruments before a performance. Each musician adjusts their notes to harmonize with others, ensuring a seamless musical experience. In Data Science, the variables in a dataset can be compared to the orchestra’s musicians: understanding the harmony or dissonances between them is crucial.

Image of a painted piano. All the keys have some paint on them.
Image source: pixabay.com.

Correlation is a statistical measure that acts like the conductor of the orchestra, guiding the understanding of the complex relationships within our data. Here we will focus on two types of correlations: Pearson and Spearman.

If our data is a composition, Pearson and Spearman are our orchestra’s conductors: they have a singular style of interpreting the symphony, each with peculiar strengths and subtleties. Understanding these two different methodologies will allow you to extract insights and understand the connections between variables.

The Pearson correlation coefficient, denoted as r, quantifies the strength and direction of a linear relationship between two continuous variables [1]. It is calculated by dividing the covariance of the two variables by the product of their standard deviations.

Here X and Y are two different variables, and X_i and Y_i represent individual data points. \bar{X} and \bar{Y} denote the mean values of the respective variables.

The interpretation of r relies on its value, ranging from -1 to 1. A value of -1 implies a perfect negative correlation, indicating that as one variable increases, the other decreases linearly [2]. Conversely, a value of 1 signifies a perfect positive correlation, illustrating a linear increase in both variables. A value of 0 implies no linear correlation.

Pearson correlation is particularly good at capturing linear relationships between variables. Its sensitivity to linear patterns makes it a powerful tool when investigating relationships governed by a consistent linear trend. Moreover, the standardized nature of the…

Source link