The Limitation Of Pearson Correlation Which Many Often Ignore
Not all bivariate analysis is linear!
Pearson correlation is commonly used to determine the association between two continuous variables. But many often ignore its assumption.
Pearson correlation primarily measures the LINEAR relationship between two variables. As a result, even if two variables have a non-linear but monotonic relationship, Pearson will penalize that.
One great alternative is the Spearman correlation. It primarily assesses the monotonicity between two variables, which may be linear or non-linear.
What's more, Spearman correlation is also useful in situations when your data is ranked or ordinal.
👉 See what others are saying about this post on LinkedIn: Post Link.
👉 If you liked this post, do leave a heart react 🤍.
👉 If you love reading this newsletter, feel free to share it with friends!
Find the code for my tips here: GitHub.
I like to explore, experiment and write about data science concepts and tools. You can read my articles on Medium. Also, you can connect with me on LinkedIn.