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.