I am currently working on a data science project involving consumer behavior trends. I noticed my variables aren't showing a linear relationship. Should I stick with Pearson correlation, or is it safer to use Spearman rank correlation for non-normal distributions? I want to ensure my statistical analysis is robust before I present these findings to the stakeholders.
3 answers
If you are dealing with non-linear relationships or data that isn't normally distributed, Spearman is definitely the way to go. Pearson measures linear strength, so if your data curves or contains significant outliers, it will give you a misleading coefficient. Spearman works on the rank of the values rather than the raw numbers, making it a non-parametric powerhouse for monotonic relationships. In my experience with market research, using Spearman saved us from misinterpreting a crucial trend in customer acquisition costs that didn't follow a straight line.
That is a great question, but have you checked if your data is actually monotonic? Spearman is great, but if the relationship isn't consistently increasing or decreasing, even rank correlation might fail you. What does your scatter plot look like?
I always recommend Spearman for non-normal data because it is far less sensitive to outliers than Pearson. It provides a more honest view of the association between variables.
I agree with David. In most real-world data science scenarios, data is rarely "perfectly" normal, so leaning on non-parametric tests like Spearman is a safer statistical bet.
Michael, the scatter plot shows a clear upward curve, but it definitely levels off at the higher end of the spectrum. Based on that, I think a monotonic assumption holds true, but I was worried about the heteroscedasticity affecting the P-values in a standard linear regression model.