The Limitation Of Pearson Correlation Which Many Often Ignore
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.
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Find the code for my tips here: GitHub.
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