What are the challenges in using Pearson correlation with ordinal data?
Understanding ordinal data and its properties is crucial when you're diving into data science. Ordinal data is categorical, rank-ordered data that does not have a fixed interval between its categories. This means that while you can tell the order of the values, you cannot quantify the exact difference between them. When you use Pearson correlation, a measure designed for continuous variables with a linear relationship, you run into issues because ordinal data doesn't fit these criteria. The Pearson correlation coefficient may not accurately reflect the association between ordinal variables, leading to misleading results.
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Bhargava Krishna Sreepathi, PhD, MBADirector Data Science @ Syneos Health | Global Executive MBA | 34x LinkedIn Top Voice
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John DanielAI Developer at Adeption | Expert Prompt Engineer | LinkedIn Top Contributor in AI & Data Science
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