Why correlation does not equal causation in data science
In short: Understanding the difference between variables is the foundation of scientific literacy. This video breaks down how to distinguish between what we change and what we measure to avoid common statistical fallacies.
The video explores the fundamental distinction between independent variables—the factors we manipulate or use to explain a phenomenon—and dependent variables, which are the measured responses. It clarifies how these relate to the x-axis and y-axis in data visualization.
It also addresses the critical nuance of correlation versus causation, introducing concepts like lurking variables that can skew results. By covering essential metrics like the Pearson Correlation Coefficient (r), p-values, and standard deviation, the content provides a framework for interpreting biostatistics and epidemiology accurately.
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Sources
- Summary by Intellect · source: Statistics- Dependent variable vs. Independent variable - Cause and Effect - Correlation
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