Data Analytical Thinking and Methods II: Correlation, Causation and Decision-Making


  • How to use data to make better decisions
  • The difference between association (or correlation) and causation
  • An introduction to the concepts of: conditional probability; independence; association; causal inference; and, statistical significance of results
Taught by
  • Daniel Goroff
38 mins


This lecture discusses common inferential challenges and risks people face when working with data, including: thinking about conditional probability, the distinction between association (or correlation) and causation, what it means for events to be independent”, and how to claim a result is “statistically significant” is related to an implicit theory of randomness. The lecture closes by giving some practical advice about how to think through these concepts in your own domain.


Causation vs Correlation by Sense About Science - An article that explores how conflating correlation with causation is one of the most common errors in health and science reporting.

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