Standard Unwrapping

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Vocabulary
associationcausationreal-world problems
Skills
  • compare (association and causation in real-world problems) #dok2
  • contrast (association and causation in real-world problems) #dok2
  • evaluate (relationships between variables for association and causation) #dok3
  • justify (whether a given relationship is associative or causal) #dok3
Learning Targets
  • I can compare association and causation using examples from real-world problems. #dok2
  • I can contrast association and causation in a given data set or scenario. #dok2
  • I can evaluate a real-world problem to determine if the relationship between variables is associative or causal. #dok3
  • I can justify my reasoning for determining whether a relationship is an association or causation. #dok3
Big Ideas
  • Understanding the difference between association and causation is essential for interpreting relationships in real-world situations.
  • Not all relationships found in data imply a cause-and-effect connection; distinguishing between association and causation prevents misinterpretation.
Essential Questions
  • What is the difference between association and causation in data analysis?
  • How can you determine if two variables are merely associated or if one causes the other?
  • Why is it important to distinguish between association and causation in real-world problems?
  • What are some examples of associations that do not imply causation?
  • What evidence is needed to justify a claim of causation from a set of data?