The Danger of Mixing Up Causality and Correlation

Prior to beginning work on this discussion forum, read Chapter 8 in the course textbook and the Instructor Guidance for Week 5 and review the Correlation Doesn’t Equal Causation: Crash Course Statistics #8 (Links to an external site.) and The Danger of Mixing Up Causality and Correlation: Ionica Smeets at TEDxDelft (Links to an external site.) videos. In this post, you will be challenged to look at how statistical tests, such as correlation, are commonly used and the possible limitations of such analyses. Additionally, you will need to explain statistical concepts; accurately interpret results of statistical tests; and assess assumptions, limitations, and implications associated with statistical tests.
Much has been written about the relationship between students’ SAT test scores and their family’s income. Generally speaking, there is a strong positive correlation between income and SAT scores. Consider and discuss the following questions as you respond:
What does this correlation tell you?
Is this correlation evidence that having a high family income causes one to have high SAT scores?
Is this correlation evidence that high SAT scores cause higher income? Or does this tell you something else? Explain your answer.
Explain why correlation alone is rarely sufficient to demonstrate cause.
Provide a personal example of two variables that may be correlated but not have a cause and effect relationship. Identify what type of bivariate correlation is involved, based on the measurement scales of the variables.

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