Correlation vs. Causation Analysis
This worksheet helps Grade 12 students differentiate between correlation and causation through various examples and questions, aligning with high school statistics standards.
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Correlation vs. Causation Analysis
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Read each question carefully and provide your best answer. Distinguish between correlation and causation in the given scenarios.
1. Which of the following statements is true regarding correlation and causation?
Correlation implies causation.
Causation implies correlation.
Neither correlation nor causation implies the other.
Correlation and causation are the same concept.
2. A study finds that people who drink more coffee tend to live longer. This is an example of:
Causation
Correlation
Both causation and correlation
Neither causation nor correlation
1. If two variables are correlated, it means one variable causes the other.
True
False
2. A confounding variable is a third variable that influences both the independent and dependent variables, creating a spurious correlation.
True
False
1. When two variables move together in a predictable way, they are said to be .
2. To establish causation, researchers often use experiments.
3. A strong correlation does not automatically mean there is a relationship.
1. Explain the difference between a positive correlation and a negative correlation. Provide an example for each.
2. Describe a scenario where two variables are correlated but not causally related. Explain why there isn't a causal link.
Consider the following scatter plot:
1. Based on the scatter plot, what type of correlation exists between ice cream sales and temperature?
2. Can we conclude that higher temperatures cause an increase in ice cream sales? Justify your answer.
1. Discuss the importance of distinguishing between correlation and causation in scientific research and everyday life. Provide examples of common misconceptions that arise from confusing the two.
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