Table of contents
- 1. Intro to Stats and Collecting Data55m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically1h 45m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables2h 33m
- 6. Normal Distribution and Continuous Random Variables1h 38m
- 7. Sampling Distributions & Confidence Intervals: Mean1h 3m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 12m
- 9. Hypothesis Testing for One Sample1h 1m
- 10. Hypothesis Testing for Two Samples2h 8m
- 11. Correlation48m
- 12. Regression1h 4m
- 13. Chi-Square Tests & Goodness of Fit1h 20m
- 14. ANOVA1h 0m
1. Intro to Stats and Collecting Data
Intro to Stats
Problem 1.1.34
Textbook Question
In Exercises 29–36, identify what is wrong.
Storks and Babies In the years following the end of World War II, it was found that there was a strong correlation, or association, between the number of human births and the stork population. It therefore follows that storks cause babies.

1
Understand the concept of correlation: Correlation measures the strength and direction of a linear relationship between two variables. It does not imply causation.
Identify the logical fallacy: The statement 'storks cause babies' is an example of a post hoc fallacy, where correlation is mistaken for causation.
Consider alternative explanations: There may be external factors influencing both the stork population and human births, such as environmental changes or societal factors post-World War II.
Explore the concept of confounding variables: A confounding variable is an external factor that affects both variables being studied, potentially leading to a spurious correlation.
Emphasize the importance of rigorous analysis: To establish causation, one must conduct controlled experiments or use statistical methods to rule out confounding variables and establish a causal link.

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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Correlation vs. Causation
Correlation refers to a statistical relationship between two variables, indicating that they change together. However, this does not imply that one variable causes the other. In the storks and babies example, while there may be a correlation, it is a logical fallacy to conclude that storks cause an increase in human births without further evidence.
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Spurious Correlation
A spurious correlation occurs when two variables appear to be related but are actually influenced by a third variable or are coincidental. In the case of storks and babies, other factors, such as population growth or environmental changes, could be influencing both variables, leading to a misleading association.
Confounding Variables
Confounding variables are external factors that can affect the relationship between the independent and dependent variables in a study. In the stork and baby scenario, factors like socioeconomic conditions or urbanization trends could confound the observed correlation, making it essential to control for these variables to draw valid conclusions.
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