Correlation vs Causation (Critical Thinking)

Crash Course: Correlation vs Causation (Critical Thinking)

Correlation vs Causation: Don't Get Fooled!

Opening Hook

Did you know that 75% of people believe in the existence of a "Mandela Effect" – where collective memories of a past event differ from recorded history? That's a pretty wild statistic, but it's also a great example of how our brains can get tripped up by correlation vs causation.

The Core Idea

Correlation vs causation is the difference between two things happening together and one thing causing the other. It's like the difference between a chicken and an egg – just because they're both present at the same time doesn't mean one caused the other. In this crash course, we'll explore the key facts and figures behind this critical thinking concept, and I'll show you how to spot the difference between correlation and causation.

Key Facts & Figures

• Ancient Greeks: Aristotle was one of the first philosophers to discuss the concept of causation, around 350 BCE.
• Statistical Significance: In 1925, Ronald Fisher developed the concept of statistical significance, which helps us determine whether a correlation is likely due to chance or not.
• Correlation Coefficient: In 1895, Karl Pearson developed the correlation coefficient, a measure of how strongly two variables are related.
• Galton's Law of Ancestral Heredity: In 1889, Francis Galton discovered a correlation between the height of parents and their children, but it wasn't a causal relationship – it was just a coincidence.
• The Spurious Correlation: In 1977, statistician Darrell Huff published a book called "How to Lie with Statistics," which included a famous example of a spurious correlation between the number of people who died from lightning strikes and the number of people who died from bee stings.
• The Simpson's Paradox: In 1951, statistician Edward Simpson discovered a phenomenon where a correlation can appear to exist when it doesn't, due to the way data is grouped.
• The Confounding Variable: In 1974, epidemiologist Bradford Hill identified a confounding variable as a third factor that can affect the relationship between two variables.
• The Example of Smoking and Lung Cancer: In the 1950s, a correlation was discovered between smoking and lung cancer, but it wasn't until later that it was determined that smoking was the cause of lung cancer, not just a correlation.
• The Example of Vitamin C and Scurvy: In the 18th century, a correlation was discovered between vitamin C and scurvy, but it wasn't until later that it was determined that vitamin C was the cause of scurvy, not just a correlation.
• The Example of the O.J. Simpson Trial: In 1995, a correlation was discovered between the presence of a bloody glove and O.J. Simpson's guilt, but it wasn't until later that it was determined that the glove was planted, and the correlation was just a coincidence.

Thought Bubble

Imagine you're a detective trying to solve a mystery. You notice that every time a murder takes place in a small town, a new pizza parlor opens up. You start to think that the pizza parlor is causing the murders, but is it really? Maybe the pizza parlor is just a coincidence – maybe the real cause of the murders is something else entirely. To figure out what's going on, you need to look for other factors that might be contributing to the correlation. Are there other pizza parlors in the area that aren't causing murders? Are there other factors that might be causing the murders, like a serial killer on the loose? By considering all the possibilities, you can start to piece together the real story behind the correlation.

Why This Matters

• Critical Thinking: Understanding correlation vs causation is essential for critical thinking, as it helps us avoid making false assumptions and jumping to conclusions.
• Science: In science, correlation vs causation is a crucial concept, as it helps us determine whether a correlation is due to chance or a real cause-and-effect relationship.
• Business: In business, correlation vs causation is important for making informed decisions, as it helps us avoid making assumptions based on incomplete data.
• Health: In health, correlation vs causation is critical for understanding the causes of diseases and developing effective treatments.
• Politics: In politics, correlation vs causation is essential for making informed decisions, as it helps us avoid making assumptions based on incomplete data.
• Everyday Life: In everyday life, correlation vs causation is important for making informed decisions, as it helps us avoid making assumptions based on incomplete data.

Crash Course Recap

• Correlation vs causation is the difference between two things happening together and one thing causing the other.
• Aristotle discussed the concept of causation in ancient Greece.
• Ronald Fisher developed the concept of statistical significance in 1925.
• Karl Pearson developed the correlation coefficient in 1895.
• Francis Galton discovered a correlation between the height of parents and their children, but it wasn't a causal relationship.
• The spurious correlation is a phenomenon where a correlation appears to exist when it doesn't.
• The Simpson's paradox is a phenomenon where a correlation can appear to exist when it doesn't, due to the way data is grouped.
• The confounding variable is a third factor that can affect the relationship between two variables.
• Smoking and lung cancer is an example of a correlation that was later determined to be causal.
• Vitamin C and scurvy is an example of a correlation that was later determined to be causal.
• The O.J. Simpson trial is an example of a correlation that was later determined to be a coincidence.

⚠️ Remember: Correlation does not imply causation!

Quiz Yourself

1. What is the difference between correlation and causation? a) Correlation is when two things happen together, and causation is when one thing causes the other. b) Correlation is when one thing causes the other, and causation is when two things happen together. c) Correlation and causation are the same thing.

Answer: a) Correlation is when two things happen together, and causation is when one thing causes the other.

1. Who developed the concept of statistical significance? a) Ronald Fisher b) Karl Pearson c) Francis Galton

Answer: a) Ronald Fisher

1. What is the spurious correlation? a) A phenomenon where a correlation appears to exist when it doesn't. b) A phenomenon where a correlation appears to exist when it does. c) A phenomenon where a correlation is due to chance.

Answer: a) A phenomenon where a correlation appears to exist when it doesn't.

1. What is the Simpson's paradox? a) A phenomenon where a correlation can appear to exist when it doesn't, due to the way data is grouped. b) A phenomenon where a correlation appears to exist when it does. c) A phenomenon where a correlation is due to chance.

Answer: a) A phenomenon where a correlation can appear to exist when it doesn't, due to the way data is grouped.

1. What is the confounding variable? a) A third factor that can affect the relationship between two variables. b) A factor that can cause a correlation to exist. c) A factor that can cause a correlation to disappear.

Answer: a) A third factor that can affect the relationship between two variables.