Lesson 10
Correlation vs causation
Big question
When does a relationship describe association, and when does it support cause and effect?
Lesson progress
Complete checkpoints as you learn
Learning objectives
- Explain correlation vs causation in plain language.
- Use correlation correctly in an interpretation.
- Connect the lesson idea to a formula, graph, Python result, or real example.
Simple explanation
Correlation means two variables move together. Causation means changing one variable would change another. Many correlations are not causal because other factors may be driving both variables.
Key terms
- Correlation
- A descriptive association between variables.
- Causation
- A cause-and-effect relationship.
- Confounder
- A third factor related to both the explanatory variable and the outcome.
- Causal claim
- A statement that changing one factor would change another.
Example
Education and wages may be correlated. A causal claim asks how wages would change if the same person received more education.
Checkpoint activity
Pause and explain this lesson's main idea in your own words before moving forward.
Try it yourself
Write one plain-English sentence explaining the main idea from this lesson.
Common mistakes
Check these before you move on.
A regression coefficient describes a pattern unless the assumptions or research design support a causal interpretation.
Quick quiz
Why can correlation fail to prove causation?
Key takeaway
Correlation is a useful clue, but causal interpretation needs stronger reasoning and design.