Overview
This lecture explains the crucial difference between correlation and causation, emphasizing the requirements to infer causation through a true experiment.
Correlation vs Causation
- Correlation means two variables share a relationship or change together.
- Causation means one variable directly causes a change in another variable.
- To infer causation, a true experiment with random assignment and manipulation of variables is required.
- Correlation does not imply causation, as related variables may be influenced by other factors.
Example of a True Experiment
- Researchers test an anti-anxiety medication by randomly assigning participants to 0, 50, or 100 mg dosage groups.
- Participants' anxiety levels are measured and compared between groups.
- Since the only manipulated factor is dosage, differences can be attributed to the medication, allowing a causal claim.
Example of Correlational Research
- Researchers examine the relationship between time spent studying and test scores.
- A positive relationship may exist, but causation cannot be inferred because study time might not be the only influencing factor.
- Without a true experiment, we cannot isolate study time as the direct cause of higher scores.
Key Terms & Definitions
- Correlation — a relationship or association between two variables.
- Causation — when one variable directly causes a change in another.
- True experiment — a study where participants are randomly assigned to conditions, and the independent variable is manipulated.
- Random assignment — assigning participants to groups by chance to minimize pre-existing differences.
- Independent variable — the variable that is changed or controlled in an experiment.
- Alpha (α) 0.05 — a common threshold for determining statistical significance in experiments.
Action Items / Next Steps
- Review examples distinguishing correlation from causation.
- Be prepared to identify true experiments and explain why they allow causal inference.