IB Psychology 1.2.1 Correlation Vs Causation Notes

Correlation vs Causation

Correlation

1. Correlation describes a statistical relationship between two variables.
2. When one variable changes, the other tends to change in a predictable way.

Causation

1. Causation means that one variable directly influences another.
2. A change in one variable causes a change in the other.

Key Differences Between Correlation and Causation

1. Directionality: Correlation does not specify which variable influences the other, whereas causation identifies a clear direction of influence.
2. Third Variables: Correlation can be influenced by a third variable that affects both variables, whereas causation rules out third variables as the primary cause.
3. Predictability vs. Explanation: Correlation allows for prediction but does not explain why the relationship exists, whereas causation provides an explanation for the relationship.

Establishing Causation

1. To establish causation, researchers use experimental methods.
2. This involves manipulating one variable (the independent variable ) and observing its effect on another variable (the dependent variable).

When Experiments Aren't Possible

1. In some cases, experiments are not ethical or practical.
2. Researchers rely on correlational studies to identify relationships.

Why Correlation is Still Valuable

1. Prediction: Correlation allows researchers to predict outcomes. For example, knowing that exercise correlates with lower stress can inform health recommendations.
2. Hypothesis Generation: Correlational studies help generate hypotheses for future research. Researchers can test these hypotheses in experimental studies.
3. Ethical Considerations: Correlation is often the only option when experiments are unethical or impractical.