Learn: Interpreting Risk - Correlation, Causation and Health Data

Welcome!Building on what you've already learned about cardiovascular health and diet, we'll now explore how scientists interpret risk using health data, focusing on correlation and causation. This topic will help you understand how evidence is evaluated in science.

What is Correlation?Correlation means a relationship between two variables, where changes in one are associated with changes in the other. For example, people who exercise more often might have lower blood pressure. However, correlation does not necessarily mean one causes the other.

What is Causation?Causation means one variable directly causes a change in another. For example, smoking causes damage to lung tissue, which increases the risk of lung cancer. Causation is stronger evidence than correlation, but it requires more rigorous testing to confirm.

Distinguishing Correlation from CausationScientists use experiments to test causation by controlling variables. For example, they might test whether a high-fat diet directly increases cholesterol levels. Observational studies, however, often show correlation, which requires careful interpretation to avoid incorrect conclusions.

Using Health DataHealth data is often used to study risks and make recommendations. For example, large studies comparing blood pressure and exercise levels help identify correlations. To confirm causation, scientists carry out controlled experiments to ensure other variables don't interfere.

Confounding VariablesConfounding variables are factors that can affect the results of a study, making it harder to determine causation. For example, a study might show that people who eat fewer vegetables have higher rates of heart disease, but a confounding variable could be that these individuals also exercise less.

Review Time!Great work! You've learned about correlation, causation, confounding variables, and how health data is interpreted. Now let's test your understanding with a few final questions.