Learn: Interpreting Risk - Correlation, Causation and Health Data

Welcome!Great to see you're building on what you've learned about cardiovascular disease, diet, and health. Today, we'll explore how to interpret health data and understand the difference between correlation and causation. This is key to evaluating risks effectively!

What is Correlation?Correlation means there is a relationship between two variables, such as smoking and heart disease. When one changes, the other tends to change too. However, correlation does not necessarily mean one causes the other. For example, ice cream sales and drowning rates may both increase in summer, but buying ice cream does not cause drowning.

What is Causation?Causation means one variable directly causes a change in another. For example, smoking causes damage to blood vessels, increasing the risk of cardiovascular disease. Unlike correlation, causation requires evidence, such as controlled experiments.

Understanding Health DataHealth data often show correlations, but scientists must investigate whether a causal relationship exists. For example, studies might find a correlation between high saturated fat intake and heart disease. To prove causation, researchers need controlled experiments or long-term studies to rule out other factors.

Risk FactorsRisk factors are variables that increase the likelihood of developing a disease. For example, high cholesterol is a risk factor for cardiovascular disease. Scientists use statistical tests to determine whether risk factors are significant and whether they show correlation or causation.

Review Time!You're doing great! We've covered correlation, causation, and risk factors. Let’s review these ideas with a few questions to test your understanding.