What is the difference between correlation and causation?

The concepts of correlation and causation are essential to understanding the relationships between variables. Let’s break them down and discuss how to identify confounding variables. 

Correlation refers to a relationship between two variables, where changes in one variable tend to be associated with changes in the other. For example, suppose you notice that ice cream sales increase on hotter days. This means there’s a positive correlation between temperature and ice cream sales: as temperature increases, ice cream sales also tend to go up.

Causation, on the other hand, implies that a change in one variable directly causes a change in the other. In our example, it’s tempting to think that the increase in temperature causes people to buy more ice cream. However, just because two variables are correlated doesn’t mean one causes the other. 

Confounding variables, also known as confounders, are variables that can affect both the predictor and outcome variables, leading to a false impression of causation. To illustrate, let’s look at our ice cream example again. One potential confounder could be the time of year. During summer months, people may be more likely to buy ice cream due to holidays and outdoor activities, and temperatures are naturally higher. The season may be the true cause of increased ice cream sales, not just the temperature itself. 

By carefully examining the relationships between variables and considering potential confounders, you can make more informed decisions about whether a correlation suggests a causal relationship or not. Just remember that correlation doesn’t always imply causation, and always look for confounding factors that could influence your results.