Understanding the Interpretation of Correlation
📊🔍 Why can we not say that one thing causes another based on a correlation coefficient or a strong correlation in a scatterplot? 🤔🤔
While a correlation coefficient or a strong correlation in a scatterplot can suggest a relationship between two variables, we cannot say that one thing causes another based on this alone. 🚫🧐
Think of it like a basketball game🏀 where a player scores a lot of points. We cannot assume that the player’s high score caused the team to win. There may be other factors at play, such as the performance of other players, the strategy of the coach, or even luck.
Similarly, in statistics, correlation does not imply causation. There may be other variables or factors that affect the relationship between the two variables we’re interested in.
📈 For example, let’s say we observe a strong positive correlation between the number of ice cream sales and the number of drownings in a city. We cannot conclude that eating ice cream causes people to drown. Instead, there may be a third variable, such as temperature, that affects both ice cream sales and swimming behavior.
When two variables appear to be correlated, but the relationship is actually caused by a third variable, we call it a spurious relationship (or correlation).
👉 For example, there is a strong correlation between the number of Nicholas Cage movies and the number of swimming pool drownings in a year. This is a spurious relationship, and the common cause is the temperature.
📊 Understanding correlation and causation can help us avoid making incorrect assumptions or decisions based on data. So next time you observe a correlation, think like a detective and consider other factors that may be involved! 🕵️♀️🔎