How do you differentiate between causation and correlation in scatter plots?

1 Basics First

Before diving into the intricacies of scatter plots, you need to understand the basic difference between causation and correlation. Correlation occurs when two variables have a tendency to move in sync with each other, but this doesn't mean one causes the other. Causation, on the other hand, means that one variable's change is responsible for the change in another variable. In business intelligence, recognizing this difference prevents incorrect assumptions that could lead to faulty strategies.

3 Pattern Spotting

To differentiate between causation and correlation in scatter plots, look for patterns in the data points. A linear pattern where points seem to follow a straight line suggests a strong correlation. However, correlation does not equal causation. Even if the data points form a perfect line, it doesn't prove that one variable is causing the change in another. It simply means they tend to change together under certain conditions.

4 Context Matters

Understanding the context of the data is essential when interpreting scatter plots. If there's a known mechanism or theory that explains why one variable should affect the other, it leans more towards causation. Without such context, you might be observing a coincidental correlation. For instance, sales and marketing spend might show a positive correlation, but without understanding the marketing strategy's effectiveness, you cannot conclude that increased spending causes higher sales.

5 Further Analysis

To establish causation, you often need to perform further statistical analysis beyond what a scatter plot can provide. This could involve controlled experiments or regression analysis where you control for other variables that might affect the relationship. If after controlling for these variables the relationship still holds, you might be looking at causation rather than mere correlation.

6 Common Pitfalls

One common pitfall in BI is the assumption that correlation implies causation. This can lead to misguided decisions. For example, if you notice that two products often sell together, it doesn't necessarily mean that promoting one will increase sales of the other. It's important to question whether other factors could be influencing sales, such as seasonality or promotions, before drawing conclusions.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?