Regression & Correlation vs. Causation

Testing whether two things are correlated is one of the most useful tools in data analysis — it can surface a real relationship worth acting on, or show that something you assumed mattered actually doesn't. But it's also easy to be misled: a strong correlation can be coincidental, driven by a hidden third factor, or point the wrong way entirely once you split the data into subgroups. Six small interactive demos below cover both sides — two on how linear regression works, and four on ways correlation can mislead you if you take it at face value.

Pick a tab below to try one.

What this teaches: Fit a regression line, vary the strength of a correlation, and explore Anscombe's quartet, spurious correlations, confounding variables and Simpson's paradox. It teaches the correlation coefficient and why correlation does not establish causation.