Check out this nice summary of common ways we go wrong interpreting data, including one of my favorites, Simpson’s Paradox.

Some fun examples of the False Causality pitfall can be found at Spurious Correlations, where we learn, for example, that per capita cheese consumption is remarkably strongly related to the number of people who die each year by becoming entangled in their bedsheets.

Statistical fallacies are common tricks data can play on you, which lead to mistakes in data interpretation and analysis. Explore some common fallacies, with real-life examples, and find out how you can avoid them.