Statistics - both wonderfully insightful and easily to screw up.

Let's discuss what statistical estimates actually tell us:

• What's an "average treatment effect"? When's it meaningful vs not? FYI, half the statistics you've ever seen are this.
• What actually are statistical significance and p-values? A favourite tool of statisticians, and misunderstood by 95.6% of individuals.
• What makes the results of a regression model meaningful? Understanding how to make sense of the shape of data and fitting a linear model.
• Getting from correlation to causation. The various techniques in causal inference.
• Does the context of the stat apply to your context? Understanding external validity.

For further reading (aimed at those with a somewhat quantitative background), this little known gem: Regression Analysis: A Practical Introduction by Jeremy Arkes