Ryan Collison

While calculus rules physics, discrete math rules computing. Logic gates, encryption keys, social network structure, algorithm complexity — all rest on foundations of countable, finite mathematics.

At its core, statistics is a discipline about epistemic honesty: what can the data actually tell you, and what are you smuggling in as assumptions? This synthesis ties together estimation, inference, and the philosophical underpinnings of why statistics works.

Chi-square asks one sharp question: is this pattern real, or just noise? Whether testing independence between variables or fit against a model, the logic is the same — and surprisingly intuitive.

The problem with comparing five groups via t-tests is that you'll eventually get a false positive just by chance. ANOVA collapses the comparison into one omnibus test — that's the insight, not just the formula.

Two things rising together doesn't mean one causes the other. The ice cream-drowning correlation is a classic confound — and the same logical trap underlies most bad science journalism.

Linear regression fits a line through data by minimizing the sum of squared residuals. Simple in concept, powerful in practice — it's the foundation of statistical prediction, causal inference, and nearly all supervised learning.