Advanced Statistics
Digging Deeper into Uncertainty
This section goes beyond the basics into the deeper reasoning behind statistical inference. Topics include Bayesian thinking, experimental design, statistical power, and the assumptions underlying the tests most people use without questioning. For when you need to not just use statistics, but understand what they're actually saying.
A/B Testing
intermediateThe structured way companies test whether a change actually works — or just feels like it does.
ANOVA
intermediateLike a t-test, but for when you have more than two groups to compare.
Assumptions of Statistical Tests
intermediateEvery statistical test has a list of things it assumes are true about your data — and most people never check them.
Bayesian Thinking
intermediateA completely different way of thinking about probability — not as frequencies, but as beliefs that update.
Effect Size
beginnerStatistical significance tells you something is real. Effect size tells you if it actually matters.
The Multiple Testing Problem
intermediateRun enough tests and something will look significant. That's a bug, not a discovery.
Statistical Power
intermediateHow good is your test at actually catching something real when it's there?
Type I & Type II Errors
beginnerThe two ways a hypothesis test can lie to you — and why fixing one makes the other worse.