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01 ·Probability & Statistics·★ MEMBER·10 min read Hypothesis Testing and A/B Tests — How to Use a p-value, and How People Misuse It Ship the identical page to two groups and the numbers still won't match. A hypothesis test converts that gap into 'how many units of noise is this?' — that number is the p-value. From the null hypothesis up through the three failures that actually ruin experiments: power, peeking, and multiple comparisons. 02 ·Probability & Statistics·★ MEMBER·11 min read Thinking Bayesian — A Working Feel for Priors, Likelihoods, and Posteriors Bayesian updating is the act of feeding yesterday's posterior back in as today's prior. With a conjugate prior the whole update collapses into adding pseudo-counts, and an A/B test becomes two numbers: the probability of winning and the expected loss. From zero background to the traps in stopping rules and prior choice. 03 ·Probability & Statistics·★ MEMBER·8 min read Probability and Statistics for AI — A Model's Output Is a Distribution Classifiers and language models do not return answers; they return probability distributions. Distributions, expectation, conditional probability and Bayes explained from the symbols up — building to the payoff: why maximum likelihood is where loss functions come from. Cross-entropy and MSE were derived, not invented.