Data Science & Deep Learning enthusiast. I write about R, Python, Haskell, and Julia โ with code examples you can run right here in the browser, no setup required.
Why accuracy lies on imbalanced data, and how precision, recall, F1, ROC-AUC, and precision-recall curves tell the real story โ built up from the math and implemented in Julia with MLJ.jl, on a synthetic churn dataset with a known, verified answer.
Simple random, systematic, stratified, and cluster sampling, plus the bootstrap โ explained with the math, implemented in both R and Julia, and checked with a Monte Carlo simulation that shows exactly why stratified sampling wins and cluster sampling doesn't.
What a p-value actually means, why 'statistically significant' isn't the same as 'real,' and why peeking at your A/B test results early quietly wrecks your false-positive rate โ demonstrated with a runnable Monte Carlo simulation, not just formulas.
