Statistics
The inference layer: what your numbers support, what they do not, and the tests people reach for too quickly.
14 articles, newest first.
- MAE, RMSE, or MAPE: what your regression metric secretly optimises Each error metric implies a different "best" prediction — median, mean, or something biased low. Choose by the cost of being wrong, not by habit.
- Sizing an experiment before you run it Power analysis without the ritual: choosing the smallest effect worth detecting, what drives the sample size, and why an underpowered test is worse than no test.
- Outliers: diagnose before you delete An outlier is an error, a whale, or a discovery — three different treatments. Robust statistics, sensible detection, and the customers you should not trim away.
- Your A/B test is probably lying to you Peeking, underpowered tests, and twenty metrics with one winner: the three failure modes that produce most false experiment wins, and the working defenses.
- The bootstrap: uncertainty for people who hate formulas Resample your data with replacement, recompute the statistic, read the spread — honest confidence intervals for medians, ratios, AUCs, and anything else.
- Simpson's paradox is hiding in your dashboard Aggregates can reverse when you split by segment — a mix-shift illusion that routinely misleads metric reviews. How to spot it and which number to trust.
- Choosing the unit of analysis Per user, per session, per order, per day. The choice quietly decides what your numbers mean, and getting it wrong invalidates the analysis before it starts.
- How to sample data without lying to yourself head(1000) is not a sample. Random, stratified, and entity-level sampling in SQL and pandas — and the bias traps in each.
- The central limit theorem, from first principles Why sums of many small independent effects turn into a bell curve, what the theorem promises about sample means, where it fails, and why "n > 30" is folklore.
- Bayes theorem from first principles Bayes as areas and counts rather than a formula: the medical-test example worked properly, why base rates dominate, and where the idea earns its keep in practice.
- PCA explained from first principles What a principal component actually is, why scaling comes first, how to read a scree plot, and the two things people wrongly expect PCA to do for them.
- Simpson's paradox from first principles Why an average over groups can point the opposite way to every group inside it, what the Berkeley admissions case actually showed, and how to decide which number to act on.
- P-values: what they say and what they do not What a p-value actually measures, the four things it gets mistaken for, how forking paths produce small p-values from nothing, and what to report instead.
- Correlation is not causation, but The three things that can produce any correlation, why the famous slogan stops thought instead of starting it, and what actually establishes a causal claim.