Everything up to here was training. You have the tools now - distributions, estimation, the linear model, reliability, resampling, Bayes, and the craft of showing data honestly. What no chapter can hand you is judgment: which tool, when, and why - and, just as important, when not to reach for the fancy one. Judgment is learned the way any craft is learned: by watching someone work, seeing the decisions, and hearing why they went one way and not the other. That is what this last part of the book is - an apprenticeship. We stop explaining tools in the abstract and start using them in public, on real projects, with every choice laid bare.
We begin with the friendliest lab I know: The Sports Page, a sister project to this book - a daily newsletter that takes one strange sports number and tells the truth about what it does and does not mean. Sports is a good teacher because the data are rich, the stakes are low, and the future arrives every single day to grade your predictions. If you can be honest about a baseball number, you can be honest about a clinical one.
The whole discipline reduces to three verbs, and the first rule is to always know which one you are doing:
Most sports writing stops at #1 and pretends it is doing #2. Most analytics stops at #2 and pretends it is doing #3. The single most useful habit you can build is to label your own work honestly: this is description, this is prediction, this is a causal claim I have not earned.
Here is the idea that separates a describer from a forecaster: any model flexible enough will fit the past perfectly, and that tells you almost nothing. Fitting the past is easy because you already know how it turned out. The real test is predicting data you have not seen, and then acknowledging it when you were wrong. That is why The Sports Page grades every prediction it makes, on a schedule, in public. Being wrong is not failure; it is the information that improves the next model. If you never publish a prediction that could turn out wrong, you never give yourself a chance to learn.
Every April, someone goes 6-for-9 and gets talked about as an MVP. Every April, that call turns out to be premature, and you already have the tools to know why. It is the same lesson as statistical power: small samples are noisy. Watch what happens when we plot every hitter’s performance against how many chances they have had:
library(ggplot2)
h <- read.csv("data/mlb_hitters_2026.csv") # a partial season, all hitters
ggplot(h, aes(PA, OPS)) +
geom_point(alpha = 0.35, color = "steelblue") +
geom_hline(yintercept = median(h$OPS[h$PA >= 200]), color = "firebrick", linewidth = 0.8) +
labs(x = "Plate appearances", y = "OPS",
title = "The funnel: extremes only survive at small samples") +
theme_minimal(base_size = 12)
The plot is a funnel. On the right, where hitters have 200+ chances, everyone clusters near the league median (the red line). On the left, where a hitter has batted a handful of times, OPS ranges from near zero to the stratosphere. The numbers make it stark:
c(sd_OPS_under_20_PA = round(sd(h$OPS[h$PA < 20]), 2), # tiny samples: huge spread
sd_OPS_over_200_PA = round(sd(h$OPS[h$PA >= 200]), 2), # big samples: tight
best_OPS = round(max(h$OPS), 2),
its_plate_appearances = h$PA[which.max(h$OPS)]) sd_OPS_under_20_PA sd_OPS_over_200_PA best_OPS
0.59 0.10 3.25
its_plate_appearances
4.00 The best OPS in the league belongs to someone with a single-digit number of at-bats. That is not a hitter who has cracked the code; that is mostly sampling noise in a very small sample. The honest read: a hot start is description of a tiny sample, and mistaking it for prediction is one of the most common errors in sports, and in science more broadly. The player will “regress to the mean” not because of some mysterious force, but because the extreme value was mostly luck, and luck does not carry over to the rest of the season.
This is the entire apprenticeship in one figure. The math was easy (a standard deviation). The judgment, recognizing that the April headline is describing noise while treating it as signal, is the hard part, and the valuable one.
Return to the strikeout revolution. Notice how the three verbs stack:
Same data, three very different epistemic commitments. Keeping them straight is not pedantry; it is the difference between careful analysis and guessing.
With the philosophy in hand, we turn to the real thing: published research, walked through end to end, with every decision on the table. You will watch measures get built and defended, watch a total score throw away information it should have kept, and watch missing data quietly change an answer. These are not toy examples; they are real studies, real datasets, and real judgment calls, some of which we would make differently today. That honesty is the point. Welcome to the apprenticeship; let’s go to work.