"The frequentist toolkit is powerful and honest about its preconditions. Citizens who use it without checking the preconditions are using a powerful tool dishonestly."
What This Track Established
Six lessons, one toolkit. Confidence intervals (long-run hit rates of an interval-construction procedure), hypothesis tests (a structured procedure for asking 'is the evidence strong enough to overturn the default?'), p-values (P(data | null), not P(null | data)), p-hacking (selection bias inside the analysis pipeline), and the Type I / Type II asymmetry (the policy decision baked into every test). Together these are the frequentist workshop.
These tools do not all rest on one bell-curve assumption. Their validity depends on the sampling design, model, estimator, test statistic, and error-control procedure. Normal approximations are common, but exact, randomization, robust, and resampling methods have different conditions.
The Bridge to Track 06
Track 06 uses a bounded courtroom analogy: the presumption of innocence resembles a protected null position, and wrongful conviction resembles a false positive. The legal burden has no fixed α, and an acquittal does not identify a factual false negative.
The 'why are they letting that monster walk?' frame may ignore the special weight given to avoiding wrongful conviction. But the comparison remains an analogy: acquittal is not proof of factual guilt, and criminal procedure is not a repeated statistical test.
The Bridge to Track 08
Track 08 uses Bayes' rule to show why a likelihood cannot be inverted into a posterior without a prior and a complete evidence model. That mathematical lesson does not imply that a court should assign defendants demographic “priors of guilt.” Legal proof and statistical inference answer related but non-identical questions.