References: Why AI runs on statistics

Source material

Source curriculum (structural mirror, cited as further study):
• Khan Academy, "Statistics & Probability"
  Author: Sal Khan and the Khan Academy team
  Course page: https://www.khanacademy.org/math/statistics-probability
  License: CC BY-NC-SA 4.0
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Source-scope note: this is an orientation lesson, so it maps the whole course
rather than mirroring a single unit. The base-rate worked example previews the
conditional-probability and Bayes material (Khan's "Probability" and
"Conditional probability" units), which Track 9 develops in full in Phase 2. The
forward-versus-backward framing (probability vs statistics) is standard pedagogy
restated in Clawdemy's voice. Exact per-unit URLs are verified at promotion.

Read this next

Khan Academy: Statistics & Probability by Sal Khan and the Khan Academy team. The full course this track mirrors, with short videos and practice for every idea Track 9 covers, free and CC-licensed. Its “Probability” and “Conditional probability” units are the natural companion to this lesson’s base-rate example.

Going deeper

A short, durable list. Both are free.

Khan Academy, “Probability” unit (within the course above). The worked-out version of the forward direction: sample spaces, events, and the rules this lesson only previewed. The grounding for Track 9 Phase 2.
Khan Academy, “Conditional probability” unit (within the course above). The home of the base-rate example worked here by counting. This is where the intuition becomes Bayes’ theorem, which Track 9 lesson 7 develops.

Adjacent topics

Where this leads inside this track.

Summarizing data: center and spread. The next lesson. Before any model learns, someone describes the data, and that is where Phase 1 begins.
Updating beliefs with evidence: Bayes’ theorem. Later in the track (Phase 2). The base-rate example here is a preview; that lesson turns the counting into the formula and the habit of mind.
Testing a claim: hypothesis testing and p-values. Late in the track (Phase 4). The backward direction in full: deciding whether an observed difference, such as one model scoring higher than another, is real or noise.