References: Probability foundations

Source material

Source curriculum (structural mirror, cited as further study):
• Khan Academy, "Probability" (Statistics & Probability)
  Author: Sal Khan and the Khan Academy team
  Unit page: https://www.khanacademy.org/math/statistics-probability/probability-library
  License: CC BY-NC-SA 4.0
Clawdemy's lessons are original prose that follows the pedagogical arc of this
unit. We do not embed, reproduce, or transcribe Khan's text or videos; we link
out to the relevant unit as recommended further study. The non-commercial
clause aligns with Clawdemy's free, zero-revenue posture. All rights to the
original materials remain with their authors.

Source-scope note: this lesson mirrors Khan's "Probability" unit (sample
spaces, the complement, addition, and multiplication rules, independence) and
restates it in Clawdemy's voice with original worked examples (dice, coins,
cards). The AI connections (pipeline reliability, the at-least-one shortcut,
scoring a sentence by multiplying word probabilities) are Clawdemy framing.
Conditional probability for dependent events is deliberately deferred to the
next lesson. Exact per-unit URLs are verified at promotion.

Read this next

• Khan Academy: Probability by Sal Khan and the Khan Academy team. The full unit this lesson mirrors, with videos and practice on basic probability, the complement, and the addition and multiplication rules, free and CC-licensed. The place to drill the three rules until they are automatic.

Going deeper

A short, durable list. Both are free.

• Khan Academy, “Counting, permutations, and combinations” (within the course above). When outcomes are equally likely but numerous, counting them is its own skill; this unit is the toolkit for “favorable over total” when the totals get large. Useful background for the binomial distribution later in Track 9.
• Khan Academy, “Conditional probability” (within the course above). Where the independence fine print of this lesson is lifted: how to handle events that do influence each other. This is the direct source for Track 9’s next lesson.

Adjacent topics

Where this sits inside this track.

• When two things move together: correlation. The previous lesson, which closed Phase 1. The track now shifts from describing data to reasoning about chance.
• When one event tells you about another: conditional probability and independence. The next lesson. It picks up exactly where this one’s independence caveat leaves off, handling dependent events.
• Updating beliefs with evidence: Bayes’ theorem. Two lessons ahead. The base-rate example from lesson 1 becomes a formula, built on conditional probability.