References: From sample to population: sampling and the central limit theorem

Source material

Source curriculum (structural mirror, cited as further study):
• Khan Academy, "Sampling distributions" (Statistics & Probability)
  Author: Sal Khan and the Khan Academy team
  Unit page: https://www.khanacademy.org/math/statistics-probability/sampling-distributions-library
  License: CC BY-NC-SA 4.0
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Source-scope note: this lesson mirrors Khan's "Sampling distributions" unit
(parameter vs statistic, the sampling distribution, the standard error, the
central limit theorem) and restates it in Clawdemy's voice with original
examples. It pays off the "why is the normal everywhere" preview from the
normal-distribution lesson. The AI framing (a test-set metric as a sample
estimate, the square-root law behind "more data helps") is Clawdemy framing.
Exact per-unit URLs are verified at promotion.

Read this next

• Khan Academy: Sampling distributions by Sal Khan and the Khan Academy team. The full unit this lesson mirrors, with videos and simulations of how sample means cluster and how the central limit theorem emerges, free and CC-licensed. The interactive sampling simulators there make the CLT click.

Going deeper

A short, durable list. Both are free.

• Khan Academy, “Modeling data distributions” (within the course above). Revisit the normal distribution and z-scores; the central limit theorem is what licenses using them on sample means, so the two lessons lock together.
• Khan Academy, “Confidence intervals” (within the course above). The direct next step: turning the standard error into a range of plausible values for the parameter. This is Track 9’s next lesson.

Adjacent topics

Where this sits inside this track.

• The bell curve: the normal distribution. Earlier in the track. The CLT is the reason the normal appears so often; this lesson answers the question that lesson left open.
• How sure are we? confidence intervals. The next lesson. It uses the standard error and the CLT to build a range around an estimate.
• Why AI runs on statistics. Lesson 1. The opener’s point that AI learns from a sample is exactly what this phase formalizes into inference.