References: Summarizing data: center and spread

Source material

Source curriculum (structural mirror, cited as further study):
• Khan Academy, "Summarizing quantitative data" (Statistics & Probability)
  Author: Sal Khan and the Khan Academy team
  Unit page: https://www.khanacademy.org/math/statistics-probability/summarizing-quantitative-data
  License: CC BY-NC-SA 4.0
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Source-scope note: this lesson mirrors Khan's "Summarizing quantitative data"
unit (measures of center, measures of spread) and restates it in Clawdemy's
voice with original examples (the skewed-salary example and the eight-score
standard-deviation walk-through). The machine-learning connection
(standardization, z-scores, outlier sensitivity in training) is Clawdemy
framing that points forward to later lessons. Exact per-unit URLs are verified
at promotion.

Read this next

• Khan Academy: Summarizing quantitative data by Sal Khan and the Khan Academy team. The full unit this lesson mirrors, with short videos and practice for mean, median, mode, variance, and standard deviation, free and CC-licensed. The natural place to drill the computations until they are automatic.

Going deeper

A short, durable list. Both are free.

• Khan Academy, “Displaying and comparing quantitative data” (within the course above). The visual companion: histograms, box plots, and how the shape of a distribution reveals the skew this lesson summarizes numerically. Useful before Track 9 lesson 3.
• Khan Academy, “Modeling data distributions” (within the course above). Where the standard deviation becomes the z-score (how many standard deviations a value sits from the mean), the idea this lesson previews under standardization and that Track 9 develops in the normal-distribution lesson.

Adjacent topics

Where this sits inside this track.

• Why AI runs on statistics. The previous lesson. It made the case that AI reasons under uncertainty; this lesson is the first concrete tool, describing data honestly before any model touches it.
• The shape of data: distributions and histograms. The next lesson. Center and spread are numbers; the next lesson shows the picture they summarize and how shape (especially skew) jumps out of a histogram.
• The bell curve: the normal distribution. Later in the track (Phase 3). The standardization and z-score idea previewed here becomes central once the normal distribution is on the table.