References: Statistics in machine learning

Source material

Source curriculum (structural mirror, cited as further study):
• Khan Academy, "Statistics & Probability" (the full course)
  Author: Sal Khan and the Khan Academy team
  Course page: https://www.khanacademy.org/math/statistics-probability
  License: CC BY-NC-SA 4.0
Clawdemy's lessons are original prose that follows the pedagogical arc of this
course. We do not embed, reproduce, or transcribe Khan's text or videos; we link
out 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 is a capstone that synthesizes the whole track rather
than mirroring a single Khan unit. The mapping of statistical tools onto a
machine-learning workflow, the "evaluation is inference" framing, the four
questions for a model claim, and the explicit boundary with the Classical
Machine Learning track are all Clawdemy framing, not drawn from a specific
source. It deliberately REFERENCES but does NOT teach the model-scoring toolkit
(confusion matrix, precision/recall, ROC/AUC, bias-variance), which is the
Classical Machine Learning track's material, to avoid duplicating that track.
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 whole track mirrors, free and CC-licensed. A good place to deepen any unit that this track only had room to introduce.

Going deeper

A short, durable list. Both are free.

• Clawdemy, the Classical Machine Learning track. The natural next step: it teaches the model-scoring toolkit this capstone hands off (the confusion matrix, precision and recall, ROC and AUC, the bias-variance tradeoff), building directly on the inference ideas from this track’s final phase.
• Khan Academy, “Significance tests” and “Confidence intervals” (within the course above). Revisit these two units together; the “evaluation is inference” core of this capstone rests on them, and seeing confidence intervals and hypothesis tests as two views of the same idea cements the phase.

Adjacent topics

Where this sits inside this track and beyond.

• Testing a claim: hypothesis testing and p-values. The previous lesson. The “is B really better than A” judgment is the engine of the capstone’s model-comparison example.
• Why AI runs on statistics. Lesson 1. The capstone closes the loop the opener began: AI speaks in probabilities, and statistics is the discipline for reasoning about them honestly.
• Classical Machine Learning (separate track). Where the model-scoring toolkit lives, built on this track’s foundation; the recommended next track for anyone who wants the precision-recall and ROC machinery.