Probability & Statistics
Reasoning under uncertainty
The statistical intuition behind every ML claim — distributions, Bayes, hypothesis testing, and the traps that fool everyone.
Beginner3 modules · 12 lessons~93 min860 XPAligned with StatQuest · Khan Academy
Progress0/12
Module
Probability Intuition
Chance, conditioning, and the theorem that updates beliefs
Two Ways to Think About Chance7 min · 60 XPTwo Ways to Think About Chance
The Condition That Changes Everything8 min · 70 XPThe Condition That Changes Everything
Bayes' Theorem: The Medical Test9 min · 80 XPBayes' Theorem: The Medical Test
Expected Value: The Long-Run Average7 min · 65 XPExpected Value: The Long-Run Average
Module
Distributions
The shapes data takes — and where those shapes break
Why the Bell Curve Is Everywhere8 min · 70 XPWhy the Bell Curve Is Everywhere
Variance & Standard Deviation7 min · 65 XPVariance & Standard Deviation
When Normal Fails: The Long Tail8 min · 75 XPWhen Normal Fails: The Long Tail
Module
Statistical Thinking
Samples, tests, and the traps that fool everyone
The Sample Lies8 min · 75 XPThe Sample Lies
p-Values: What They Say (and Don't)9 min · 80 XPp-Values: What They Say (and Don't)
Correlation ≠ Causation7 min · 70 XPCorrelation ≠ Causation
Confidence Intervals: The Honest Range7 min · 70 XPConfidence Intervals: The Honest Range
Simpson's Paradox: When Aggregates Lie8 min · 80 XPSimpson's Paradox: When Aggregates Lie
Capstone project
Chance in the wild
Find a news headline or ad that uses statistics misleadingly — survivorship bias, base-rate neglect, cherry-picked averages. Reconstruct what the honest version would say.
Deliverable: A short write-up: the claim, the statistical trick, and the corrected interpretation.
Milestones
Finish every lesson to earn the Probability & Statistics Certificate.