Week 8 — Readings & Resources · The Normal Distribution
Course: Introduction to Statistics (18-week generic edition)
Objective covered: Objective 5 (normal-distribution portion) — use the normal model to compute and interpret proportions and percentiles.
Your primary reading is Chapter 8 (in this module — it carries the friendly z-table). Everything below is the optional, go-deeper layer.
How to use this page
Everything here is a link to an external resource — open it in your browser, the same way you'd open a YouTube link. Nothing needs to be downloaded, and nothing costs money.
The load is deliberately light: 4 short readings + 4 short videos, grouped by the four big ideas of the week. Read or watch one item per group and you're well prepared; do all of them and you'll be very comfortable. Total time is roughly 55–65 minutes if you do everything, far less if you pick one per group.
Order that matches the chapter and lecture: ① density curves & the normal model → ② the empirical rule (68–95–99.7) → ③ z-scores & normal calculations → ④ assessing normality (is the bell really there?).
A habit for this week: whenever a resource states a percentage, ask which z it corresponds to — then spot-check it against the friendly z-table in Chapter 8. If a claimed area and the table disagree, trust the table.
① Density Curves & the Normal Model
Maps to Chapter 8, Sections 1–2 and Lecture Segments 2–3. The whole game: area = proportion, and two numbers — μ and σ — draw the entire curve.
Reading — Normal Distribution: Examples, Formulas, & Uses (Scribbr)
🔗 https://www.scribbr.com/statistics/normal-distribution/
Why it's assigned: the cleanest plain-language tour of the normal curve's properties — symmetry, mean = median, and why the model shows up everywhere — with the empirical rule previewed on real-world examples.
⏱ ~8 min
Video — The Normal Distribution, Clearly Explained!!! (StatQuest with Josh Starmer) (video, ~5 min, captioned)
🔗 https://www.youtube.com/watch?v=rzFX5NWojp0
Why it earns the click: the fastest good intuition for what μ and σ each do to the curve — watch the bell stretch and slide as the two numbers change.
Video — An Introduction to the Normal Distribution (jbstatistics) (video, ~5 min, captioned)
🔗 https://www.youtube.com/watch?v=iYiOVISWXS4
Why it earns the click: a calm, precise walk through the normal density curve and its area-under-the-curve logic — the closest match to how this course frames Section 1.
② The Empirical Rule: 68–95–99.7
Maps to Chapter 8, Section 2 and Lecture Segment 3. The only numbers you memorize this week — plus their password: IF bell-shaped.
Reading — Empirical Rule: Definition & Formula (Statistics By Jim)
🔗 https://statisticsbyjim.com/basics/empirical-rule/
Why it's assigned: works the 68–95–99.7 slices exactly the way the lecture does (halving the middle, splitting the tails), with a worked delivery-times example you can mirror on the chapter's height model.
⏱ ~7 min
③ z-Scores & Normal Calculations
Maps to Chapter 8, Sections 3–4 and Lecture Segments 4–6. The recipe: standardize → look up the left-tail area → decide (below / above / between) — and run it backwards for percentiles.
Reading — The Standard Normal Distribution: Calculator, Examples & Uses (Scribbr)
🔗 https://www.scribbr.com/statistics/standard-normal-distribution/
Why it's assigned: the standardizing step and the z-table reading, with worked examples in both directions; its full z-table is the "grown-up" version of our friendly six values.
⏱ ~9 min
Video — Z-Scores and Percentiles: Crash Course Statistics #18 (CrashCourse) (video, ~10 min, captioned)
🔗 https://www.youtube.com/watch?v=uAxyI_XfqXk
Why it earns the click: the liveliest treatment of the week's signature move — using z to compare values from different distributions — and it polices the percentile-vs-score confusion the quiz loves.
Video — Standardizing Normally Distributed Random Variables (jbstatistics) (video, ~5 min, captioned)
🔗 https://www.youtube.com/watch?v=4R8xm19DmPM
Why it earns the click: shows why subtracting the mean and dividing by the SD turns every normal model into N(0, 1) — the reason one small table serves every problem.
④ Assessing Normality
Maps to Chapter 8, Section 5 and Lecture Segment 7. Before trusting any z-based percentage: histogram, actual 68/95 check, skew-and-outlier hunt.
Reading — Assessing Normality: Histograms vs. Normal Probability Plots (Statistics By Jim)
🔗 https://statisticsbyjim.com/basics/assessing-normality-histograms-probability-plots/
Why it's assigned: a candid look at how histograms can fool you when samples are small — read it for the histogram half; the normal-probability-plot half is a bonus preview of tools later courses formalize.
⏱ ~7 min
Optional one-stop reference (free online text)
If you'd like one optional reference to skim, OpenStax Introductory Statistics 2e keeps its full text free to read online. Chapter 6 (The Normal Distribution) covers this week end to end — the standard normal, z-scores, and finding areas in both directions.
🔗 https://openstax.org/books/introductory-statistics-2e/pages/6-introduction
Why it's here: a reputable, currently-available reference you can return to during midterm review — entirely optional this week.
Pick-one quick path (≈20 min total)
In a hurry? You've read Chapter 8 — then do exactly these three and you'll be ready for the quiz:
1. Read Empirical Rule: Definition & Formula (group ②).
2. Watch StatQuest — The Normal Distribution, Clearly Explained!!! (group ①).
3. Watch Crash Course #18 — Z-Scores and Percentiles (group ③).
Heads-up (links rot): these point to outside sites that occasionally move or rename pages. If a link ever fails, tell your instructor and use the OpenStax reference above in the meantime.