Title: Week 13 — Readings & Resources · Hypothesis Testing: Foundations
Course: Introduction to Statistics (18-Week Generic Edition)

Course: Introduction to Statistics (18-week generic edition) Objective covered: Objective 7 — Carry out and interpret hypothesis tests (this week: the logic — hypotheses, p-values, α, and error types). Your primary reading is Chapter 13 (in this module). 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 60–70 minutes if you do everything, far less if you pick one per group.

Order that matches the chapter and lecture: ① the two hypotheses & the logic of a test → ② the p-value and what it really means → ③ α, decisions & the two error types → ④ statistical vs. practical significance.

A habit to start now: every time you meet the word "significant" this week (or in any headline, forever), ask the two questions in order — How surprising is this, if nothing's going on? and How big is the effect?

① The Two Hypotheses & the Logic of a Test

Maps to Chapter 13, Sections 1–2 and Lecture Segments 2–3. The frame to keep: every test is a tiny courtroom — H₀ gets the benefit of the doubt, and the data are the evidence.

Reading — Hypothesis Testing: A Step-by-Step Guide with Easy Examples (Scribbr) 🔗 https://www.scribbr.com/statistics/hypothesis-testing/ Why it's assigned: walks the same five-step skeleton as our "Anatomy of a Test" slide — hypotheses, evidence, p-value, decision, conclusion — with clean worked examples. ⏱ ~7 min

Video — Hypothesis Testing and The Null Hypothesis, Clearly Explained!!! (StatQuest with Josh Starmer) (video, ~15 min, captioned) 🔗 https://www.youtube.com/watch?v=0oc49DyA3hU Why it earns the click: the friendliest walk through why the null — the dull explanation — is the thing we test, with StatQuest's signature small examples.

② The p-Value — What It Really Means

Maps to Chapter 13, Section 3 and Lecture Segments 3–4. The definition, one more time: the probability, assuming H₀ is true, of data at least as extreme as yours. Never the probability that H₀ is true.

Reading — Understanding P-values: Definition and Examples (Scribbr) 🔗 https://www.scribbr.com/statistics/p-value/ Why it's assigned: a compact, plain-language treatment of the p-value with a table of interpretations — including a direct confrontation with the misreadings our quiz targets. ⏱ ~6 min

Video — p-values: What they are and how to interpret them (StatQuest with Josh Starmer) (video, ~11 min, captioned) 🔗 https://www.youtube.com/watch?v=vemZtEM63GY Why it earns the click: the best plain-English account of what a p-value is (and isn't), built from coin-flip examples that mirror this week's Data Lab.

Video — How P-Values Help Us Test Hypotheses: Crash Course Statistics #21 (CrashCourse) (video, ~12 min, captioned) 🔗 https://www.youtube.com/watch?v=bf3egy7TQ2Q Why it earns the click: the liveliest tour of the whole null-hypothesis-significance-testing dance — how "innocent until proven guilty" became a scientific method.

③ α, Decisions & the Two Ways to Be Wrong

Maps to Chapter 13, Sections 4–5 and Lecture Segments 5–6. The hooks: α is your false-alarm budget, chosen before the data — and Type I cries wolf when there's no wolf; Type II sleeps through the real one.

Reading — Type I & Type II Errors: Differences, Examples, Visualizations (Scribbr) 🔗 https://www.scribbr.com/statistics/type-i-and-type-ii-errors/ Why it's assigned: crisp side-by-side definitions (false positive vs. false negative) with pictures of how the two risks trade off — exactly the spam-filter dilemma from lecture. ⏱ ~7 min

Video — Type I Errors, Type II Errors, and the Power of the Test (jbstatistics) (video, ~9 min, captioned) 🔗 https://www.youtube.com/watch?v=7mE-K_w1v90 Why it earns the click: calm, precise illustrations of both error types in context. (Its last section introduces "power" — a topic beyond this course's scope; treat that part as an optional preview, not required.)

④ Statistically Significant vs. Actually Important

Maps to Chapter 13, Section 6 and Lecture Segment 7. The line to carry out of this week: statistical significance measures surprise, not size — with 80,000 runners, even 4 meaningless seconds earn p = 0.001.

Reading — An Easy Introduction to Statistical Significance, With Examples (Scribbr) 🔗 https://www.scribbr.com/statistics/statistical-significance/ Why it's assigned: connects α, p-values, and significance in one place, then lands the week's closing lesson — why researchers report effect sizes alongside p-values, and what "practically significant" adds that p never can. ⏱ ~8 min

Optional one-stop reference (free online text)

If you'd like one optional reference to skim all term, OpenStax Introductory Statistics 2e keeps its full text free to read online. Chapter 9 (Hypothesis Testing with One Sample) covers this week end to end — null and alternative hypotheses, Type I and II errors, and the decision logic — and its later sections preview Week 14's machinery. 🔗 https://openstax.org/books/introductory-statistics-2e/pages/9-introduction Why it's here: a reputable, currently-available reference you can return to next week, when the t-test mechanics arrive — entirely optional this week.

Pick-one quick path (≈25 min total)

In a hurry? You've read Chapter 13 — then do exactly these three and you'll be ready for the quiz: 1. Watch StatQuest — p-values: What they are and how to interpret them (group ②). 2. Read Type I & Type II Errors (group ③). 3. Read An Easy Introduction to Statistical Significance (group ④).

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.