Week 13 — Readings & Resources · Hypothesis Testing: Foundations

Course: Introduction to Statistics (MATH 11) · Silver Oak University (fictional sample) · Prof. Rivera
Objective covered: Objective 7 — Conduct and interpret hypothesis tests (this week: the logic and interpretation).

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.

This week is conceptual, so the load is light: ~4 short readings + ~4 short videos, grouped by the four ideas from the lecture. Read or watch one item per group and you're ready for the quiz; do all of them and you'll be very comfortable. Total time is roughly 45–55 minutes if you do everything, far less if you pick one per group.

Reading order that matches the lecture: ① the logic of a test + the null (H₀) and alternative (Hₐ) → ② the p-value and comparing it to α → ③ statistical significance (and statistical vs. practical) → ④ Type I and Type II errors.

A habit to start now: before you trust any "a study found a significant effect" claim, run the week's checklist — What's the null? How surprising are the data if it's true (the p-value)? Is "significant" being confused with "large"? Keep those in mind as you read.

① The Logic of a Test · The Null (H₀) and Alternative (Hₐ)

Maps to Lecture Segments 2–3. The whole idea in one image: a test is a courtroom — H₀ is "innocent" (nothing's going on), and the data have to overturn it "beyond a reasonable doubt."

Reading — "Hypothesis Testing | A Step-by-Step Guide with Easy Examples" (Scribbr)
🔗 https://www.scribbr.com/statistics/hypothesis-testing/
Why it's assigned: the cleanest plain-language walk through the exact pipeline we drew on the board — state the hypotheses, look at how surprising the data are, compare to α, conclude. Read the first half (through the p-value step) for this week.
⏱ ~8 min

Reading — "Null & Alternative Hypotheses | Definitions, Templates & Examples" (Scribbr)
🔗 https://www.scribbr.com/statistics/null-and-alternative-hypotheses/
Why it's assigned: nails the one move students get backwards — the "no effect / status quo" claim goes in H₀ (always with an "="), the claim you hope to show goes in Hₐ. Templates make stating them automatic.
⏱ ~6 min

Video — "Hypothesis Testing and The Null Hypothesis, Clearly Explained!!!" (StatQuest with Josh Starmer)
🔗 https://www.youtube.com/watch?v=0oc49DyA3hU
Why it earns the click: a friendly, visual tour of what the null hypothesis is and what "reject vs. fail to reject" actually means — the courtroom logic from Segment 2, made concrete.
⏱ ~10 min

② The p-value · Comparing it to α

Maps to Lecture Segment 4. The one sentence to carry: the p-value is how surprising the data would be if H₀ were true — small p means surprising means evidence against H₀. Then the rule: p ≤ α → reject; p > α → fail to reject.

Reading — "Understanding P-values | Definition and Examples" (Scribbr)
🔗 https://www.scribbr.com/statistics/p-value/
Why it's assigned: defines the p-value carefully and — crucially — warns against the classic misreading we hammered in class (the p-value is not "the probability the null is true").
⏱ ~7 min

Reading — "The idea of significance tests" (Khan Academy, article)
🔗 https://www.khanacademy.org/math/statistics-probability/significance-tests-one-sample/idea-of-significance-tests/a/idea-of-significance-tests
Why it's assigned: a tight, example-driven version of how a p-value gets compared to α to reach a decision — a good second pass if the first reading felt fast.
⏱ ~6 min

Video — "How P-Values Help Us Test Hypotheses: Crash Course Statistics #21"
🔗 https://www.youtube.com/watch?v=bf3egy7TQ2Q
Why it earns the click: the liveliest tour of how a p-value powers a hypothesis test — exactly the Segment-4 pipeline, with real examples.
⏱ ~11 min

③ Statistical Significance — and Statistical vs. Practical

Maps to Lecture Segments 5–6. The line to carry out of this week: "statistically significant" means "probably real," not "large or important." A big enough sample can make a trivial effect significant.

Reading — "An Easy Introduction to Statistical Significance (With Examples)" (Scribbr)
🔗 https://www.scribbr.com/statistics/statistical-significance/
Why it's assigned: explains what "significant at α = 0.05" does and does not mean, and includes a section on effect size / practical significance — the exact gap behind the "0.3-pounds-but-significant" example from Segment 6.
⏱ ~7 min

Video — "P-Value Problems: Crash Course Statistics #22"
🔗 https://www.youtube.com/watch?v=PPD8lER8ju4
Why it earns the click: the best short tour of how p-values get misused and misread — significant ≠ important, p-hacking, and why the threshold isn't magic. Pure Segment 5.
⏱ ~11 min

④ Type I and Type II Errors

Maps to Lecture Segment 7. Remember the framing: Type I = convict the innocent (false positive, reject a true H₀); Type II = let the guilty go free (false negative, miss a real effect).

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: clean definitions and pictures for the two ways a test can be wrong, including how α controls the Type I rate and why you can't shrink both errors at once.
⏱ ~7 min

Video — "p-values: What they are and how to interpret them" (StatQuest with Josh Starmer)
🔗 https://www.youtube.com/watch?v=vemZtEM63GY
Why it earns the click: a careful, visual explanation of what a p-value really is — the perfect antidote to all three classic misreadings before the quiz.
⏱ ~11 min

Optional one-stop reference (free online text)

If you'd like one optional reference to skim, OpenIntro Statistics keeps its full text free to read online. Chapter 5 ("Foundations for Inference") covers everything in this week — hypothesis testing, p-values, significance levels, and the two error types — and even has a short piece on "Why do we use 0.05 as a significance level?"
🔗 https://www.openintro.org/book/os/
Why it's here: a reputable, currently-available reference you can return to in Week 14 — entirely optional this week.

Pick-one quick path (≈15 min total)

In a hurry? Do exactly these four and you'll be ready for the quiz:
1. Read Null & Alternative Hypotheses (group ①).
2. Read Understanding P-values (group ②).
3. Read An Easy Introduction to Statistical Significance (group ③).
4. Watch StatQuest — p-values: how to interpret them (group ④).

Heads-up (links rot): these point to outside sites and YouTube channels that occasionally move, rename, or remove pages. If a link ever fails, tell Prof. Rivera and use the OpenIntro reference above in the meantime.