Course: Introduction to Statistics (MATH 11) · Silver Oak University (fictional sample) · Prof. Rivera
Module: Week 13 of 16 · Fall 2026 · in-person, two 75-minute sessions
Objective covered: Objective 7 — Conduct and interpret hypothesis tests (this week: the logic and interpretation).

This file holds two pieces: (A) the Module 13 Overview page ("Start Here") and (B) the Welcome Announcement that drips out when the module opens. Thanksgiving week: Week 13 meets Tuesday Nov 24 only — Thursday Nov 26 is Thanksgiving and campus is closed Thu Nov 26–Fri Nov 27 (no class, no office hours). Because of the holiday, end-of-week work is due the following Sunday Nov 29, 11:59 p.m. (extended past the holiday weekend). Adjust day-of-week and times to match your section.

(A) Module 13 Overview — Start Here

Welcome to Week 13: Hypothesis Testing — Foundations

This is your home base for the week. Read it first, then work the checklist below from top to bottom. Everything you need is linked inside the module.

This week is where everything we've built turns into a decision. All term you've measured samples — and samples wobble. So when a study shows an effect, how do we decide whether it's real or just the kind of random wobble chance produces all the time? That's a hypothesis test, and this week is about its logic and its honest interpretation — not heavy math. By Friday you'll read "a study found a significant effect" and know exactly what it does, and does not, mean.

🦃 Heads-up — Thanksgiving week. We meet Tuesday Nov 24 only. Thursday Nov 26 is Thanksgiving, and campus is closed Thursday and Friday — no class and no office hours. So you can actually enjoy the break, this week's Quiz 13, Discussion 13, and Assignment 13 are all due Sunday Nov 29 (extended past the holiday weekend). The one earlier date is the Discussion initial post, due Tue Nov 24, so classmates have something to reply to over the break.

The week's big question

"When a sample shows an effect, how do we decide whether it's real — or just the kind of wobble random chance produces all the time?"

By the end of the week you'll be able to take any "a study found…" claim and run the logic yourself: state what "nothing's going on" would mean (H₀), state the rival claim (Hₐ), look at how surprising the data are if nothing's going on (the p-value), compare that to a line drawn in advance (α), and say — in plain English — whether there's real evidence or just noise.

By the end of this week, you can…

Use this as a checklist. If you can do all four out loud, you're ready for the quiz.

• [ ] State a null (H₀) and alternative (Hₐ) hypothesis for a described claim — H₀ is the "no effect / status quo" claim (always with an "="); Hₐ is what the researcher hopes to show.
• [ ] Explain what a p-value is — how surprising the data would be if H₀ were true — and compare it to α to decide reject (p ≤ α) or fail to reject (p > α), then state the conclusion in context.
• [ ] Tell a Type I error (rejecting a true H₀ — a false alarm) from a Type II error (failing to reject a false H₀ — a miss), and name a consequence of each.
• [ ] Catch the three classic misreadings: the p-value is not "the probability H₀ is true"; "fail to reject" is not "H₀ proven true"; "statistically significant" is not "large or important."

What's due this week, and when

Work these in order — each one gets you ready for the next.

Heads-up on the AI tutorial: you'll use a chatbot to draft, and then you judge its work against what we cover in class. Chatbots routinely write the forbidden sentence — "p = 0.03 means there's a 3% chance the null is true" — or slide from "significant" to "large/important." Catching and rewriting those is the point.

Late policy reminder: 10% off per day late. The deadline is already pushed to Sun Nov 29 to clear the holiday — but if life happens, reach out before then; I'd much rather hear from you early.

How to succeed this week

• Lead with the logic, not the formula. This week has almost no computation — every number (like p = 0.03 vs. α = 0.05) is handed to you. The skill is reasoning and interpreting, so focus there.
• Memorize the courtroom. A test is a trial: H₀ = innocent ("nothing's going on"), Hₐ = guilty, the data are the evidence, and α is "beyond a reasonable doubt." Two verdicts only — reject (guilty) or fail to reject (not guilty) — and "not guilty" is not "innocent."
• Hold onto one sentence about the p-value. "The p-value is how surprising the data would be if H₀ were true — so it can't be the probability H₀ is true." That one line defeats the most common mistake in the news.
• Always finish the sentence. A conclusion isn't "reject H₀"; it's "At the 0.05 level, we have significant evidence that [the real-world claim]." The sentence about the world is the answer.
• Remember: significant ≠ big. "Statistically significant" means probably real, not important. A huge sample can make a trivial effect significant — always ask for the effect size too.

You don't need new math for this week — just careful thinking about evidence. Come to class Tuesday ready to argue about a headline. And have a wonderful Thanksgiving.

(B) Welcome Announcement — Module 13

Release setting: post on the module's start day (offset = 0 days), i.e., Tue Nov 24, 2026 — not before. If your platform won't preserve the scheduled date on import, post this as a draft labeled "Release: Tue Nov 24."

Subject: Week 13 — what "statistically significant" really means 🔬🦃

Hi everyone,

Quick question to start the week: when a news headline says a study found a "statistically significant" result, what does that actually mean? Most people read it as "the effect is big and important." It doesn't mean that at all — and by Friday you'll know exactly what it does mean.

This week — Hypothesis Testing: Foundations — we tackle the big question: when a sample shows an effect, is it real, or just the random wobble chance produces all the time? You'll learn to state a null (H₀) and alternative (Hₐ) hypothesis, read a p-value (how surprising the data would be if nothing were going on), compare it to a threshold α, and conclude in plain English — plus tell a Type I error (a false alarm) from a Type II error (a miss). It's a thinking week, not a math week — almost no computation.

🦃 Thanksgiving note (please read): we meet Tuesday Nov 24 only — Thursday is Thanksgiving and campus is closed Thursday and Friday (no class, no office hours). So you can enjoy the break, Quiz 13, Discussion 13, and Assignment 13 are all due Sunday, Nov 29. The only earlier piece is the Discussion initial post, due Tue Nov 24, so people have something to reply to over the weekend.

Two things not to miss:
1. Lecture Tutorial 13 — work the logic with one approved chatbot (Gemini, Claude, or ChatGPT) and submit the share link. You'll catch the model's classic mistakes (it loves to say "there's a 3% chance the null is true" — which is wrong). Due Sun Nov 29.
2. Discussion 13 asks you to find a real "significant" headline and figure out what it truly means — initial post Tue Nov 24, replies Sun Nov 29.

One callback: Weeks 11–12 you built confidence intervals — an honest range for a parameter. A hypothesis test is the yes/no cousin: is a specific claimed value too far from our data to believe? Same evidence, a different question.

Open the Start Here / Module Overview page first — it lays out everything in order with due dates. Bring your curiosity (and a "significant" headline that annoyed you) to class on Tuesday — and have a wonderful Thanksgiving.

See you soon,
Prof. Rivera