Course: Introduction to Statistics (18-week generic edition)
Module: Week 1 of 18 · planned around two ~75-minute sessions
Objective covered: Objective 1 — Distinguish populations from samples and identify appropriate sampling and study designs.

This file holds two pieces: (A) the Module 1 Overview page ("Start Here") and (B) the Welcome Announcement that drips out when the module opens. All timing is relative — "start of Week 1," "end of Week 1" — and maps onto real dates when the adopting instructor sets the term calendar.

(A) Module 1 Overview — Start Here

Welcome to Week 1: Statistics, Data & Study Design

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 the foundation the whole course is built on. Before we ever calculate anything, we have to answer a more basic question: where do numbers come from, and when do they deserve our trust? You already generate data all day long — ratings you tap, steps your phone counts, minutes an app logs. Statistics is the other side of that: how anyone turns numbers like yours into a claim about people they never met, and how you tell the honest claims from the garbage.

The week's big question

"Where do data come from, and when can the numbers be trusted to speak for more people than we actually measured?"

By the end of the week you'll be able to look at any statistic in the wild — a poll, a star rating, a "studies show" — and ask the three questions that decide whether it earns your trust: Who was measured? How were they picked? What was actually recorded?

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.

• [ ] Tell a population from a sample — and a parameter (a number describing the whole population) from a statistic (the matching number from your sample).
• [ ] Classify a variable by its level of measurement — nominal, ordinal, interval, or ratio (remember N-O-I-R), using the "does zero mean none?" test.
• [ ] Name a sampling method (simple random, stratified, cluster, systematic — or the traps: convenience and voluntary response) and say whether it's likely to be biased.
• [ ] Tell an observational study from an experiment, and explain why correlation isn't causation by pointing to a possible confounding variable.

What to do this week, in order

The table below lists the week's items in working order, with what each is worth and when it's due.

Heads-up on the AI work: in this course the chatbot drafts, and you judge. Chatbots routinely misclassify variables — they'll call a bus route number "ratio" (it's nominal) or a calendar year "ratio" (it's interval). Catching the model is the point, and this week's lab makes you do exactly that.

Late policy reminder: 10% off per day late. If life happens, reach out to your instructor before the deadline — early is always easier.

How to succeed this week

• Lead with the idea, not the notation. Every term this week is a plain-English idea first (population = everyone we want to know about; sample = the part we measured). The symbols (p, p̂) come after the idea clicks.
• Memorize two tiny hooks. "Population → Parameter, Sample → Statistic — the letters line up." And "N-O-I-R" for the four levels of measurement, in order of how much math they permit.
• Use the one-question test for levels. Does zero mean "none"? Yes → ratio. Equal gaps but zero is arbitrary → interval. Ordered labels, fuzzy gaps → ordinal. Just names → nominal.
• Remember the headline lesson: method beats size. A huge sample picked the wrong way is confidently wrong. (Ask in class about the poll with 2.4 million responses that still called the wrong winner.)
• Treat the chatbot as a smart intern, not an oracle. It drafts; you check. That habit is the whole course in miniature — and it's literally graded in the lab.

You don't need any background for this week — just curiosity and a willingness to question numbers. Come to the first session ready to argue about star ratings.

(B) Welcome Announcement — Module 1

Release setting: drips at the start of Week 1 (offset = 0 days from module start) — not before. If your platform won't preserve the scheduled release on import, post it as a draft labeled "Release: start of Week 1."

Subject: Welcome to Week 1 — let's learn to question numbers

Hi everyone, and welcome to Introduction to Statistics!

Quick question before we start: in the last 24 hours, did you generate any data? A rating tapped, steps counted, minutes streamed? You did — constantly. This whole course is about the other side of that: how anyone turns numbers like yours into a claim about people they never met, and how you tell the trustworthy claims from the junk.

This week — Statistics, Data & Study Design — we tackle the big question: Where do data come from, and when can the numbers speak for more people than we actually measured? By the end of the week you'll be able to look at any poll, rating, or "studies show" and ask the three questions that decide whether it deserves your trust: Who was measured? How were they picked? What was actually recorded?

Three things not to miss:
1. Chapter 1 is your primary reading — start there; it makes everything else this week easier.
2. Lecture Tutorial 1 — work through the week's ideas with your chatbot and submit the share link. You'll catch the model's mistakes, not just trust it. Due at the end of Week 1.
3. Data Lab 1 puts real Antarctic penguin research data in your spreadsheet — and the Discussion ("The 4.8-Star Problem") wants your initial post two days before the week ends, so classmates have time to reply.

One promise: this is a course about thinking clearly, not about being a "math person." We lead with plain-language ideas every single week; the notation comes second. If a number ever surprises you this term, you'll know exactly what to ask.

Open the Start Here / Module Overview page first — it lays out everything in order with due points. Bring your curiosity (and an opinion about whether a 4.8-star rating can be trusted) to the first session.

See you in the course!