Title: Week 1 Overview — Start Here
Course: Introduction to Statistics (18-Week Generic Edition)

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.

# | Do this | Type | Due
1 | Read Chapter 1 — the module's primary reading | Chapter (ungraded prep) | Early in the week
2 | Skim the slides (Deck 1) and the Week 1 lecture outline; browse the Readings & Resources links that interest you | Prep (ungraded) | Alongside class
3 | Lecture Tutorial 1 — work through the week's ideas with your chatbot, then submit the share link + Completion Summary | Tutorial · graded (Lecture tutorials, 20% group) | End of Week 1
4 | Practice exercises — quick reps with the AI coach | Practice · ungraded | Before the quiz (recommended)
5 | Data Lab 1 — "Meet Your Data: The Penguins of Palmer Station" — import real research data, classify its variables, draw your own random sample | Data lab · graded (Data labs, 15% group) | End of Week 1
6 | Quiz 1 — population/sample, parameter/statistic, NOIR, sampling, bias, observational vs. experiment | Quiz · graded (Quizzes, 15% group) · closed to AI | End of Week 1
7 | Discussion 1 — "The 4.8-Star Problem" — interrogate a star rating in a dialogue with your chatbot, post the AI summary + chat link, then reply to two classmates | Discussion · graded (Discussions, 15% group) | Initial post two days before week's end; replies by end of Week 1
8 | Assignment 1 — "Numbers With a Backstory" — four AI-coached problems; submit the report (score on line 1) + chat link | Assignment · graded (Assignments, 25% group) | End of Week 1

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.