Course: Introduction to Statistics (18-week generic edition)
Module: Week 2 of 18 · planned around two ~75-minute sessions
Objective covered: Objective 2 — Summarize and display univariate data (this week: the tables-and-graphs half).

This file holds two pieces: (A) the Module 2 Overview page ("Start Here") and (B) the Welcome Announcement that drips out when the module opens. All timing is relative — "start of Week 2," "end of Week 2" — and maps onto real dates when the adopting instructor sets the term calendar.

(A) Module 2 Overview — Start Here

Welcome to Week 2: Summarizing Data with Tables & Graphs

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.

Last week you learned to ask where numbers come from — who was measured, how they were picked. This week is the first thing statisticians do with a trustworthy column of data: turn it into a table and a picture. A pile of raw numbers is unreadable; the right graph makes its story instant. And because pictures persuade instantly, the week ends with self-defense: the design tricks that make honest numbers lie — and how to spot every one of them.

The week's big question

"How do you turn a pile of numbers into a picture your eyes can read — and how do you spot the pictures built to fool you?"

By the end of the week you'll look at every chart — in the news, in an ad, in your own spreadsheet — with two questions ready: is this the right display for this variable? and is it drawn honestly?

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.

• [ ] Build a frequency table and a relative-frequency table — counts, then shares (count ÷ total) — and run the built-in check: shares sum to 1.
• [ ] Choose and read the right display — bar chart or pie for categories (pie only for parts of one whole), histogram / dot plot / stem plot for numbers. Remember the giveaway: bars apart = categories; bars touching = a number line.
• [ ] Describe a distribution's shape — symmetric, skewed right, skewed left, uniform, bimodal — arguing skew from the tail (the tail tells the tale), and flag informal outliers (investigate, don't delete).
• [ ] Spot a misleading graph — truncated axis, area tricks, missing labels, cherry-picked buckets — and state the fix (bars start at zero; if you zoom, label it loudly).

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 flip a skew direction ("the tall bars are on the left, so it's skewed left" — wrong: skew follows the tail) and call histograms "bar charts." Catching the model is the point, and this week's lab and tutorial make 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 variable, then pick the picture. Categorical or quantitative (Week 1's skill) decides everything: categories → bars or a legal pie; numbers → dot plot, stem plot, or histogram.
• Memorize the two giveaway hooks. "Bars apart = categories; bars touching = a number line" and "the tail tells the tale." Between them they answer half the quiz.
• Run the built-in checks. Counts sum to the total; relative frequencies sum to 1; pie slices sum to 100%. If a check fails, the table is confessing.
• Check the axis floor before you read any bar chart. Bars start at zero. If a graph zooms, it owes you a loud label.
• Do the lab's lying-axis exercise with feeling. Once you've drawn the 8× lie from a 1.2× truth yourself, no ad will ever pull it on you again.

You don't need any math beyond dividing a count by a total this week — just your eyes, a spreadsheet, and healthy suspicion. Come to the first session ready to be fooled by a graph, once, on purpose.

(B) Welcome Announcement — Module 2

Release setting: drips at the start of Week 2 (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 2."

Subject: Week 2 — turning numbers into pictures (and catching the pictures that lie)

Hi everyone,

Nice work getting through Week 1 — you can now ask the three questions that decide whether a number deserves trust: who was measured, how were they picked, what was recorded. This week we finally do something with the data: turn it into pictures.

This week — Summarizing Data with Tables & Graphs — the big question is: How do you turn a pile of numbers into a picture your eyes can read — and how do you spot the pictures built to fool you? You'll build frequency tables, bar and pie charts, histograms, dot plots, and stem plots — learn to describe a distribution's shape — and finish with the fun part: the four classic tricks behind misleading graphs.

Three things not to miss:
1. Chapter 2 is your primary reading — start there; every graph this week gets easier after it.
2. Data Lab 2 brings back the penguins: you'll draw their first real pictures, then build a deliberately lying chart on purpose (and fix it). Due at the end of Week 2.
3. Discussion 2 — "The Zoomed-In Axis" — wants your initial post two days before the week ends, so classmates have time to reply. When is a graph allowed to not start at zero? You'll have opinions.

One habit to carry all week: before you read any bar chart, check where the axis starts. You'll be amazed how often that one glance changes the story.

Open the Start Here / Module Overview page first — it lays out everything in order with due points. Bring a graph you've seen recently (any app or ad will do) — we'll put it under oath.

See you in the course!