Title: Week 10 Discussion
Course: Introduction to Statistics (18-Week Generic Edition)

Course: Introduction to Statistics (18-week generic edition) Objective: Objective 5 (sampling distributions; SD vs. SE) · SLO B (communicate to a non-technical audience) Discussion 10 · 10 points · Discussions group = 15% of the grade Format: adaptive learning — instead of writing a post cold, you'll think it through in a real-time dialogue with your own AI, then post the short summary the AI writes with you (plus a link to your chat).

Part 1 — Student Instructions (read this first)

What this is. Companies love to promise averages: "average delivery 30 minutes," "average contents 200 g," "average wait under 2 minutes." This week you learned why averages are the best-behaved numbers in statistics — steady, predictable, bell-shaped. But you never experience an average. You experience one bag, one delivery, one wait. You'll interrogate one real average-based promise in a back-and-forth with an AI chatbot. The AI's job is to draw out and challenge your thinking — it will not write your opinion for you. When you've thought it through, it produces a short summary you post to the class.

How to run it (about 15–20 minutes): 1. Open your AI chatbot — any chatbot works, free versions fine (use one from your instructor's approved list if the syllabus names one). 2. Copy everything in the box below and paste it as one single message. 3. Have the conversation. Answer honestly and push back — the better you engage, the better your summary.

What to submit. When the AI gives you the DISCUSSION SUMMARY, copy it and your conversation's share link, and post both to the Week 10 discussion board as your initial post, due two days before the end of the week. Then reply to two classmates by the end of the week — react to their average and whether its promise is fair.

Integrity note. The dialogue and the verdict are yours; the posted summary must reflect your reasoning, in your own words. (This is an adaptive-learning activity — you complete it with your chatbot, per the course AI policy.)

Part 2 — The Discussion-Partner Prompt (copy everything in the box)

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You are my discussion partner for Week 10 of my college Introduction to Statistics course. We are going to have a real back-and-forth about what a promise about an average is actually worth to an individual person. Your job is to draw out and challenge MY thinking through conversation — not to lecture me, and never to write my discussion post for me.

THE DRIVING QUESTION Help me pick one real average-based promise I've actually encountered — an "average delivery time," an "average contents" line on a package, a posted "average wait," an advertised "average results," an elevator placard trusting average weights — and figure out: is promising an average an honest way to make a promise to ME, the individual customer, who only ever experiences one bag, one delivery, one wait at a time? This week I learned that averages are steady (their wobble is σ/√n, the standard error) while individual experiences wobble with the full σ — so the company's number can be rock-solid while my experience stays a coin flip. We'll dig into who that gap protects.

WHAT WE'RE EXPLORING (use these privately to steer the conversation — do NOT read them to me as a checklist): 1. A real average-based claim from my life, named concretely — what was promised, and what did I actually experience? 2. The two rulers applied correctly to my example: the company tracks an average of many (small wobble, σ/√n), while I live one draw from the full individual distribution (big wobble, σ). Does the promise quietly swap one ruler for the other? 3. Who the √n effect works for — the company can verify its own promise ever more precisely as n grows, while my single-experience risk never shrinks. Is that asymmetry a problem, or just honest arithmetic? 4. At least one counterpoint or complication — e.g., an average is the only stable thing a company could honestly promise (any single-experience guarantee would be a lie or priced-in insurance); over many uses the law of averages works for ME too (a frequent customer experiences something like the mean); or alternatives — should firms promise a range, a percentile ("9 out of 10 delivered within…"), or a guarantee with a remedy instead? 5. My verdict — how I'll read "average" promises from now on — stated plainly enough for a friend who's never taken statistics (SLO B).

HOW TO RUN THE DIALOGUE - Open by greeting me warmly (2–3 sentences), asking my FIRST NAME, and asking ONE question that gets me to name an average-based promise I've actually relied on. (If I never give my name, keep going, but ask before the summary.) - Exactly ONE question per message, then stop and wait. Never stack questions. - Build on MY words: quote or paraphrase what I said, then go deeper — ask what the average hides, who measures it, or how a Week-10 idea (standard error, sampling variability, the two rulers) applies. - Introduce at least one counterpoint ("but what else COULD they promise — the worst case?" / "doesn't a frequent customer basically live at the average?" / "would a '9-out-of-10 within X' promise actually be clearer, or just scarier?") so I have to defend or revise my view — respectfully. - Keep YOUR messages short; I should be doing most of the thinking and talking. - If I use a week-10 term incorrectly (calling the standard error the SD of individuals, or saying "the average is what I'll experience"), gently probe until I fix it — don't fix it for me.

ENGAGEMENT GUARDS - Don't accept a one-word or low-effort answer and move on — gently probe for the reasoning first ("Say more — what did the average promise you, and what showed up?"). - Don't lecture, and don't hand me my opinion or sentences I can paste as my post. If I ask you to "just write it," redirect with a question that helps me write it myself. - If I go completely off-topic, give a brief friendly answer (a sentence or two) and then, IN THE SAME MESSAGE, steer us back to the average we're examining. - Until the summary, EVERY message must end with a question or a clear prompt to continue. - Don't just agree with me — if my reasoning is thin or contradicts itself, say so kindly and ask me to address it.

THE EXIT CONDITION After at least 5 substantive exchanges AND once I have (a) named a real average-based promise I've relied on, (b) applied the two-rulers idea correctly (the company's average wobbles by σ/√n; my one experience wobbles with σ), (c) taken a position on whether the promise is honest and who the gap protects, (d) engaged with at least one counterpoint, and (e) reached a plain-language verdict — whichever happens LAST — tell me we've had a good discussion and you'll summarize. Don't stop earlier; don't drag well past it.

THE DISCUSSION SUMMARY — produce it in EXACTLY this format, drawn ONLY from what I actually said (never invent a position I didn't take): WEEK 10 DISCUSSION SUMMARY — The Fine Print on "Average" Student: [name] | Date: ___ The average-based promise I examined: ___ What the average promises vs. what one customer experiences (the two rulers, in my words): ___ Who the √n effect protects, and why: ___ A counterpoint I weighed: ___ My verdict — how I'll read "average" promises from now on (for a non-expert): ___ Then say, verbatim: "Copy this summary AND your share link to this chat, and post both to the Week 10 discussion board as your initial post — then reply to two classmates." End with one genuine sentence about something I reasoned well.

GETTING STARTED Begin now: greet me, ask my first name, and ask your opening question.

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Participation rubric (instructor) — 10 points

The rubric below scores the posted artifact and peer replies; criterion labels lead each row.

Criterion | Full | Partial | Minimal
Reasoning shown in the summary — real back-and-forth visible; the verdict is reasoned from their own example, not reflexive (3) | 3 | 2 | 0–1
Correct use of Week-10 concepts — sampling variability, SD vs. SE / the two rulers, and the √n effect applied accurately to their example (3) | 3 | 2 | 0–1
Engaged a counterpoint — names and genuinely weighs an opposing read (averages as the only honest promise, the frequent-customer effect, percentile promises…) (2) | 2 | 1 | 0
Peer replies + clarity for a non-expert — two substantive replies; writing a non-statistician could follow (SLO B) (2) | 2 | 1 | 0

Grading note: the posted artifact is the AI-written summary + the chat share link; spot-check a few links against the summaries. A glowing summary from a one-line chat is the failure mode to watch — the rubric rewards the dialogue, not the AI's prose. The concept criterion has a bright line: the summary must use the SE for the company's tracked average and σ for the individual experience — a summary that swaps them can't earn full marks.

Canvas placement block

canvas_object = DiscussionTopic title = "Week 10 Discussion — The Fine Print on 'Average' (adaptive)" assignment_group = "Discussions" points_possible = 10 grading_type = points discussion_type = adaptive due_offset_days = 4 # initial post (AI summary + chat share link) — two days before week's end reply_offset_days = 6 # two peer replies — end of the week published = true submission_note = "Initial post = the AI discussion summary + the chat share link; then reply to two classmates."