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

Adopting instructors: complete the bracketed fields below, attach your institution's required policy statements where flagged, and map the 18 relative weeks onto your term calendar. Nothing else in the course pack needs editing to go live.

Course | Introduction to Statistics
Instructor | [Instructor name]
Institution | [Institution]
Term | [Term]
Meeting days/times | [Meeting days/times]
Contact | [Contact info]
Office hours | [Office hours]
Units | 4
Length | 18 weeks — 16 instructional weeks, a midterm week (Week 9), and a final exam week (Week 18)
Designed for | In-person or hybrid delivery; planned around two ~75-minute sessions per week

Course Description

Introduction to Statistics is a one-term, college-level survey of statistical reasoning for students across every major. We move along the natural arc of the subject — describe → relate → quantify uncertainty → infer → model — and at each step we lead with the plain-language idea before the notation. You will learn to summarize and display data, reason about chance and variability, build and interpret confidence intervals and hypothesis tests, analyze categorical data with chi-square methods, and fit a simple regression model.

The emphasis throughout is on understanding and interpretation, not memorizing formulas. We use spreadsheets (Google Sheets or Excel) and a free graphing/statistics tool for computation and charts, do hand computation on small cases to build intuition, and adopt an "interpret-the-output" stance for the heavier machinery. Each week you'll read a short course chapter written for this class, practice with an AI tutor at your own pace, and get your hands on real public data in a weekly data lab. No prior statistics experience is assumed.

Learning Objectives

By the end of the course, you will be able to:

1. Distinguish populations from samples and identify appropriate sampling and study designs.
2. Summarize and display univariate data, describing shape, center, and spread.
3. Describe relationships between two variables using scatterplots, correlation, and two-way tables.
4. Apply basic probability rules, including conditional probability, and work with random variables.
5. Use normal and sampling distributions to reason about variability.
6. Construct and interpret confidence intervals for means and proportions.
7. Conduct and interpret hypothesis tests for means and proportions.
8. Fit and interpret a simple linear regression model, including inference for the slope.
9. Conduct and interpret chi-square tests for categorical data, including goodness-of-fit and tests of independence.

Student Learning Outcomes (SLOs)

- SLO A — Quantitative reasoning. Reason quantitatively to draw evidence-based conclusions from data.
- SLO B — Communication. Communicate statistical results clearly to a non-technical audience.

Required Materials

There is no required textbook, and you will pay nothing for course materials. Your primary reading is the weekly Chapter written for this course and posted in each module. Supplemental readings and videos are links to external resources — nothing to download or buy.

You will need:

- A device with a web browser and internet access.
- A spreadsheet tool — Google Sheets (free) or Microsoft Excel — for computation and charts.
- A free online graphing/statistics tool (your instructor will point to one; any Desmos-class tool works).
- Access to one AI chatbot for the weekly Lecture Tutorials, Discussions, Assignments, and Data Labs (see the AI-Use Policy below; free tiers are fine).

Grading

Your course grade is the weighted total of the groups below. Weights sum to 100%.

Assignment group | Weight | Notes
Assignments | 25% | 16 weekly AI-coached assignments (100 points each)
Lecture tutorials | 20% | 18 tutorials — one per instructional week, plus one exam-prep tutorial per exam; submit the conversation share link
Quizzes | 15% | 16 weekly quizzes (10 points each; every instructional week)
Data labs | 15% | 16 weekly data labs on real public datasets (50 points each)
Discussions | 15% | 17 discussions — every instructional week, plus the Week 9 midterm debrief (10 points each)
Midterm | 5% | Week 9 — 50 questions covering Weeks 1–8
Final | 5% | Week 18 — 60 questions, cumulative
Practice exercises | 0% | Ungraded; weekly, for mastery practice (plus an ungraded practice exam before each exam)
Total | 100% |

A note on this course's grading philosophy: the grade lives in the weekly work. The midterm and final are low-stakes checkpoints — they tell you (and your instructor) how the ideas are consolidating, but they cannot sink a term of steady work. The flip side: there is no end-of-term rescue, so the weekly rhythm is the whole game. Start each week's work early.

Letter-Grade Scale

Grade | Range
A | 90–100%
B | 80–89.9%
C | 70–79.9%
D | 60–69.9%
F | below 60%

Due Dates & Late Policy

All timing in this course is stated relative to the week: weekly graded work is due at the end of its week; discussion initial posts are due two days before the end of the week (so classmates have time to reply); peer replies are due by the end of the week. Your instructor's course calendar maps each week onto real dates.

- Late penalty: 10% per day. Submitted work loses 10 percentage points of its earned score for each day (or part of a day) it is late.
- Quizzes, the Midterm, and the Final are time-bound. Make-ups are arranged only for documented emergencies — contact your instructor as early as possible, ideally before the due date.
- Practice exercises are ungraded and exist for your benefit; the late penalty does not apply to them.
- If something serious is getting in the way of your work, reach out early. It is almost always easier to arrange support before a deadline than to repair a grade after it.

AI-Use Policy

This course builds AI into the coursework as a learning partner, and it draws a clear line for everything else. Read this section carefully.

Which chatbots

Any AI chatbot is allowed — free tiers are fine — unless your instructor narrows this to an approved list. Pick one you like and use it consistently; every AI activity below works the same way in any of them.

AI in this course (adaptive-learning activities)

Your Lecture Tutorials, Discussions, and Assignments are adaptive-learning activities you complete with your chatbot, and each week's Data Lab includes a required AI-critique step:

- Weekly Lecture Tutorials — paste the course-provided tutor prompt, work through the week's ideas in conversation, then submit the conversation share link and your Completion Summary.
- Discussions — think a question through in a real-time dialogue with your chatbot, then post the AI-generated summary plus your chat share link to the discussion board (and reply to classmates).
- Assignments — solve problems with a chatbot coach that grades against an embedded rubric and teaches you as you go, then submit the coach's self-scored report (the line beginning STUDENT'S SCORE:) plus your chat share link.
- Data Labs — do the hands-on work yourself, then hand your results to your chatbot and check its interpretation against your own — chatbots routinely mis-add columns and misread tables, and catching them is part of the lab.

For all of these, the share link is part of your submission — treat the conversation as your work, keep it on-topic, and do your own thinking.

Permitted vs. not permitted

- AI may be used on your coursework — the Lecture Tutorials, Discussions, Assignments, Data Labs, and the ungraded Practice Exercises. (For the adaptive activities, working with the chatbot is the activity.)
- AI may not be used on the Quizzes, the Midterm, or the Final — these are closed to AI and must be entirely your own work, unless an item explicitly says otherwise.

Disclosure

The adaptive activities need no separate disclosure — the share link already documents your AI use. If you use an AI tool to help you think about any other graded work, add a one-line note stating which tool you used and how.

Alignment with academic integrity

Using AI as described here is encouraged and fully consistent with the integrity standard below. The violations are fabricating or doctoring a chat you submit, and using AI on the closed assessments (Quizzes, Midterm, Final). When in doubt, ask before you submit.

Attendance & Participation

The in-class work — worked examples, think-pair-share, the technology-and-AI-critique moments — is where much of the learning happens. Attendance is not a weighted grade group in this course, but consistent absence will show in your weekly work. Arrive on time, engage professionally, and if you must miss a session, review the module materials and check the week's overview page to catch up; you remain responsible for content and due dates from a missed class. (Adopting instructors: adjust this section to your own attendance policy.)

Academic Integrity

You are expected to do your own work and to represent it honestly. Cheating, plagiarism, unauthorized collaboration, and submitting another's work — human or AI — as your own are violations of academic integrity and will be handled according to your institution's policy, which may include a failing grade on the work or in the course. Collaboration is welcome where an activity invites it; when in doubt about what is allowed, ask first. Holding to this standard is what makes your grade — and your degree — mean something.

Accessibility & institutional policies: Students who need accommodations should contact their campus disability-services office to arrange them, and are encouraged to notify the instructor early in the term so supports can be in place. (Placeholder — institutions should insert their official accessibility, integrity, and other required policy statements here.)

Course Schedule — 18 Weeks

All timing is relative; your instructor's calendar maps each week onto real dates. Every instructional week carries the same graded set — quiz, discussion, assignment, data lab, and lecture tutorial — plus ungraded practice; the table lists the week's focus and highlights.

Wk | Focus | Key assessments due
1 | Statistics, Data & Study Design | Quiz 1; Discussion 1; Assignment 1; Data Lab 1; Tutorial 1
2 | Summarizing Data with Tables & Graphs | Quiz 2; Discussion 2; Assignment 2; Data Lab 2; Tutorial 2
3 | Numerical Summaries: Center & Spread | Quiz 3; Discussion 3; Assignment 3; Data Lab 3; Tutorial 3
4 | Relationships Between Two Variables | Quiz 4; Discussion 4; Assignment 4; Data Lab 4; Tutorial 4
5 | Probability Foundations | Quiz 5; Discussion 5; Assignment 5; Data Lab 5; Tutorial 5
6 | Random Variables | Quiz 6; Discussion 6; Assignment 6; Data Lab 6; Tutorial 6
7 | The Binomial Distribution | Quiz 7; Discussion 7; Assignment 7; Data Lab 7; Tutorial 7
8 | The Normal Distribution + Midterm Review Kickoff | Quiz 8; Discussion 8; Assignment 8; Data Lab 8; Tutorial 8
9 | Midterm Week | Midterm (covers Weeks 1–8) + study guide, exam-prep tutorial, practice exam; Debrief Discussion 9
10 | Sampling Distributions & the Central Limit Theorem | Quiz 10; Discussion 10; Assignment 10; Data Lab 10; Tutorial 10
11 | Confidence Intervals for a Mean | Quiz 11; Discussion 11; Assignment 11; Data Lab 11; Tutorial 11
12 | Confidence Intervals for a Proportion | Quiz 12; Discussion 12; Assignment 12; Data Lab 12; Tutorial 12
13 | Hypothesis Testing: Foundations | Quiz 13; Discussion 13; Assignment 13; Data Lab 13; Tutorial 13
14 | Testing Claims About Means | Quiz 14; Discussion 14; Assignment 14; Data Lab 14; Tutorial 14
15 | Testing Proportions & Two-Sample Inference | Quiz 15; Discussion 15; Assignment 15; Data Lab 15; Tutorial 15
16 | Chi-Square Tests for Categorical Data | Quiz 16; Discussion 16; Assignment 16; Data Lab 16; Tutorial 16
17 | Linear Regression with Inference + Course Synthesis | Quiz 17; Discussion 17; Assignment 17; Data Lab 17; Tutorial 17
18 | Final Exam Week | Final (cumulative) + study guide, exam-prep tutorial, practice exam

The schedule may be adjusted with advance notice; changes will be announced in the course.