Introduction to Statistics — Syllabus
Introduction to Statistics is a 3-unit, lower-division undergraduate course offered at [Institution] in [Term]. The class meets in person twice a week for about 75 minutes each time: [Meeting days/times].
This syllabus is the governing document for the course. It tells you what the course is, how your grade is built, what happens each week, and the policies you can count on. Weeks are numbered from Week 1 to Week 16; your instructor's course site gives the due date for each item.
Instructor and Contact
- Instructor: [Instructor name]
- Contact: [Contact info]
- Office hours: [Office hours]
Bring a specific question or a problem you are stuck on to office hours, and reach out early: a question asked in the week it comes up is far easier to answer than one saved for exam week. Check the course announcements at the start of each week.
Course Description
This is a first course in reasoning with data, built for students in every major. You will learn where data come from, how to describe them with tables, graphs, and numbers, how to put a number on chance, and how to draw an honest conclusion from a sample with confidence intervals, hypothesis tests, and simple linear regression. The course teaches statistical thinking, not mathematical proof: there is no calculus, and every formula appears only after the idea behind it is clear in plain words. You will work with real data all term, including a field log of 344 penguins that a research team recorded on three Antarctic islands, and you will do the hands-on analysis in a spreadsheet.
Required Materials
You buy no textbook and pay nothing for course materials. Every reading and video is a link to an external resource, opened from the week's page so you always reach the version the course teaches from. Beyond a web browser, you need:
- A spreadsheet: Google Sheets (free) or Excel, for the weekly Data Lab.
- A free account with one approved AI chatbot, from the list in the AI-Use Policy below. A free tier is enough for everything in this course, and every AI-based activity also has a route that needs no AI at all.
Learning Outcomes
By the end of this course you will be able to:
- Outcome A — Reason with evidence. Reason quantitatively to draw evidence-based conclusions from data, stating what the evidence does and does not support.
- Outcome B — Communicate honestly. Communicate statistical results clearly and honestly to a non-technical audience, in plain language and with the appropriate caveats.
The eight course objectives below build toward those two outcomes. Each week's Start Here page names the objective that week serves.
- Distinguish populations from samples and parameters from statistics, classify variables by type and level of measurement, and identify appropriate sampling methods and study designs, including what each can and cannot conclude.
- Summarize and display a single quantitative or categorical variable with appropriate tables and graphs, and describe its distribution's shape, center, and spread with the right numerical summaries.
- Describe the relationship between two variables using scatterplots, the correlation coefficient, and two-way tables, and explain why association alone does not establish causation.
- Apply the basic rules of probability, including conditional probability and independence, and compute and interpret the expected value and standard deviation of a discrete random variable, including binomial settings.
- Use the normal model and sampling distributions, including the Central Limit Theorem, to reason about variability in individual values and in sample statistics.
- Construct and correctly interpret confidence intervals for a population mean and a population proportion, including the meaning of the confidence level and the margin of error.
- Carry out and correctly interpret hypothesis tests for means and proportions, including one- and two-sample settings, p-values, significance levels, and Type I and Type II errors.
- Fit and interpret a simple linear regression model, assess its fit with r-squared and residuals, and carry out inference for the slope.
How this course works
Every module presents the same content in three ways — a chapter to read, a slide deck with speaker notes, and captioned videos — so you can choose how to take it in. A lecture page carries the week's explanation as one spoken-style walk-through as well.
A typical week runs in this order:
- Start Here. Open the week's module overview. It lists the week's items in order with time estimates and names the week's checkpoint, the one thing you should be able to do by the end of the week.
- Chapter (about 30 to 60 minutes). The week's primary reading. Read it first.
- Lecture page and slide deck (about 15 to 20 minutes for the lecture, plus time with the deck). The same ideas, explained aloud and in slides.
- Readings and videos. An optional go-deeper menu; every video has captions.
- Lecture tutorial (60 to 90 minutes). A one-on-one session with your chatbot that teaches, gives practice, and ends with a summary you submit.
- Practice set (about 20 minutes). Ungraded practice with a coach, at your own pace.
- Data Lab (about 90 minutes). A real analysis in a spreadsheet, with a chart, a written interpretation, and a check of your chatbot's work.
- Discussion (about 45 minutes) and assignment (60 to 90 minutes). Where you put the week's ideas to work on a real question.
- Quiz. A short auto-graded check of what you now know, taken on your own.
Week 8 is lighter: the chapter, lecture, slides, practice set, and discussion, then the study guide, practice exam, and midterm. Week 16 has no new module; it holds the final exam and its study guide, practice exam, and exam-prep tutorial.
Grading
Your course grade is the weighted average of the six groups below. The weights sum to 100%.
| Component | Weight |
|---|---|
| Tutorials & Practice | 10% |
| Quizzes | 10% |
| Workshops | 20% |
| Assignments | 25% |
| Discussions | 15% |
| Exams | 20% |
| Total | 100% |
What each group holds
- Tutorials & Practice: the 14 weekly lecture tutorials, 5 points each. The practice sets sit beside them and are ungraded.
- Quizzes: 14 weekly quizzes, 10 points each, in every module except Week 8.
- Workshops: the 14 weekly Data Labs, 50 points each, in every module except Week 8.
- Assignments: 14 weekly assignments, 100 points each, in every module except Week 8.
- Discussions: 15 weekly discussions, 10 points each, one in every module from Week 1 to Week 15.
- Exams: the midterm in Week 8 (50 points) and the cumulative final in Week 16 (100 points).
Each week's discussion and assignment ship in two versions. The adaptive version is published and graded by default. A traditional version that uses no AI is also in the module, unpublished; your instructor may publish it instead. An unpublished version does not appear to you and does not count. A published traditional version takes the adaptive one's place in the same group, for the same points.
Letter grades follow the standard scheme:
| Letter | Percentage range |
|---|---|
| A | 90–100 |
| B | 80–89 |
| C | 70–79 |
| D | 60–69 |
| F | below 60 |
Weekly Schedule
The course runs for sixteen weeks: fifteen weeks of modules and a finals week. Every module from Week 1 to Week 15 holds the same materials: the Start Here page, the chapter, the lecture page, the slide deck, the readings and videos page, and the practice set. Every module also ships both an adaptive discussion and a traditional no-AI alternative, and every module except Week 8 ships both an adaptive assignment and a traditional no-AI alternative. The adaptive versions are graded and published; the traditional alternatives are unpublished by default and count only if your instructor publishes them instead.
| Week | Focus | Graded and published | Traditional alternative (unpublished) |
|---|---|---|---|
| 1 | Foundations & Types of Data | Tutorial, Data Lab, discussion, assignment, quiz | Discussion, assignment |
| 2 | Summarizing Data with Tables & Graphs | Tutorial, Data Lab, discussion, assignment, quiz | Discussion, assignment |
| 3 | Numerical Summaries: Center & Spread | Tutorial, Data Lab, discussion, assignment, quiz | Discussion, assignment |
| 4 | Exploring Relationships Between Two Variables | Tutorial, Data Lab, discussion, assignment, quiz | Discussion, assignment |
| 5 | Probability Foundations | Tutorial, Data Lab, discussion, assignment, quiz | Discussion, assignment |
| 6 | Random Variables | Tutorial, Data Lab, discussion, assignment, quiz | Discussion, assignment |
| 7 | Binomial & Normal Models | Tutorial, Data Lab, discussion, assignment, quiz | Discussion, assignment |
| 8 | Midterm Review & Exam | Discussion, midterm exam | Discussion |
| 9 | The Normal Distribution | Tutorial, Data Lab, discussion, assignment, quiz | Discussion, assignment |
| 10 | Sampling Distributions & the Central Limit Theorem | Tutorial, Data Lab, discussion, assignment, quiz | Discussion, assignment |
| 11 | Confidence Intervals for a Mean | Tutorial, Data Lab, discussion, assignment, quiz | Discussion, assignment |
| 12 | Confidence Intervals for a Proportion | Tutorial, Data Lab, discussion, assignment, quiz | Discussion, assignment |
| 13 | Hypothesis Testing: Foundations | Tutorial, Data Lab, discussion, assignment, quiz | Discussion, assignment |
| 14 | Tests for Means & Proportions | Tutorial, Data Lab, discussion, assignment, quiz | Discussion, assignment |
| 15 | Linear Regression & Inference for the Slope | Tutorial, Data Lab, discussion, assignment, quiz | Discussion, assignment |
| 16 | Finals week (no module) | Final exam | None |
The midterm in Week 8 covers Weeks 1 to 7. The final in Week 16 is cumulative and draws on every course objective. Both exams come with a study guide, a practice exam, and an exam-prep tutorial you work with your chatbot.
Attendance and Participation
This course meets in person twice a week. Come to class having opened the week's Start Here page and read the chapter, because class time builds on that reading with examples and questions. Your participation also shows in your weekly work: the tutorial, the Data Lab, the discussion, the assignment, and the quiz. Falling a week behind is the most common reason a grade slips, so keep to the weekly rhythm and talk to your instructor the moment you feel lost. If you must miss class, follow the week's module from its Start Here page so you stay on pace.
Late Work
Late work loses 10% of the item's points for each day it is late. If something serious comes up, such as an illness or a family emergency, contact your instructor before the due date whenever you can so the two of you can agree on a fair plan. The penalty exists to keep the course moving, not to punish genuine hardship.
Academic Integrity
The work you submit must be your own, and you are bound by your institution's academic integrity standard. Do not copy another student's work, do not submit work you did not do, and do not misrepresent how a piece of work was produced. Two violations matter most in this course: fabricating or doctoring a chatbot conversation or its share link, and using AI on the quizzes, the midterm, or the final. Violations are handled under your institution's academic integrity process and may result in no credit for the item.
Accessibility and Disability
This course is designed to reflect universal-design principles and offers multiple means of representation, engagement, and expression. If you have a disability and need an accommodation, contact your institution's disability services office to arrange it, and let your instructor know so it can be put in place promptly. If any material is hard to use with your assistive technology, tell your instructor and an equivalent version will be provided. This course is not yet accessibility-certified, and your feedback helps improve it. The course page Accessibility & Inclusion in Module 0 explains how the course offers multiple modes, how to turn on captions, and how to request an accommodation.
AI-Use Policy
This course uses an AI chatbot as a study partner, and then asks you to check its work. Use any one of the approved chatbots: Claude, Gemini, ChatGPT, Grok, or Microsoft Copilot. Your instructor may edit this list. Free tiers are fine, and Copilot is often included with a school Microsoft 365 account.
AI may be used on these activities:
- Lecture tutorials (completed with a chatbot). Each week you work the tutorial in a conversation with an approved chatbot, then submit the conversation share link and your completion summary.
- Practice sets (ungraded). You work the practice problems with a chatbot coach.
- Adaptive discussions (completed with a chatbot). You hold a real-time dialogue with an approved chatbot, then post the AI's summary with the conversation share link. This version does not require replies to classmates.
- Adaptive assignments (AI-coached). You work the problems with a chatbot coach that scores your work against the assignment's rubric, then submit the self-scored report and the conversation share link.
- Data Labs (a required AI-critique step). After your own analysis, you ask your chatbot to do the same analysis, then catch, explain, and correct a mistake in its work.
- Exam-prep tutorials and studying. Use AI freely to review, to check your understanding, and to prepare for the exams.
AI may not be used on the quizzes, the midterm, and the final. These are closed to AI and must be entirely your own work, because they confirm that you personally understand the concepts, notation, procedures, and interpretations.
If your instructor publishes a traditional alternative. A traditional discussion is your own post and your own replies to classmates; a traditional assignment is your own submitted work, graded by your instructor. AI may help you think, but you write and submit the work yourself.
Disclosure. The share link documents your AI use on the AI-based activities, so they need no separate note. For any other graded work where AI help is allowed, add one line naming the tool and how you used it.
Bring your own AI: access, cost, and privacy
- Equity and a non-AI route. You never have to create an account with a chatbot company to complete this course or to reach any grade in it. Every AI-based activity in this course carries an Equivalent route (no AI required) on its own page. It assesses the same objectives, for the same points, against the same rubric. Take it whenever you like, for any reason or none; you do not have to ask permission, explain, or arrange it in advance.
- Cost. A free tier of any approved chatbot covers everything in this course. No graded work depends on a paid subscription.
- Privacy. Before you start, read what your chosen chatbot keeps of your conversations and how it uses them. Never paste personal information you would not want stored, and remember that a share link lets anyone who has it read that conversation.
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