Prof. Judith Fan

Fall 2026

Syllabus

Why take this course?

It is impossible to understand the modern world without an understanding of statistics. From public opinion polls to clinical trials in medicine to online systems that recommend purchases to us, statistics play a role in nearly every aspect of our lives. The goal of this course is to provide an understanding of essential concepts in statistics — how to construct models to explain variation in data — as well as the skills to apply these concepts to real data.

At the end of the course, students will possess:

  • Statistical literacy: The ability to dissect and understand statistical claims in scientific research and popular media

  • Statistical ability: The skills necessary to apply statistical analysis methods to real data

  • Statistical curiosity: The interest in further developing their statistical skills and knowledge, and the confidence in your ability to do so

What we offer

My priorities as an instructor are to:

  • Emphasize conceptual understanding over rote memorization. It is not important to memorize formulae. Instead, we will focus on helping you get in the habit of reflecting on what you have learned, how it connects to other concepts you’ve learned, and how to apply it in new contexts.

  • Reward deep thinking over simply getting the right answer. I believe everyone enrolled in our course can learn to think critically about numbers and statistics, and to enlist computers to help them do so. The grading of your CourseKata work and project milestones will be generous in this course. The lion’s share of your grade will reward good-faith engagement with the material and participation in class.

  • Prioritize hands-on activities over listening to me talk. As long as there are lectures built into this course, I will do my best to prepare material that presents abstract concepts via concrete examples, and include interactive elements as much as I can.

  • Give you authentic experience with modern statistical tools. Statistics is a broad and evolving field, not a fixed set of tricks. We will engage with real data in all its messiness using real statistical tools that practicing scientists use. We won’t be able to cover every interesting topic in statistics, and we won’t settle for superficial familiarity with terms. Instead, we will work towards a deep understanding and ability to apply a set of core concepts to a broad range of scenarios.

  • Continually improve this course over time. I will do my best to handle unexpected issues fairly, and incorporate your feedback to make this course better. Please expect that this syllabus is subject to change, but we will only aim to make changes that we think will improve your learning experience.

AI tools in this course

I want you to learn the foundational concepts that will help you navigate a sea of information, and to feel empowered to work with data. There are parts of this course where we will actively encourage you to use AI tools to expand the horizon of what you might have thought you could do in a project, in ways that are similar to how professional researchers themselves are experimenting with these tools today. (Stanford now provides several of these tools to students free of charge.) This is what discussion section and the final project are for. There are other parts of this course where we will deliberately remove digital technology in order to invite you to interact with each other and deepen your understanding of the material. This is what lectures and the Friday tutorials are for: the worksheets are on paper, and you will work through them with the people sitting next to you.

Whenever you use AI tools on your project, you are responsible for understanding and being able to explain every part of your work. Please keep a log of what you asked, what you checked, and what you changed. “I don’t know because AI did this” is NOT an acceptable answer at the final project showcase.

What we expect from you

The teaching team is looking forward to making this an awesome, positive, and supportive learning experience for everyone. These are the expectations we have of all students enrolled in this course, and your core responsibilities as a student:

  • Show up. This means attending lectures, discussion sections, Friday tutorials, and the final project showcase.

  • Try. This means engaging sincerely with the material, even when — especially when — it’s hard. This means doing your best to figure things out on your own (e.g., Googling it, or checking the syllabus) before going to someone else for help. This course WILL require a lot of hard work and persistence, so please budget your study time accordingly.

  • Ask for help when you need it. This means letting us know when you are stuck, even after trying to figure things out on your own and consulting with your peers on Slack. This means asking your TA in section or posting on Slack, and being prepared to describe your question, what you have already done to answer it, and what you are looking for from us.

  • Be professional. This means being actively respectful, courteous, and thoughtful when communicating with one another in class, over Slack, and with members of the teaching team. This means proofreading your messages to all members of the teaching team, and ensuring that you have provided enough context for us to provide an informative response.

Attending lecture: Attendance is expected at all lectures. Each lecture alternates between short stretches of explanation and a paper worksheet that you work on with the people next to you. What happens in lecture is on the comprehensive exam, alongside CourseKata, so missing class means missing material you will be tested on. Lectures and sections will not be recorded.

Attending discussion section: Weekly attendance of your assigned discussion section is expected. Section is where your project group meets and works, so section attendance counts toward your final project grade. Please consult with your TA about the best way to get caught up if you have to miss section.

How we are supporting you

Textbook: We will be using an interactive textbook entitled “CourseKata Statistics and Data Science.” It is already embedded in Canvas and accessible as “Modules” to work through at your own pace. This textbook is provided to you completely free of charge.

Website: We will be updating the course website throughout the term: http://psych10.github.io.

Canvas: We will use Canvas to post announcements, to submit assignments, and to post grades.

Slack: The only digital communication channel that is officially supported in this course is Slack. Here are some instructions on how to join the Slack workspace for this course. You can access Slack using a browser, but downloading the Slack app to your desktop or mobile device gives you the option to receive notifications on those devices.

  • It is a good idea to review these tips on Slack etiquette, particularly the parts about replying in a thread rather than creating a new post, and asking questions in a channel rather than by direct message to me. These tips will help keep notifications to a manageable level.
  • If you reduce your notification level, it is still your responsibility to review these channels periodically and respond appropriately.
  • If you have a question that is either personal or specific to you (others in the class would not need the answer), please send a Direct Message to your TA and/or Dr. Fan via Slack.
  • Please note that email and Canvas messaging are not supported in this class.

Getting help: There are no office hours this quarter. Your TA is your first stop for help: in section, at the Friday tutorials, and on Slack. The members of your teaching team are interested in getting to know you! The better they know you and your unique interests, the better they will be able to provide mentorship and support to you in this class and beyond.

Digital access: You need a laptop for this course. You will use it every week in section and for your final project. If you do not have one, Stanford can help: financial aid covers a one-time computer purchase of up to $3,000 (the Computer Expense Request form), and the Lathrop Learning Hub and the Diversity & First-Gen Office lend laptops. If you have other concerns related to digital access, please contact your TA via Slack.

Software: We will use R and RStudio, both free. Your first discussion section, in Week 1, is an install clinic: bring your laptop, and your TA will help you set everything up. To save time, download the R and RStudio installers before section (links on the Resources page).

Accommodations: Stanford is committed to providing equal educational opportunities for students with a disability. Students who require accommodations are a valued and essential part of the Stanford community and our class. If you require an accommodation, please register with the Office of Accessible Education (OAE). Professional staff will evaluate your needs, support appropriate and reasonable accommodations, and prepare an Academic Accommodation Letter for faculty. To get started, or to re-initiate services, please visit: https://oae.stanford.edu/. If you already have an Academic Accommodation Letter, we invite you to share your letter with us. Academic Accommodation Letters should be shared at the earliest possible opportunity so we can partner with you and OAE to identify any barriers to access and inclusion that might be encountered in your experience of this course.

What you will be doing

CourseKata Modules (20% of your grade)

Chapter sections will be assigned from “CourseKata Statistics and Data Science,” a FREE online and interactive textbook. We will work through all 15 chapters this quarter; the schedule says which chapters to finish each week.

  • Unlike a traditional textbook, you will be asked questions throughout each chapter. To receive full credit for the CourseKata portion of your grade, you are responsible for making good-faith attempts to answer ALL of the questions embedded in the assigned chapter by the end of the week that they are assigned.
  • We advise that you budget approximately 6-8 hours per week working through these CourseKata chapters. This is a lot of time! Please plan your study schedule accordingly.
  • Your responses do not need to be correct to receive full credit – the purpose of working on the embedded questions/problems is to help you keep track of how well you understand the material.
  • Late Policy: If you are not quite done with your assigned CourseKata modules by the time they are due, do not panic! Please take as much time as you need to complete these modules and engage with the material at your own pace. It is way more important to us that you learn by working through these modules than that you finish by a particular time. So long as you complete any CourseKata module by Wednesday during Final Exam Week at 11:59PM PT, you will still be able to receive FULL credit for it. However, we strongly recommend that you keep up with the recommended schedule for working through the CourseKata modules so you come to lecture and the Friday tutorials prepared.

Lecture Worksheets (15% of your grade, together with the Friday tutorials)

  • Every Monday and Wednesday lecture has a paper worksheet. You fill it in during two or three pauses in the lecture, on your own or with the people next to you, while the teaching team walks the room.
  • Hand your worksheet in on your way out. Each one you turn in counts toward the worksheet portion of your grade (see below).
  • Worksheets are not graded for correctness — write what you actually think. They show you (and us) how well you understand the material while there is still time to do something about it.
  • Worksheets are on paper, so bring a pen.

Friday Tutorials (15% of your grade, together with the lecture worksheets)

  • There is no lecture on Fridays. Instead, you attend one 20-minute tutorial in a group of 3-5 students with a discussion leader from the teaching team.
  • Each tutorial is one problem that your group solves together against a 12-minute timer, then a short debrief. Tutorials only use ideas from CourseKata chapters you have already been assigned.
  • Your session time is assigned each week and posted by Thursday. If you have a fixed conflict on Fridays between 1:30pm and 2:50pm, let your TA know in Week 1 so we can give you a standing time.
  • Write your name on your case file and hand it in at the end of the session. That is how we record tutorial attendance, and it counts toward the worksheet portion of your grade; nothing on it is graded for correctness.
  • Tutorials are on paper; bring a pen, not a laptop.

Discussion Section (part of your project grade)

  • Your discussion section meets once a week for 50 minutes. Section is project work time: your group works on the current milestone, laptops open, with your TA on hand.
  • Section attendance counts toward your final project grade (see below).
  • In Week 1, section is introductions and the install clinic: bring your laptop, charged, with the R and RStudio installers already downloaded.

Comprehensive Exam (35% of your grade)

  • There is one exam in this course, on Wednesday, November 18 in the lecture slot. It is closed-book and on paper.
  • Scope: CourseKata Chapters 1-12, and the material and activities from lecture that go with them. Much of what is on the exam gets worked through in class, on the worksheets, so the surest way to keep up with it is to come to lecture and keep up with the chapters.
  • Practice exam: On Monday, November 9, in the lecture slot, you will take a practice exam in the same format. It is not graded. We will release the answer key, and your discussion leader will return your practice exam at Tutorial 8 (Nov 13) and go over it with your group. Taking the practice exam earns 1% of extra credit.

Final Project (30% of your grade)

  • There is no final examination for this class: the scheduled final exam time will be used for our Final Project Showcase on Wednesday, December 9, 12:15-3:15pm. Attendance at the final project showcase is mandatory and counts toward the final project grade.
  • You will work in a group of 3, formed in your discussion section at the start of the quarter.
  • Your group will pick a quantitative claim about people that you doubt, or are curious to check — something you saw in the news, in your feed, or on a flyer. You will then find or collect data that bear on the claim, analyze the data in R, and report what you found. Details are on the Project page.
  • The weekly project milestones, one per discussion section, make up close to half of the project grade. Their purpose is to give your TA an opportunity to see where you are and provide you with personalized feedback to help you and your group stay on track. The final report, the showcase talk and poster, your group’s AI log, and attendance at section and the showcase make up the rest. The exact split will be posted here before the first milestone is due.
  • Each group is responsible for submitting a final report following a template that the teaching team will provide, and for presenting a 5-minute talk with a poster at the showcase.
  • AI tools: You may use AI tools throughout the project: to find data, write and debug code, make figures, and argue with you about what could have produced your data. Two rules. You are responsible for understanding and verifying every detail of your project; “I don’t know, the AI did it” is not an acceptable answer at the showcase, from anyone in your group. Two milestones ask you to audit an AI’s attempt at your own analysis and report what it got wrong. We grade the log and the audits on your judgment, not on how much you used the tools.
  • Late Policy: Weekly project milestones that are submitted late may still receive full credit, but it is not guaranteed and late submissions will not yield as useful and timely feedback from your TA. The final report and poster will NOT be accepted late. NO exceptions. The reason for the strict deadlines for these final milestones is that the schedule for grading them is very tight at the end of the quarter, and late submissions place an undue burden on the teaching team. Please plan your group submissions accordingly.
  • Honor Code: You can use any resource you wish for your project (textbook, internet, AI tools), and you should feel free to discuss your project with students in other groups. However, the work your group submits must be your group’s own, and every member of the group should be able to explain any part of it.

Worksheets, Attendance, and Extra Credit

  • The worksheets you hand in are 15% of your grade: 17 lecture worksheets and 10 Friday tutorial case files, 27 in all. The best 23 count, so you can miss four for any reason, no questions asked, with no effect on your grade. Nothing on them is graded for correctness; making a good-faith effort and handing one in is what counts.
  • Beyond four misses, an absence is excused only through an OAE accommodation or an athletics travel letter (send these to your TA as soon as you have them), or through the end-of-quarter narrative below. We do not review absences one at a time during the quarter. Keep track of your own.
  • End-of-quarter narrative: At the end of the quarter, you can optionally submit a short narrative that contextualizes any unexcused absences. The teaching team reads these to understand how you have engaged with the course overall, and decides which absences beyond the free four to excuse. The same narrative covers discussion section and the showcase.
  • Your record: Every worksheet and tutorial is a column in the Canvas gradebook, updated throughout the quarter. If you handed a sheet in and see a blank after 1-2 weeks, let your TA know.
  • Extra credit: Taking the practice exam on November 9 earns 1%, and a good-faith response to the student background survey earns 0.5%. There are no makeups for extra credit; it can only help you.

Grading

Grades will be determined as follows:

  • CourseKata modules (20%)
  • Comprehensive exam (35%)
  • Final project (30%)
  • Lecture and tutorial worksheets (15%; the best 23 of 27 count)
  • Extra credit: the practice exam (1%) and the student background survey (0.5%)

Grading scale. The grading scale will be as follows:

  • 97-100: A+
  • 93-96: A
  • 90-92: A-
  • 87-89: B+

and so on (rounding to the nearest whole number). We may curve up at the bottom of the scale depending on the distribution, but I will not curve down (i.e. 87 will never be worse than B+).

What We Expect From Everyone

Values we share: We are genuinely committed to equality, diversity, and inclusion in this course. We aim to provide an intellectual environment that is at once welcoming, nurturing and challenging, and that respects the full spectrum of human diversity in race, ethnicity, gender identity, age, socioeconomic status, national origin, sexual orientation, disability, and religion. We sincerely hope that you will share our commitment to actively creating and maintaining a safe environment founded on mutual respect and support. To be clear, this course affirms people of all gender expressions and gender identities. If you prefer to be called a different name than what is indicated on the class roster, please let us know. Feel free to correct us on your preferred gender pronoun. If you have any questions or concerns, please do not hesitate to contact any member of the teaching team.

Code of conduct: You are expected to treat the teaching team and your fellow students with courtesy and respect. This class should be a harassment-free learning experience for everyone regardless of gender, gender identity and expression, sexual orientation, disability, physical appearance, body size, race, age or religion. Harassment of any form will not be tolerated. For clear violations of course expectations for professional and respectful conduct in this course, whether in class or online, we may deduct points from a student’s grade, with the number of points proportional to the severity of the violation. If someone makes you or anyone else feel unsafe or unwelcome, please report it as soon as possible to a member of the teaching team. If you are not comfortable approaching the teaching team, you may also contact the Stanford Office of the Ombuds.

Course Privacy Statement: As noted in the university’s recording and broadcasting courses policy, students may not audio or video record class meetings without permission from the instructor (and guest speakers, when applicable). If the instructor grants permission or if the teaching team posts videos themselves, students may keep recordings only for personal use and may not post recordings on the Internet, or otherwise distribute them. These policies protect the privacy rights of instructors and students, and the intellectual property and other rights of the university. Students who need lectures recorded for the purposes of an academic accommodation should contact the Office of Accessible Education.

Affordability: Stanford University and its instructors are committed to ensuring that all courses are financially accessible to all students. If you are an undergraduate who needs assistance with the costs related to this class, you are welcome to approach me directly. If you would prefer not to approach me directly, please note that you can ask the Diversity & First-Gen Office for assistance by completing their questionnaire on course textbooks & supplies, or by contacting Joseph Brown, the Associate Director of the Diversity and First-Gen Office (jlbrown@stanford.edu; Old Union Room 207). Dr. Brown is available to connect you with resources and support while ensuring your privacy.

Student Background Survey

We will set aside 20 minutes at the end of the first lecture (Wed Sep 23) for you to complete the student background survey in one sitting. If you missed it, please complete it during Week 1. A good-faith response earns 0.5% of extra credit. The purpose of this survey is for the teaching team to get to know you, how you think about learning, and relevant aspects of your circumstances that may affect your learning experience in this course.

Acknowledgements

Many thanks to Prof. Ji Son, Prof. James Stigler, everyone in the UCLA Teaching and Learning Lab, Prof. Russ Poldrack and Prof. Tobias Gerstenberg for generously sharing their instructional materials.

In designing the latest version of the course, I consulted with many more of my colleagues, including Tselil Schramm, Dennis Sun, Jonathan Taylor, and Julia Palacios. I have used AI tools to explore many different possibilities for how to re-imagine the way an introductory statistics course could look and feel. One that could be both playful and rigorous, using cutting-edge tools but also a course committed to fundamental concepts.