Stats 110
This syllabus and everything else you need will be posted on the course website: stats110.stanford.edu.
Learning Objectives
The theme of this class is the ubiquity of uncertainty in statistics and in everyday life.
By the end of this class, you should be able to:
- Carry out and interpret a hypothesis test to determine when a signal is real, not just noise.
- Construct and interpret a confidence interval to quantify uncertainty in a statistic.
- Design, implement, and analyze a survey.
- Design, implement, and analyze a randomized experiment.
- Write code in R to perform basic statistical analyses.
Course Staff
Instructor
|
Instructor | Prof. Dennis Sun |
| Lectures | Mon, Wed, Fri 1:30 - 2:50 PM in McMurtry Art Building Oshman | |
| Office Hours | Mon, Wed 11 AM - 12 PM in CoDa E230 |
Teaching Assistants
| Teaching Assistant | Section | Office Hours | |
|---|---|---|---|
|
Leqi Zhou leqizhou@stanford.edu |
Thu 9:30 - 10:20 AM in Littlefield 104 | Tue 9 - 10 AM in Sequoia 105 |
|
|
Suehyun Kim suehyun@stanford.edu |
Thu 12:30 - 1:20 PM in 160-319 | Tue 12 - 1 PM in Sequoia 105 |
|
|
Danielle Paulson paulsond@stanford.edu |
Thu 3:30 - 4:20 PM in 160-331 | Wed 3 - 4 PM in CoDa B06 |
|
|
Leon Lufkin lufkin@stanford.edu |
Thu 4:30 - 5:20 PM in Lathrop 015 | Tue 4:30 - 5:30 PM (room TBA) |
|
|
Ilia Popov ip2004@stanford.edu |
Fri 9:30 - 10:20 AM in 160-123 | Wed 4 - 5 PM in CoDa B06 |
|
|
Leo Vanciu vanciu@stanford.edu |
Fri 10:30 - 11:20 AM in 160-315 | Mon 3 - 4 PM in Sequoia 105 |
|
|
Ashwin Ram ashram03@stanford.edu |
Fri 11:30 AM - 12:30 PM in 160-317 | Tue 4 - 5 PM in CoDa B05 |
|
|
Saraswata Sensarma sensarma@stanford.edu |
— | Tue 3:30 - 4:30 PM (room TBA) |
Contact Outside Class and Office Hours
We prefer to talk to you in person, in section, or in office hours! But if you need to reach us outside of these times, please post on the Ed Discussion forum.
Grading
Your final grade in the course will be computed as follows: $$\text{Final Grade} = \text{Attendance\%} \times \text{Assignments\%}$$
Attendance
In order for you to get the most out of this course and contribute to a dynamic learning community, attendance and participation are essential. To earn full credit for attendance, you are expected to come on time, participate in all class activities, adhere to the device-free policy, and stay until the end of class. Students with exceptional participation can earn higher than 100%.
We realize that other commitments and unexpected events may occasionally conflict with class. However, you remain responsible for mastering all material that you missed, which we will measure by the exams. For this reason, there are no excused absences, but we will automatically replace any missing absences by your midterm or final grade.
To monitor attendance, we will be recording video (no audio) of the classroom. This recording will only be shared with the instructor and TAs (and possibly Stanford administrators, if there are specific concerns). In addition, we will use polls with geolocation, as well as TA observations, to monitor attendance. By taking this course, you consent to this use of videorecording and geolocation. Forging attendance, and assisting others in forging attendance, are serious violations of the honor code and will result in an automatic failing grade for the course.
Assignments
| Component | Weight |
|---|---|
|
There will be two short interviews, which will replace the sections and Friday lectures of October 15-16 and November 12-13, where you will demonstrate your understanding of statistics concepts to an instructor. We will provide sample questions. Although interviews might sound intimidating, we have found that interviews give students more chances to succeed, and they are more useful preparation for your future careers! Sections are intended to prepare you for these assessments. |
30% |
|
There will be a midterm exam in class on Monday, October 26. |
20% |
|
There will be a final exam during the registrar-scheduled exam time on Wednesday, December 9, 3:30 - 4:50 PM. |
20% |
|
To help you achieve the learning objectives, you will collect your own data and analyze it in two projects. For each project, you will submit a report. Then, you will present one of the projects in a poster session in class on Friday, December 4. |
25% |
|
Section This grade will be based on completion of the assignments before section, as well as attendance and participation in section. |
5% |
| Total | 100% |
Policies
Regrade Policy
If you believe that we have made a mistake in grading, please fill out this form within 1 week of getting the assignment back. Note that Professor Sun will regrade your entire assignment, so your grade could go up or down.
Device-Free Policy
To enhance your learning and the focus of those around you, laptops, tablets, smartphones, and wearable devices like smart glasses or smart watches are not permitted in class, except when an activity requires them. One exception: if you use a tablet to take notes, you may use the tablet only for taking notes.
Several activities require a laptop (e.g., applets), while others require a tablet or smartphone (e.g., polls). You should bring these devices to class, fully charged. These devices should only be taken out when necessary and otherwise should be silenced and stowed in your bag.
The TAs will be walking around and will deduct attendance points from any student who is using a device in violation of this policy.
Collaboration and AI Policy
The goal of this class is for you to develop fluency in statistical argumentation and reasoning. Too much collaboration with either AI or another person (e.g., a classmate, a tutor, your mom who is a statistician) will rob you of your intellectual development.
In this course, you may use AI or another person to do the following:
- check your statistical analysis
- provide feedback on ideas, problem-solving processes, code, or writing
- edit your writing for spelling and grammar
- debug code
- prettify presentations or graphs
- brainstorm ideas for projects
- locate sources for projects
- draft timelines and division of labor for projects
- tutor you on concepts you're having trouble with
- quiz you on course material
In this course, you may not use AI or another person to do the following:
- do statistical analysis for you (you should do the first pass, but if you make mistakes, AI will hopefully catch them)
- write text or code for you (writing is thinking, so do the thinking yourself, then ask AI for feedback)
- make presentations or graphs (the process of figuring out how to communicate to humans the most important skill in a post-AI world)
When using AI for this course, you should only use Stanford-approved tools, such as Claude for Education Standard or ChatGPT Edu Standard, both of which are free for all Stanford students. Do not upload any materials into third-party tools or personal accounts because they have not been vetted for data security.
Accommodations
Students who may need an academic accommodation based on the impact of a disability must initiate the request with the Office of Accessible Education (OAE). Professional staff will evaluate the request with required documentation, recommend reasonable accommodations, and prepare an Accommodation Letter for faculty dated in the current quarter in which the request is being made.
Once you have your letter, please upload it to this form. (Please don't email it to the professor or your TA; they will just tell you to e-mail it to this list.)
To help us prepare for your accommodations, please submit your letter by Wednesday, September 30. In order for us to make accommodations for an exam, we must receive your letter at least 10 days before the exam. According to the OAE, accommodations cannot be given retroactively.