Last semester, when I was scrolling through the registrar’s Fall 2024 course offerings, I took note of the major requirements I still needed to fulfill for my degree. I needed to take a statistics course the following semester, but each course I reviewed looked extremely unexciting: according to course reviews from previous students, many of these classes struggled to make their lecture content understandable or meaningful.
Many of Princeton’s statistics courses have an alarmingly low rating, even though statistics is a vital skill for many Princeton students’ majors and careers. To address this, Princeton must be more responsive to student reviews of statistics courses and implement changes that can sufficiently address student concerns.
Huge swaths of students in every Princeton class are required to enroll in statistics courses. Students in the Class of 2024 enrolled in SPIA, the second most popular major, and Statistics and Machine Learning, which emerged as the most popular minor, are required to take these courses. This doesn’t even include the various natural sciences and engineering paths where statistics are essential. When so many students are required to take statistics prerequisites, Princeton must ensure these courses teach the material effectively. This could be done by evaluating the course feedback, in which many students have discussed what they felt helped or hindered their learning.
For example, consider the ratings — out of a maximum of 5 — of the following courses that satisfy statistics prerequisites: ECO 202: 3.35, ORF 245: 2.92, POL 345: 2.91, SML 201: 4.31, SPI 200: 2.23, EEB 330: 4.06 and SOC 301: 2.55. Reviewing the course reviews, housed on the registrar’s website, exposes systemic pedagogical problems with many of these courses that have persisted for two or more semesters. For this article, I focused on the reviews and feedback from POL 345 and SML 201 students. These two courses in particular are required for several majors and minors.
Strikingly, several of the reviews for POL 345: Introduction to Quantitative Social Science concentrate on the fact that students walk away with little understanding of R, the coding language on which the whole class is based.
“The worst part is that in class you learn statistics, and outside of class every assignment is in R (the coding language), from precepts and PSETs to the final. BUT, they [don’t] actually teach you R,” one anonymous review wrote.
Another wrote: “This year’s class was hilariously unorganized. I learned nothing but am still going to get an A.”
This critique — that students walk away without an understanding of the course but with top marks — is rare, however, in the more positively reviewed SML 201: Introduction to Data Sciences. The course also teaches R and basic statistical methods, but students report a balance well-struck, writing, “If you’re shopping for a stats class, this is not a bad bet. You’ll learn introductory R and basic knowledge of stats concepts.”
“Probably better than the other crappy stats classes at Princeton,” one student concluded.
But, why do we have some courses in statistics that leave students unfulfilled and without the skills they need, while also having other courses that hit those marks? That must change.
Statistics is important for students because it equips them with essential skills for analyzing data and making informed decisions in their fields of study. Understanding statistical concepts helps students critically evaluate research, interpret results, and draw conclusions based on numerical evidence. This knowledge is valuable not only for academic success but also for future careers, as many professions rely on data analysis to solve problems and drive innovation.
President Christopher Eisgruber ’83 recently published a piece surrounding The Committee of Three, one of the faculty governance committees that he describes as a University “quality-control check” on academic departments. However, this committee concentrates solely on faculty appointments, leaving little — if any at all — student-facing accountability that our evaluations are incorporated and applied to future course design.
When classes receive sub-three point scores for the past few iterations of its teaching, it should be a sign that the University should intervene. And when they do, they must let students know. It is not enough to simply take feedback from students and make small changes — professors must restructure the courses in a way that fits student feedback and is designed to teach students. For statistics, this means transforming the definition of success away from an A to an enriching, helpful learning experience.
It’s time for Princeton to restructure the way statistics — a pertinent subject for many — is taught. Being receptive to student concerns about course quality is a good place to start.
Liz Reyes is a second-year contributing Opinion writer planning to major in Neuroscience. She is from Cherry Hill, N.J., and can be reached at lizbeth.reyes[at]princeton.edu.
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