Instructor: Michelle R. Greene, Ph.D

Office hours: Option 1: Book me via calendar
Option 2: 1:00 - 2:00 Monday, Wednesday

TA: Sundari Ruth

Office hours: T/Th 10:00 - 11:00 on Google Meets

Logistics: M/W 2:40 - 3:55 LL107 R&D Science Center
Recitation: M or W 4:10 - 6:00 Milstein 516

Image by artist Laurie Frick who visualized statistics of her EEG.

Introduction

“In our private life as in our collective life there is no other truth than a statistical one.” ~ Simone de Beauvoir, The Ethics of Ambiguity (1947)

We live in an uncertain world. In every facet of our lives, we make judgments about the future based on incomplete evidence. Most of the time, our intuitions serve us remarkably well, but they are also prone to systematic biases. Dramatic examples can outweigh base rates, patterns in small samples can feel more meaningful than they are, and we are often more receptive to evidence that confirms what we already believe. For example, we fear plane crashes and terrorist attacks over car crashes and heart disease, despite the latter being far more common.

Statistics gives us a way to reason more carefully under that uncertainty. Despite its mixed reputation, statistics is the art of quantitatively learning from data.

Wait, art? Really??

Yes, really. Data do not interpret themselves. Doing statistics well requires judgment: deciding what to measure, choosing an appropriate model, distinguishing signal from noise, quantifying uncertainty, and deciding what conclusions the evidence actually warrants. At its best, statistics can test our intuitions, challenge our claims, and reveal structure that we could not otherwise see.

The goal of this course is therefore not simply to teach you how to perform statistical calculations. It is to teach you how to reason from imperfect evidence. These skills will be useful throughout your Barnard career as you read increasingly sophisticated research and conduct your own work, including your senior thesis. More broadly, statistical reasoning lies at the heart of fields ranging from psychology and medicine to public policy and beyond—and it gives you a powerful set of tools for evaluating the claims you encounter every day.

Learning Objectives

After this course, you will be able to:

Given a set of data, produce appropriate tabular and graphical summaries of the variables. and give an accurate verbal interpretation of the results.
Have a working knowledge of probability theory: compute probabilities for disjoint and independent events, compute and interpret conditional probabilities.
For a given research question, formulate null and alternative hypotheses. Describe the logic behind null-hypothesis significance testing, and be able to choose the appropriate statistical test for a given question.
Complete statistical analyses in R, correctly interpret the results, and form well-reasoned conclusions from them.

Classroom Expectations

Commitment to Inclusion

I am committed to inclusive and equitable pedagogical practices. I strive to create a learning environment that (1) recognizes, values, and supports individual differences and identities and (2) works against societal inequalities. I consistently reflect on these values and how I am implementing them. I will seek your feedback—if you are willing to provide it—regarding how well you think these values are practiced in our class and what could be improved. If, at any time, you feel that I am not living up to this commitment, I would appreciate speaking with you about your experiences, if you are willing. Please reach out to me, and we will set up a time to talk.

Generative AI policy

You are not permitted use generative AI tools such as ChatGPT, Claude, Gemini, or similar systems for work in this course.

This is because this course is designed to help you develop skills that only improve through practice: reasoning through uncertainty, translating ideas into quantitative form, interpreting evidence, writing clearly about results, and noticing when something does not make sense. If you outsource those difficult parts of the work, you may produce a cleaner answer in the short term, but you also lose the practice that the assignment was designed to give you. Struggling productively is part of learning.

More broadly, AI systems are not equally available to everyone: students who can afford paid access often have substantially more capable tools than students using free versions. I do not want success in this class to depend on who can purchase the best model. Further, some students may not wish to use generative AI because of a discomfort in how the systems have been built. For example, training has relied heavily on enormous collections of writing, art, code, and other intellectual work, often without meaningful permission or compensation to the people who created it. Their development and use also require substantial computational resources, with real environmental costs in energy and water. These are not abstract concerns, and I do not think we should treat the use of these systems as ethically neutral simply because they are convenient.

Most importantly, I want to evaluate your thinking. I am much more interested in an imperfect analysis that reflects your own developing understanding than in a polished answer generated by a system that already knows how a statistics assignment is supposed to sound.

If you are stuck, confused, or worried that your work is not good enough, please come talk to me. Asking for help is part of learning. Handing the thinking over to a machine is not.

Academic Integrity

Approved by the student body in 1912 and updated in 2016, the Code states:

We, the students of Barnard College, resolve to uphold the honor of the College by engaging with integrity in all of our academic pursuits. We affirm that academic integrity is the honorable creation and presentation of our own work. We acknowledge that it is our responsibility to seek clarification of proper forms of collaboration and use of academic resources in all assignments or exams. We consider academic integrity to include the proper use and care for all print, electronic, or other academic resources. We will respect the rights of others to engage in pursuit of learning in order to uphold our commitment to honor. We pledge to do all that is in our power to create a spirit of honesty and honor for its own sake.

Cheating is bad, I think we can all agree to that. The less-acknowledged truth is that it’s not even worth it. Cheating cheapens the value of your work, and everyone else’s, and a single violation can literally ruin your entire academic and professional career. Students’ work will be held to the standards of the Honor Code. If you are concerned that your collaboration might put you at risk of an academic integrity violation, please come see me during office hours as soon as possible. In my experience, violations of academic integrity are acts of desperation. If you are ever feeling desperate enough that a few extra points in this course seem to be worth risking so much, please consider talking to someone first — that could be me, a friend, or even someone at the Furman Counseling Center. I want you to succeed, and I am happy to talk to you if you feel undue pressure from this course or anything else.

This course assumes that work submitted for a grade by students – all process work, drafts, brainstorming artifacts, final works – will be generated by the students themselves, working individually or in groups as directed by class assignment instructions. This policy indicates the following constitute violations of academic honesty: a student has another person/entity do the work of any substantive portion of a graded assignment for them, which includes purchasing work from a company, hiring a person or company to complete an assignment or exam, and/or using generative AI tools (such as ChatGPT).

Students with Disabilities or Learning Differences

If you believe you may encounter barriers to the academic environment due to a documented disability or emerging health challenges, please feel free to contact me and/or the Center for Accessibility Resources & Disability Services (CARDS). Any student with approved academic accommodations is encouraged to contact me during office hours or via email. If you have questions regarding registering a disability or receiving accommodations for the semester, please contact CARDS at (212) 854-4634, cards@barnard.edu, or learn more at the CARDS website. CARDS is located in the Diana Center, room 307.

Collaboration:

Collaboration is the basis of all scientific discovery and is often instrumental in the learning process. However, you are individually responsible for learning the course content. I encourage students to form study groups. However, if you are working together on homework or the final project, then you must give written credit to the collaborators. The final written product must be your own. In other words, you may conceptually discuss the approach with your group, but you must write your code on your own. As there are many valid approaches to coding the same solution, acts of co-coding are easy to identify and will be treated as violations of academic integrity.

Late work:

For all of our deadlines, if you turn in a component late, you will lose 10% of the total score per day. For example, the maximum possible percentage for a product turned in one day late is 90. This policy does not apply to a documented personal or family emergency.

Emergencies:

If I must cancel class due to weather or an emergency, I will inform you via the class email list. Please consider your Barnard email to be the default place to look for class-related information and get into the habit of checking it daily.

Electronics:

I want electronics to enhance your learning rather than detract from it. Please silence your cell phone upon entering class. We will have interactive components where you will respond to questions via a device. Except for these times, please refrain from using your devices in class. If possible, consider leaving your laptop or tablet at home and take notes by hand. There are good reasons for this: laptop use is correlated with lower learning outcomes for you and those around you, and the act of taking notes on the laptop is less effective than hand-written notes.

A Note on Email:

My goal is to deliver you the best possible course experience. Part of how I do this is by spending less time on email. The multitasking associated with email is associated with less productivity and more stress. Unfortunately, multitasking is a myth: not only are we limited to one task at a time, but we underestimate how much task switching harms our performance.

Therefore, I process all of my emails in one batch once a day. While I will always respond to you on the same business day, this means that you may go 23 hours without a response. Need a faster response? Stop by my office — I’m there most of the time and am happy to chat if my door is open! Need to meet with me? Feel free to send me a calendar invite (see the top of this syllabus for instructions). I kindly ask you to search this syllabus before asking questions about class policies.

Affordable access to course materials

All students deserve to be able to study and make use of course texts and materials regardless of cost. Barnard librarians have partnered with students, faculty, and staff to find ways to increase student access to textbooks. By the first day of advance registration for each term, faculty will have provided information about required texts for each course on CourseWorks (including ISBN or author, title, publisher, copyright date, and price), which can be viewed by students. A number of cost-free or low-cost methods for accessing some types of courses texts are detailed on the Barnard Library Textbook Affordability guide. Undergraduate students who identify as first-generation and/or low-income students may check out items from the FLI lending libraries in the Barnard Library and in Butler Library for an entire semester. Students may also consult with their professors, the Dean of Studies, and the Financial Aid Office about additional affordable alternatives for having access to course texts. Visit the guide and talk to your professors and your librarian for more details.

Helpful Resources

You!

“Self-belief does not necessarily ensure success, but self-disbelief assuredly spawns failure.” ~ Albert Bandura.

Student Hours (a.k.a. Office Hours)

Office hours are scheduled time outside of class to meet with students. You can meet with me during office hours to discuss the class materials or other related interests. This could include asking for extra help, seeking clarification of something presented in class, or following up on an aspect of the class that you find compelling. You should also feel free to discuss majoring in psychology (if you are considering it), summer research opportunities, graduate school, campus events, and much more.

You are encouraged, but not required, to attend office hours. I find that it’s very helpful to get to know you individually during office hours in a relatively large class like this one. I might ask you to come to office hours if I’d like to offer some support. Please know that this is not a punishment like detention in high school. Office hours do not have a lesson plan. I assume you will “drive” these meetings with your own questions and thoughts. A good way to prepare for office hours is to review your notes from class and the readings and write down your questions beforehand.

Resources from the Center for Engaged Pedagogy

These resources, including tips for studying, reading, and attending office hours, have been collated by the Center for Engaged Pedagogy. They are excellent – check them out!

Loaner laptops

If you need access to a loaner laptop, please fill out the Supplemental Academic Support Application on myBarnard (instructions). If you are having trouble accessing the request form or knowing what to do, please the Access Barnard office at accessbarnard@barnard.edu or visit the office on the first floor of Milbank Hall.

Peer tutoring

You can receive two hours of tutoring per week for this course from a peer tutor for free via the Peer-to-Peer Tutoring Program. You can request a tutor after the second week of the semester. In order to provide the most effective peer tutoring experience, you should be able to clearly articulate why you need a peer tutor for this course.

Wellness

Although our thinking comes from our brains, we are more than brains on a stick. Caring for your whole self will enable you to flourish in your endeavors. We as a community urge you to make yourself–your own health, sanity, and wellness–your priority throughout this term and your career here. Sleep, exercise, and eating well can all be a part of a healthy regimen to cope with stress. Resources exist to support you in several sectors of your life, and we encourage you to make use of them. Should you have any questions about navigating these resources, please visit these sites:

Grading

Grading Overview

Product	Grade Percentage
Exit Tickets	20% (21 x 1.05% each)
Labs	40% (10 x 4% each)
Exams	30% (Exam 1: 5%, Exam 2: 10%, Exam 3: 15%)
Final Project	10%

Exit Tickets and Challenge Questions: 20% of total

I will ask for an exit ticket at the end of each class that you can submit on your phone or other electronic device. The ticket will ask for your name, a take-away from that day’s session, and your final answer to the day’s challenge question. There will be an optional field that will allow you to ask any lingering questions. This exercise serves three purposes: (1) it provides a small incentive for you to attend and participate in class; (2) it allows you to reflect on your learning, which has been associated with better learning; (3) it allows me to understand and address the questions that the class has about the content. You will receive full credit for submitting a ticket, not on the correctness of your answer to the challenge question. Students submitting at least 90% of their exit tickets will receive full-credit on this portion of the grade.

Labs: 40% of total

Most recitation sessions will consist of computer labs where you will practice concepts that you have learned in lecture and implement them with R. 10 recitations will have labs that you will write up for your final grade. Each lab report is worth 4% of your final grade, and is due 24 hours after the end of your recitation section (i.e., 6:00 pm on Tuesday or 6:00 pm on Thursday). A typical lab will have you implement a specific statistical technique and interpret the result. 60% of your grade for each lab will be for completion (i.e., did you complete all aspects of the assignment); 30% will be for the accuracy of your answers; 10% will be for the clarity of your presentation. Please submit each lab as a single PDF to CourseWorks.

Exams: 30% of total
There will be three non-cumulative exams on material covered in the course readings, lectures, and recitations. Please note that the exams increase in weight throughout the semester. The exams are closed-book and will be administered in class. If you are going to miss an exam for any reason, you must contact me PRIOR to the exam. If you are going to miss an exam because you are sick, you need to provide a doctor’s note that states you were unable to attend that particular exam. It is your responsibility to schedule a make up for the exam (assuming you have appropriate documentation). If you do not respond to any of my emails about rescheduling the exam within 72 hours (3 days), you will receive a grade of zero on the exam.

Final project: 10% of total

On November 20, the final project will be released. You will have until the end the semester (December 14) to turn it in on CourseWorks. The format of the final project is “choose your own adventure” — you will receive a package of datasets along with a research question for each. You can choose the datasets you want to use, analyze the data, and provide a written interpretation of the results. A grading rubric and additional information will be made available later in the semester.

Your final grade will be determined on the following scale:

Grade	Percentage	Grade	Percentage
A+	>95%	B-	77-79%
A	90-94%	C+	74-76%
A-	87-89%	C	70-73%
B+	84-86%	D	50-69%
B	80-83%	F	<50

Reading

We will be using the following (free) open textbook:

Danielle Navarro (2019) Learning Statistics with R

Other excellent supplementary resources (optional) are:

Russell A. Poldrack (2018) Statistical Thinking for the 21st Century

David M. Diez, Christopher D. Barr & Mine Cetinkaya-Rundel. (2014) Introductory Statistics with Randomization and Simulation
If you would like a copy of this source in print, there are very affordable ($8.49) copies on Amazon.

David Lane (n.d.) Online Statistics: An Interactive Multimedia Course of Study

The reading schedule is on the course schedule.

Course Calendar

Date	Topic	Reading	Lab
9/09/26	Introduction	Chapter 1	None
9/14/26	What are data?	Chapters 2-3	Introduction to R
9/16/26	Central tendency and variability	Chapter 5.1 to 5.2	Introduction to R
9/21/26	Normal distribution and z-scores (asynchronous option available for Yom Kippur)	Chapters 5.6 and 9.5	Descriptive Statistics
9/23/26	Probability	Chapter 9.1-9.4	Descriptive Statistics
9/28/26	Probability and sampling	Chapter 10.1-10.2	Sampling and standard error
9/30/26	Exam 1 review	None	Sampling and standard error
10/05/26	Exam 1	None	Unit 1 integration
10/07/26	Confidence intervals	Chapter 10.3-10.4	Unit 1 integration
10/12/26	Confidence intervals	Chapter 10.5	Confidence intervals
10/14/26	Logic of null-hypothesis significance testing	Chapter 11.1-11.4	Confidence intervals
10/19/26	Null-hypothesis significance testing	Chapters 11.5-11.9 and Chapter 13.1	Hypothesis testing and z-test
10/21/26	t-tests	Chapter 13.2-13.3	Hypothesis testing and z-test
10/26/26	t-tests	Chapter 13.4-13.10	T-tests
10/28/26	Exam 2 review	None	T-tests
11/02/26	NO CLASS: ELECTION	None	None
11/04/26	Exam 2	None	None
11/09/26	One-way ANOVA	Chapter 14.1-14.6	Unit 2 integration
11/11/26	Two-factor ANOVA	Chapter 16.1-16.3	Unit 2 integration
11/16/26	Two-factor ANOVA	Chapter 16.8-16.9	ANOVA
11/18/26	Correlation	Chapter 5.7	ANOVA
11/23/26	Work day for final project (asynchronous)	None	None
11/25/26	NO CLASS: THANKSGIVING	None	None
11/30/26	Univariate regression	Chapter 15.1-15.2	Correlation and Regression
12/03/26	Multivariate regression	Chapter 15.3-15.8	Correlation and Regression
12/07/26	Exam 3 review	None	Work on final projects
12/09/26	Exam 3	None	Work on final projects
12/14/26	Work day for final project (in-person)	None	None

Introduction to Statistics

Michelle R. Greene

Fall 2026