Course goals & objectives:

Statistics, at its core, is a set of mathematical tools for reasoning about uncertainty. These tools are invaluable for making sense of data and making predictions about the future. The language of statistics allows us to connect our scientific theories to the phenomena we observe in the world.

This course is designed to introduce you to some of the key ideas in the discipline of statistics, and to make you comfortable with tools for applying them to data that you will encounter in your future courses and outside of class in the future.

By the end of the semester, you should be able to:

  1. Understand how the way that data is collected affects what you can learn from it.
  2. Use statistical software to summarize this data numerically and visually.
  3. Build statistical models of the data and understand which models are better and why.
  4. Make predictions about what kind of data you would expect to see in the future.
  5. Ask questions about the data, and make statistical inferences to answer them.
  6. Present these results in a transparent way to others.
  7. Understand the claims that others make from data and be able to critique them.

Course structure:

The course is divided into six learning units. For each unit a set of learning objectives and required and suggested readings, videos, etc. which will be posted on the course website. You are expected to complete the readings and/or watch the videos familiarize yourselves with the learning objectives.

Lectures and the textbook will cover the bulk of the theoretical ideas, and lab sessions will give you practice applying these ideas to real data. These are deeply intertwined, and it’s important to make sure you understand both the theoretical and practical parts of doing statistics.

Your understanding of the theoretical ideas will be assessed with short weekly quizzes, as well as problem sets that will be due at the end of each unit. Your understanding of the practice of statistics will be assessed by your work on the lab assignments.

The primary goal of this class is to teach you how to apply the ideas and tools of statistics to real-world data. For that that reason, the capstone assessment of the class is a Project in which you will apply these skills to an interesting real-world dataset. There will be No Graded Exams.

You will however be asked to complete the Comprehensive Assessment of Outcomes in a first Statistics class (CAOS) twice: At the start of the course and again at the end. Your responses at the beginning of the class will help us to know what to focus on, and your answers at the end of the quarter will help us to revise the class for the next cohort of students. These assessments will be graded for completion and not for correctness; you will receive full credit independent of your score.

Attendance and participation

Lecture and lab attendance is mandatory. Lecture slides will be posted after class each day, but reading these slides is only a partial substitute for attendance – they will often be terse and difficult to interpret without having heard the actual lecture. Similarly, lab assignments will be posted at the start of lab, but your TAs will present additional useful information for successfully completing the assignments.

All of the lectures are designed to elicit active learning through participation, and in general the best way to learn is to ask questions! In addition, to helping yourself and your classmates learn, you will be making it a lot easier for your instructors to view grades on the edge of two categories more favorably.

Problem sets:

These will be assigned at the start of each unit, and will be comprised of problems from the textbook. Each assignment will list a set of problems from the book to be turned in for grading, and roughly 6 practice problems. You do not need to turn in the practice problems, and the solutions can be found in the back of the book.

You are welcomed, and encouraged, to work with each other on the problems. But, you must turn in your own work.

Submission instructions: You will turn in your problem sets by submitted them through Canvas. You are welcome to write up problem sets in a word processor of your choice (Word, Google Docs, etc.), but we strongly suggest saving the file as a PDF and submitting the PDF. This will ensure that what we read is exactly what you intended to submit. You are also welcome to do the work in a notebook and upload a picture of each notebook page.

All assignments will be time stamped and late work will be penalized based on this time stamp (see late work policy below).


The objective of the labs is to give you hands on experience with data analysis using modern statistical software. The labs will also provide you with tools that you will need to complete the project successfully. We will use a statistical analysis program called RStudio, which is a front end for the R statistical language.

In class, your TA and I will give a brief overview of the lab and learning goals, and guide you through some of the exercises. You will start working on the lab during the class session, but note that the labs are designed to take more than just the class time, so you will probably need to continue working on them in order to submit labs before the due date (which will generally be the following week).

Submission instructions: Always submit the .Rmd and .HTML files via Canvas.


Each week on Wednesday we will have a short (~15 min) quiz at the beginning of lecture. You will do these in-class on paper and hand them in. Quizzes are designed to give both you and your instructors rapid feedback about your understanding of the theoretical ideas covered the previous week. We understand that sometimes things come up and you will be late or absent. For this reason, your lowest score will be dropped.


The objective of the project is to give you independent applied research experience using real data and statistical methods. You will be allowed to work on the final project either on your own, or with a partner. The goal will be to synthesize what you have learned in the labs over the course of the semester, and to show that you understand which analyses are appropriate and interesting for which kinds of data.


CAOS Pre and Post tests 5%
Quizzes 10%
Problem sets 20%
Labs 40%
Project 25%

Final grades will be determined as: [90,100] = A, [80,89] = B, [70,79] = C, [60,69] = D, [< 60] = R. Curving may occur at the instructor’s discretion.

Late Work Policies:

  • Late work policy for the problem sets and lab reports:
    • next day: lose 30% of total possible points
    • later than next day: lose all points
  • Late work policy for the project: 10% off for each day late.

  • Regrade requests must be made within one week of when the assignment is returned, and must be submitted in writing. These will be honored if points were tallied incorrectly, or if you feel your answer is correct but it was marked wrong. No regrade will be made to alter the number of points deducted for a mistake.

Announcements & Forum (Canvas and Piazza):

I will regularly send announcements through Canvas. You should get an email for these by default, so please make sure to check your email daily to make sure that you don not miss them.

Any non-personal questions related to the material covered in class, problem sets, labs, project, etc. should be posted to our Piazza. Before posting a new question please make sure to check if your question has already been answered. Your TA and I will be answering questions in Piazza daily and all students are expected to answer questions as well. Please use informative titles for your posts.

Note that it is more efficient to answer most statistical questions “in person” so make use of Office Hours.

Accommodations for Students with Disabilities:

If you have a disability and are registered with the Office of Disability Resources, I encourage you to use their online system to notify me of your accommodations and discuss your needs with me as early in the semester as possible. I will work with you to ensure that accommodations are provided as appropriate. If you suspect that you may have a disability and would benefit from accommodations but are not yet registered with the Office of Disability Resources, I encourage you to contact them at

Respect in the Classroom:

It is my intent to present materials and activities that are respectful to the diverse backgrounds and perspectives of students in the classroom. You may feel free to let me know ways to improve the effectiveness of the course for you personally or for other students or student groups. If you feel uncomfortable discussing this with me or your TA, you may voice your concerns to the Chair of the Department of Psychology Diversity and Inclusion Committee, Timothy Verstynen ( Dr. Verstynen is available to hear your concerns related to respect for diversity for any psychology class you are taking.

Take care of yourself:

Do your best to maintain a healthy lifestyle this semester by eating well, exercising, avoiding drugs and alcohol, getting enough sleep and taking some time to relax. This will help you achieve your goals and cope with stress. All of us benefit from support during times of struggle. You are not alone. There are many helpful resources available on campus and an important part of the college experience is learning how to ask for help. Asking for support sooner rather than later is often helpful.

If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, we strongly encourage you to seek support. Counseling and Psychological Services (CaPS) is here to help: call 412-268-2922 and visit their website at Consider reaching out to a friend, faculty or family member you trust for help getting connected to the support that can help.

If you or someone you know is feeling suicidal or in danger of self-harm, call someone at CaPS immediately, day or night: You can also call the Re:solve Crisis Network (888-796-8226). If the situation is life threatening, call the CMU police (412-268-2323), or call 911.

Cheating and Plagiarism:

Cheating and plagiarism are defined in the CMU Student Handbook, and include (1) submitting work that is not your own for assignments or exams; (2) copying ideas, words, or graphics from a published or unpublished source without appropriate citation; (3) submitting or using falsified data; and (4) submitting the same work for credit in two courses without prior consent of both instructors. Any student who is found cheating or plagiarizing on any work for this course will receive a failing grade for that work. Further action may be taken if necessary, including a report to the dean.