Surveys, Sampling, and Observational Data

STA304 is an upper-level undergraduate course at the University of Toronto’s Department of Statistical Sciences.



The best thing about being a statistician, is that you get to play in everyone’s backyard.

John Tukey

The work of applied statisticians, regardless of their specific job title and area of application, is the most important and exciting work in the world right now. The ability to gather data, analyse it, and communicate your understanding of the underlying process is incredibly valuable. In this course you will learn and apply the essentials of this.

We focus on surveys, sampling and observational data. The very stuff of statistical science! We will approach these topics from a practical perspective. You will actually run surveys and learn how messy it is to put one together. You will learn how to think about sampling, how to implement it, and why the details matter. You will forecast an election. And you will conduct original research. More generally, you will learn how to obtain and analyse data and use it to make sensible claims about the world.

To work as an applied statistician requires you to be able to, as part of a small team:

You likely have some of these skills already. This course will further develop them. At the end of the course you will have a portfolio of work focused on surveying, sampling, and observational data, that you could show off to a potential employer.

Each week you will read relevant papers and books, engage with them through discussion with each other, myself, and the TA. You will bring this all together and show off how much you have learnt through practical, on-going, assessment.

It is important to recognise that putting together everything that you have learnt to this point in this way will be difficult. It is not possible to cover everything that you will need to know. You should proactively identify and address aspects where you are weak through seeking additional information and resources. This course acts as a guide as to what is important, it does not contain everything that is important.

This course is different to many other courses at the University of Toronto. At the end of this course, you will have a portfolio of work that you could show off to a potential employer. You will have developed the skills to work successfully as an applied statistician or data scientist. And you will know how to fill gaps in your knowledge yourself. A lot of scholarships and jobs these days ask for GitHub and blog links etc to show off a portfolio of your work. This is the class that gives you a chance to develop these. It’s very important to having something to show that needs to go beyond what is done in a normal class.

How to succeed

In this course you will work in a self-directed, open-ended manner. Identify relevant areas of interest and then learn the skills that you need to explore those areas.

To successfully complete this course, you should expect to spend a large portion of your time reading and writing (both code and text). Deeply engage with the materials. Find a small study group and keep each other motivated and focused. At the start of the week, read the course notes, all compulsory materials and some recommended materials based on your interest. After doing that, but before the ‘lecture’ time you should complete the weekly quiz. During ‘lectures’ I’ll live-code, discuss materials in the course notes, talk about an experiment, and you’ll have a chance to discuss the materials with me.

You need to be more active in your learning in this course than others - read the notes and related materials - and then go out there and teach yourself more and apply it. You will not be spoon-fed in this course. Each week try to write reproducible, understandable, R code surrounded by beautifully crafted text that motivates, backgrounds, explains, discusses, and criticizes. Make steady progress toward the assessment.

This is not a ‘bird course’. Typically, after the term is finished, students say that the course is difficult but rewarding. The TAs and I are always available to answer any questions. Please come to office hours!

How we’ll work

This webpage will provide almost all the guiding materials that you need and links to the relevant parts of the notes. The course notes are available here. Those contain notes and other material that you could go over. We’ll use Quercus really only for assessment submission and grading.

A rough weekly flow for the course would be something like:

  1. Read the week’s course notes.
  2. Read/watch/listen to the required materials.
  3. Attend the lecture.
  4. Attend the lab.
  5. Complete the weekly quiz.
  6. Make progress on a paper.

Advice from past students

Successful past students have the following advice (completely unedited by me):


Thank you to the following people for generously providing comments, references, suggestions, and thoughts that directly contributed to this outline: Bethany White, Dan Simpson, Jesse Gronsbell, Kelly Lyons, Lauren Kennedy, Monica Alexander and Uzair Mirza. Thank you especially to Samantha-Jo Caetano who influenced all aspects of this and co-taught the first version in Fall 2020.



(Exact coverage will change based on how the class progresses.)

Week 1

Week 2

Week 3

Week 4

Week 5

Week 6

Week 7

Week 8

Week 9

Week 10

Week 11

Week 12



Item Weight (%) Due date
Quiz 20 Weekly before the lecture
Tutorial 20 Weekly the day before the tutorial
Paper 1 25 End of Week 3
Paper 2 25 End of Week 6
Paper 3 25 End of Week 8
Paper 4 25 End of Week 10
Final Paper (initial submission) 1 Middle of Week 12
Final Paper (peer review) 4 End of Week 12
Final Paper 25 Two weeks after that

You must submit Paper 1. And you must submit the Final Paper.

Beyond that, you have scope to pick an assessment schedule that works for you. We will take your best 3 of the 11 tutorials, or your best 8 of 11 quizzes for that 20 per cent—whichever results in a better grade for you (i.e. you can choose to do either quizzes or tutorials). And we will take your two best papers from Papers 1-4 for that 50 per cent (25 per cent for each). The remainder is made up of 1 per cent for submitting a draft of the Final Paper, 4 per cent for peer reviewing other people’s drafts of the Final Paper, and 25 per cent for the Final Paper.

Additional details:



Paper #1

Paper #2

Paper #3

Paper #4

Final Paper


Learning objectives


If you have a question, there is a decent chance that others have the same question or, at least, will benefit from the answer. Please post all questions to Piazza so that everyone in the course can benefit from your questions and our answers. You are encouraged to post answers to the questions of other students, where appropriate. Of course, if you have a concern of a personal nature then please email the TAs or me and you should begin your subject line with the course code ‘STA304’, and then an appropriate subject.

Emails and the message board are not checked or responded to by either the TA or me after hours or on the weekend.

Please be polite. We continue to be in a pandemic.

Accommodations with regard to assessment

You do not need to reveal your personal or medical information to me. I understand that illness or personal emergencies can happen from time to time. The following accommodations to assessment requirements exist to provide for those situations.

Straight-forward (will automatically apply to all students - there’s no need to ask for these):

So for those (with the exception of Paper #1), if you have a situation, then just don’t submit.

Slightly more involved:

Minimum submission requirement

If you are going to not be able to submit at least two problem sets, and/or be unable to submit the final paper then it would be unfair on the other students to allow you to pass the course. Please ensure you and your registrar get in touch with me as early as possible if this may be the case for you.


Requests to have your work re-graded will not be accepted within 24 hours of the release of grades. This is to give you a chance to reflect. Similarly, requests to have your work re-graded more than seven days after the release of the grades will not be accepted. This is to ensure the course runs smoothly.

Inside that 1-7 day period if you would like to request a re-grade, please email with a subject line that starts with ‘STA304’. You must specify where the marking error was made in relation to the marking guide. Your entire assessment will be re-marked and it is possible that your grade could reduce.

Plagiarism and integrity

Please do not plagiarize. In particular, be careful to acknowledge the source of code - if it’s extensive then through proper citation and if it’s just a couple of lines from Stack Overflow then in a comment immediately next to the code.

You are responsible for knowing the content of the University of Toronto’s Code of Behaviour on Academic Matters.

Academic offenses include (but are not limited to) plagiarism, cheating, copying R code, communication/extra resources during closed book assessments, purchasing labour for assessments (of any kind). Academic offenses will be taken seriously and dealt with accordingly. If you have any questions about what is or is not permitted in this course, please contact me.

Please consult the University’s site on Academic Integrity Please also see the definition of plagiarism in section B.I.1.(d) of the University’s Code of Behaviour on Academic Matters Please read the Code. Please review Cite it Right and if you require further clarification, consult the site How Not to Plagiarize

Late policy

You are expected to manage your time effectively. If no extension has been granted and no accommodation applies, then the late submission of an assessment item carries a penalty of 10 percentage points per day to a maximum of one week after which it will no longer be accepted, e.g. a problem set submitted a day late that would have otherwise received 8/10 will receive 7/10, if that same problem set was submitted two days late then it would receive 6/10.


Papers and reports should be well-written, well-organized, and easy to follow. They should flow easily from one point to the next. They should have proper sentence structure, spelling, vocabulary, and grammar. Each point should be articulated clearly and completely without being overly verbose. Papers should demonstrate your understanding of the topics you are studying in the course and your confidence in using the terms, techniques, and issues you have learned. As always, references must be properly included and cited. If you have concerns about your ability to do any of this then please make use of the writing support provided to the faculty, colleges and the SGS Graduate Centre for Academic Communication.

Accessibility needs

Students with diverse learning styles and needs are welcome in this course. In particular, if you have a disability/health consideration that may require accommodations, please feel free to approach me and/or Accessibility Services at 416 978 8060 or visit

Intellectual Property Statement

Course material that has been created by your instructor is the intellectual property of your instructor and is made available to you for your personal use in this course. Sharing, posting, selling, or using this material outside of your personal use in this course is not permitted under any circumstances and is considered an infringement of intellectual property rights.