Department of Psychology

Winter 2025

Psychology 2811B - 650

Statistics for Psychology I

1 CALENDAR DESCRIPTION

This course introduces students to the basics of data analysis for psychological research. Topics  include probability, sampling, estimation, data visualization, and the conduct and interpretation of basic statistical analyses. Throughout the term, students will gain experience in computer-based data analytic methods.

Antirequisite(s): Biology 2244A/B,Economics 2122A/B,Economics 2222A/B,Geography 2210A/B,Health Sciences 3801A/B,MOS 2242A/B,the former Psychology 2810, the former

Psychology 2820E,Psychology 2830A/B,Psychology 2850A/B,Psychology 2851A/B,Social Work 2207A/B,Sociology 2205A/B,Statistical Sciences 2035,Statistical Sciences 2141A/B,Statistical Sciences 2143A/B,Statistical Sciences 2244A/B,Statistical Sciences 2858A/B.

Antirequisites are courses that overlap sufficiently in content that only one can be taken for credit. If you take a course that is an antirequisite to a course previously taken, you will lose credit for the earlier course, regardless of the grade achieved in the most recent course.

Prerequisite(s): Prerequisite(s): At least 60% in 1.0 credits of Psychology at the 1000 level; a passing grade (i.e., at least 50%) inData Science 1000A/Band a passing grade (i.e., at least 50%) in 0.5 credit of Year 1 Math from among the following courses:Calculus 1000A/B,Calculus 1301A/B,Calculus 1500A/B,Calculus 1501A/B,Mathematics 1225A/B,Mathematics 1228A/B,Mathematics 1229A/B,Mathematics 1600A/B,orApplied Mathematics 1201A/B.

Students enrolled in Year 2 of an Honours Specialization in Neuroscience may enrol with 0.5 credit ofApplied Mathematics 1201A/Band 0.5 credit ofComputer Science 1026A/B. Students who have completedStatistical Sciences 1024A/B(or other introductory statistics course, in addition to 0.5 credit of Year 1 Math) may enrol after completing an introductory programming class from the following list:Computer Science 1025A/B,Computer Science 1026A/B,Computer Science 2120A/B,Data Science 1200A/B,Digital Humanities 2220A/B,orEngineering Science 1036A/B.Data Science 2000A/Bmay be substituted forData Science 1000A/Bfor students entering the program with 1.0 Year 1 Math courses.

Unless you have either the prerequisites for this course or written special permission from your Dean to enrol in it, you may be removed from this course and it will be deleted from your record. This decision may not be appealed. You will receive no adjustment to your fees in the event that you are dropped from a course for failing to have the necessary prerequisites.

2 lecture hours and 2 laboratory hours, 0.5 course

2 COURSE INFORMATION:

Lecture (Online/Asynchronous): New lectures will be posted at 9am each Monday

Lab (Online/Asynchronous):        New labs will be posted at 9am Monday every second week (see course schedule below)

Students must have a reliable internet connection and computer that are compatible with online learning system requirements.

3 COURSE MATERIALS

There is no specific textbook for this course. Instead, readings will be drawn from a number of sources – mainly online textbooks but sometimes blog posts and other resources. All of these  sources are freely available online. The links for each reading appear in the course reading list.

4 COURSE OBJECTIVES

The aim of this course is to develop students’ basic data literacy skills by learning to use a data- driven approach to think critically about data. Students will develop statistical knowledge via sampling data from real and simulated datasets, visualizing their results, testing for relationships in their data, and interpreting the patterns they see. The class will extend basic data science training by teaching students to code their own statistical tests and visualizations in Python.

STUDENT LEARNING OUTCOMES

5 EVALUATION

Lab/Homework Assignments                     15%

Statistics in the News Project                     15%

Midterm Exam                                               32%

Final Exam                                                       38%

The evaluation and testing formats for this course were created to assess the learning objectives as listed in section 4 and are necessary for meeting these learning objectives.

Bi-weekly Lab/Homework Assignments (15%):

*** This assessment has flexible deadlines. It is exempt from the academic considerations policy. ***

For each lesson, you will complete a set of lab and homework problems in a Jupyter Notebook. The lab elements will be guided by video tutorial. The homework problems you will do on your own. The homework problems will be based on the lecture material for the lesson and will also relate to the corresponding lab material. The Jupyter Notebook with the lab/homework assignment will be released on OWL on the same day as the video tutorial it corresponds with (Mondays of the release week at 9am). It will be due 12 days later, on Friday at 5:00pm. You must upload the Notebook (‘.ipynb’ extension) to the assignment portal on Gradescope. You are responsible for uploading the correct file, in the correct format to the correct portal on Gradescope. If you upload the file incorrectly, you will receive a mark of 0. There are a total of 6 assignments that you will complete over the course of the term. I will drop your lowest score, which means that you can skip one assignment without penalty.

Each of the remaining 5 assignments will count toward 3% of your grade. The solution to the assignment will be released the Monday after the assignment is due at noon. If your assignment has not been submitted before the solution is posted, you will receive a grade of 0. There will be absolutely no exceptions to this policy.

Statistics in the News Project (15%):

We frequently see statistics reported in the news. But are they noteworthy? Or not worthy of the space they take up? The goal of this assignment is to critically evaluate a statistical claim reported in a media outlet. You should select a statistic that is interesting to you but that sounds a bit too good/weird/unusual/outlandish be true. The statistic should also have a clear source citation (e.g., a research article published in a scientific journal, upon which the news story is based). You should then critically evaluate the claim, as well as the original source article, using evidence from both sources.

Write a 280-character “Tweet” style report that states your conclusions about the news article, relative to your evaluation of the source article. Additional details and rubric are available in the resources section on OWL.

Exams (70%): There will be two Proctor-track proctored exams in the course. These exams will be online, synchronous and scheduled by the registrar. The midterm will cover the course material from weeks 1-5. The final will be cumulative (weeks 1-12).