Psychology 2811B 200 FW25
Statistics for Psychology I
WESTERN UNIVERSITY
LONDON CANADA
Department of Psychology
Winter 2026
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%) in Data Science 1000A/B and 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, or Applied Mathematics 1201A/B. Students enrolled in Year 2 of an Honours Specialization in Neuroscience may enrol with 0.5 credit of Applied Mathematics 1201A/B and 0.5 credit of Computer Science 1026A/B. Students who have completed Statistical 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, or Engineering Science 1036A/B. Data Science 2000A/B may be substituted for Data Science 1000A/B for 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: * Both sessions are required *
In Person/Synchronous session: Wednesdays. See Student Centre Timetable for times and location.
Online/Asynchronous session: New material will be posted at 9am Mondays
(see course schedule below for details)
COURSE STAFF:
Instructor: Dr. Erin Heerey
Email: eheerey@uwo.ca
Office Hours: see OWL Brightspace
TAs: See information on OWL for names, email addresses and office hours.
Students must have a reliable internet connection and computer that are compatible with online/technical learning elements.
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
|
Learning Outcome |
Learning Activity |
Assessment |
|
Depth and Breadth of Knowledge. Demonstrate basic knowledge of probability as it applies to sampling.
Describe the logic and basic elements of null hypothesis significance testing. |
Lectures; readings; lab activities
Lectures; readings; lab activities |
Homework; Exams; Quizzes
Homework; Exams; Quizzes |
|
Application of Knowledge. Produce appropriate statistics to describe sample data.
Plot sampling distributions and graphs that show the relationships between different types of variables. |
Lab activities
Lab activities |
Homework; Exams; Quizzes
Homework; Exams; Quizzes
|
|
Interpret both graphical and statistical evidence to make conclusions about data.
Recognize from data and/or study design descriptions which statistical tests should be used. |
Lectures; readings; lab activities
Lectures; readings; lab activities |
Homework; Media v Science Project; Exams; Quizzes
Homework; Exams; Quizzes |
|
Application of Methodologies. Produce code in Jupyter Notebook to calculate statistical tests and data visualizations.
|
Lectures; readings; lab activities |
Homework; Exams; Quizzes
|
|
Demonstrate basic data wrangling skills including outlier exclusion, data cleaning and transformation. |
Lab activities
|
Homework; Exams; Quizzes |
|
Awareness of Limits of Knowledge. Explain the strengths and weaknesses of null hypothesis significance testing. |
Lectures; readings |
Homework; Media v Science Project; Exams |
5 EVALUATION
Lab/Homework Assignments 7%
Lab/Homework Quizzes (in class) 8%
Media v Science 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 (7%):
*** This assessment has built-in flexibility. It is exempt from the academic considerations policy. ***
For each assignment, you will complete a set of lab/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 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 9:00am). It will be due 8 days later, on Tuesday at 9:00am. 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 your grade. This assignment has a 24-hour grace period. The grace period extends from Tuesday at 9:00am to Wednesday at 9:00am. If your assignment has not been submitted by this time, you will receive a grade of 0. There will be absolutely no exceptions to this policy.
Bi-weekly Lab/Homework Quizzes (8%):
*** This assessment has built-in flexibility. It is exempt from the academic considerations policy. ***
Starting with the Lab/Homework 2, the synchronous class period that occurs the day the Lab/Homework grace period ends will include a quiz in the first 60 minutes of class (see schedule below for exact dates). To complete the quiz, you will need an iClicker account, your laptop, and Jupyter Notebook. You will also need to be present in the classroom. At the start of the class period, OWL will release a Jupyter notebook that you will download, open, and analyze in class. The quiz questions will be based on analysis steps within the Jupyter notebook. You will have a chance to ask questions and interact with the instructor before answering each quiz question. The quiz questions will only be presented in person. You must upload your completed notebook to the assignment portal to receive full credit for the quiz. There are a total of 5 quizzes that you will complete over the course of the term. I will drop your lowest score, which means that you can skip one quiz without penalty. Each of the remaining 4 quizzes will count toward your grade. If you do not submit a quiz question via iClicker, you will receive a grade of 0 on that item. There will be absolutely no exceptions to this policy.
Media v Science Project (15%):
*** You MAY use your self-attested absence on this course element. ***
We frequently see statistics reported in the news. But have you stopped to consider where those statistics come from what they really mean? The goal of this assignment is to critically evaluate how a research study, as reported in a media outlet, compares with its original source. You should select a recent news article (no more than 1 year old). The article should report on a peer-reviewed research article published in a scientific journal. You should then critically evaluate the news article, as well as the original source article, using evidence from both sources. You will write a 500-word critical analysis of the media and the scientific source article. You must document your work-flow on this assignment. To support this, there will be a series of interim due dates (see schedule below). Additional details and rubrics are available on OWL.
Exams (70%): There will be two proctored exams in the course. These exams will take place in person. The midterm will cover the course material from weeks 1-5. The final will be cumulative (weeks 1-12).
*** Final Exams are always exempt from the academic considerations policy. ***
*** The Midterm Exam is the designated course component that is exempt from the academic considerations policy. ***
Both exams will be closed book/closed note. No calculators or other devices will be allowed. You will be allowed one “cheat sheet” of notes for the midterm and two cheat sheets for the final exam. Your cheat-sheet(s) may include any course material that you think will help you on the relevant exam and you may use both the front and back sides of each paper. Each cheat sheet may not include more than a single piece of “letter” sized paper (no stapled, glued, or taped elements are allowed). Your cheat sheet(s) will be checked by the proctors. If they are found to be in violation of the requirements the proctors will confiscate them during the exam. The exams will include multiple-choice/select all that apply/matching/fill in the blank questions, along with several short answer, and graph/code interpretation questions.
The midterm will be completed during class time on Wednesday, 25 February. You will have 100 minutes to complete it. The final exam will be scheduled by the registrar during the April exam period. It will be 3 hours long. If you are an accommodated student, your time will be adjusted according to the time listed in your official accommodation if and only if you request an exam via the accessible education office. You MUST take the exams independently. The answers on the exam must be entirely your own work. If there is evidence that you worked with another student on the exam or that the work is not entirely your own, you will receive a score of 0.
Extra Credit (OPTIONAL; up to +3%): Statistics is a discipline that relies on the analysis of empirical data. You have the chance to participate in this process by helping to generate research data and to see the data analysis from the data creation side. To take part, you will be given access to the SONA system, and you may participate in any “for credit” studies that you wish. You will receive bonus credits added to your overall course grade for each SONA credit you earn, to a maximum of 3.0 SONA credits (50% of these credits must be earned in-person). Note that if you sign up for a study and then fail to attend, you will receive a penalty equal to the number of study-credits the original study was worth. This penalty will count against your earned credits until it is made up.
The SONA system will track the studies you complete, and the course instructor will be given this information at the end of the term. This is an opportunity to earn extra credits and is not required as part of your normal grade, you will not lose any marks if you do not participate in research studies. The maximum number of bonus credits you may earn is 3.0. For each credit you earn, you will receive an additional 1% in the gradebook. All extra credits must be completed by 11:55AM (just before noon) on the last day of term to count toward your grade. Because this item is extra credit and will never count against you, there will be absolutely no exceptions to this deadline.
*** Because this is not an official assessment, is not required, and is subject to the SONA credit deadline, it is exempt from the academic considerations policy. ***
POLICY ON MISSING COURSEWORK
Lab/Homework Assignments: Assignments are due at 9:00am on Tuesday of the week they are due (see schedule below). The 24-hour grace period for assignment submission lasts until Wednesday at 9:00am in the week they are due. Please ensure that you give yourself enough time to complete your submission by this time. There is no need to email the course staff about late homework, as the submission portal will remain available until the grace period closes (Wednesday of the relevant week at 9:00am). If your assignment has not been completed and correctly uploaded by the time the assignment portal closes, it will receive a score of 0. Because the assignments are worth < 2% each, there is a grace period between the due date and assignment submission portal closure, and the lowest score is dropped, absolutely no excuses will be accepted for missed assignments.
Lab/Homework Quizzes: Beginning with Homework 2, each homework will be associated with an in-class quiz. The quiz will involve a Jupyter notebook that you will complete in class (see schedule below). The Jupyter notebook will be based on the relevant lab/homework you completed. The quiz will be given via iClicker. You must be present in class to take the quiz. If your assignment has not been completed and correctly uploaded by the time the answer key is released, it will receive a score of 0. Because the assignments are worth 2% each and the lowest score is dropped, absolutely no excuses will be accepted for missed quizzes.
Media v Science Project: The final project will be due at 11:55pm on Wednesday 8 April. This assignment also has interim deadlines (e.g., annotated articles are the first submitted project element; see schedule below for more information). If you wish to attest to an illness or other extenuating circumstance, you may do so using the appropriate reporting portal. You may use your single self-attested absence for any of the assignment deadlines, as you wish. If you attest to an illness or other short-term extenuating circumstance, your assignment will be due 24 hours after the deadline to make an attestation. A chart with the relevant attested-absence due dates is posted on OWL in the Media v Science tab. You must upload the completed element to the correct assignment portal before the deadline, or you will receive a score of 0 on that element.
Exams: If you need to miss an exam due to illness or other issue, you MUST request relief from academic counselling. Without an approved consideration from academic counselling, you will receive a score of 0 on the exam. You will be given an opportunity to make up the final exam. The make-up final exam will be held in-person on a date and time that will be announced on OWL. Note that the make-up exam may include new test questions. The make-up exam may include an oral exam (conducted in-person) on the course material. If you do not take the make-up exam, you will receive a 0 for this course component.
You will NOT have an opportunity to make up the midterm exam. Instead, if you have an approved consideration for the midterm, you will receive a midterm score based on the items on the final exam that cover the same content as the midterm. Your proportion correct on these items will be used to calculate a midterm score for you. Your final exam score will then be calculated based on the proportion of items you get correct that cover content from the second part of the course.
The expectation for course grades within the Psychology Department is that they will be distributed around the following averages:
70% 1000-level to 2099-level courses
72% 2100-2999-level courses
75% 3000-level courses
80% 4000-level courses
The Psychology Department follows Western’s grading guidelines, which are as follows (see: https://www.uwo.ca/univsec/pdf/academic_policies/general/grades_undergrad.pdf
A+ 90-100 One could scarcely expect better from a student at this level
A 80-89 Superior work that is clearly above average
B 70-79 Good work, meeting all requirements, and eminently satisfactory
C 60-69 Competent work, meeting requirements
D 50-59 Fair work, minimally acceptable
F below 50 Fail
In the event that course grades are significantly higher or lower than these averages, instructors may be required to make adjustments to course grades. Such adjustment might include the normalization of one or more course components and/or the re-weighting of various course components.
Policy on Grade Rounding
Please note that although course grades within the Psychology Department are rounded to the nearest whole number, no further grade rounding will be done. No additional assignments will be offered to enhance a final grade; nor will requests to change a grade because it is needed for a future program be considered.
6 ASSESSMENT/EVALUATION SCHEDULE
- Bi-Weekly Lab/Homework Assignments: Due every other week, Tuesday at 9:00am. See exact schedule below.
- Bi-Weekly Lab/Homework Quizzes: Start of class, after a homework is completed (except Homework 1). See schedule below.
- Media v Science Project:
- Article Annotations: Wednesday, 25 February at 11:55pm
- Comparison Chart: Wednesday, 11 March at 11:55pm
- Final Project: Wednesday, 8 April at 11:55pm
- Midterm Exam: Wednesday, 25 February at 9:30am
- Final Exam: TBA (April Exam Period)
7 CLASS SCHEDULE
|
Class |
Lecture Topic |
Lab Topic |
|
1 7 Jan |
Course introduction and descriptive statistics |
Introduction to Jupyter / Python; Descriptive statistics Lab/Homework 1 Assigned (Mon) |
|
2 14 Jan |
Sampling distributions |
Lab/Homework 1 Due (Tues) |
|
3 21 Jan |
Probability |
Distributions and sampling; Probability Lab/Homework 2 Assigned (Mon) |
|
4 28 Jan |
Estimation, effect size and precision |
Lab/Homework 2 Due (Tues) Lab/Homework Quiz (Wed, 9:30am) |
|
5 4 Feb |
Null hypothesis significance testing (NHST) |
Estimating differences; NHST basics and limitations Lab/Homework 3 Assigned (Mon) |
|
6 11 Feb |
Exam Review and open Q&A |
Lab/Homework 3 Due (Tues) Lab/Homework Quiz (Wed, 9:30am) |
|
7 18 Feb |
Reading week: No class |
No Lab |
|
8 25 Feb |
Midterm (pencil and paper format) Exam tests content from weeks 1-5 |
No Lab Article Annotations (Media v Science) Due (Wed, 11:55pm) |
|
9 4 Mar |
Tests of association: Continuous data & Categorical data |
Simple Correlation/Regression & Chi-squared tests Lab/Homework 4 Assigned (Mon) |
|
10 11 Mar |
Single sample tests |
Lab/Homework 4 Due (Tues) Lab/Homework Quiz (Wed, 9:30am) Comparison Chart (Media v Science) Due (Wed, 11:55pm) |
|
11 18 Mar |
Two-sample tests |
Z-tests, t-tests; Simple group comparisons Lab/Homework 5 Assigned (Mon) |
|
12 25 Mar |
One-way ANOVA |
Lab/Homework 5 Due (Tues) Lab/Homework Quiz (Wed, 9:30am) |
|
13 1 Apr |
Correlated samples tests |
Comparing multiple groups; Non-independent data Lab/Homework 6 Assigned (Mon) |
|
14 8 Apr |
Exam review and open Q&A |
Lab/Homework 6 Due (Tues) Lab/Homework Quiz (Wed, 9:30am) Media v Science Project Due (Wed, 11:55pm) |
|
Exam Period |
Final (pencil and paper format) Exam tests content from weeks 1-12 |
Time: TBA Location: TBA |
8 Academic Integrity
Scholastic offences are taken seriously, and students are directed to read the appropriate policy, specifically, the definition of what constitutes a Scholastic Offence, at the following Web site: https://www.uwo.ca/univsec/pdf/academic_policies/appeals/scholastic_discipline_undergrad.pdf.
Possible penalties for a scholastic offence include failure of the assignment/exam, failure of the course, suspension from the University, and expulsion from the University.
Statement on Use of Electronic Devices
Electronic devices are allowed during ordinary class periods. Electronic devices (all types) may not be used during examinations.
Plagiarism Detection Software
All required papers may be subject to submission for textual similarity review to the commercial plagiarism detection software under license to the University for the detection of plagiarism. All papers submitted for such checking will be included as source documents in the reference database for the purpose of detecting plagiarism of papers subsequently submitted to the system. Use of the service is subject to the licensing agreement, currently between Western and Turnitin.com.
Use of AI
The use of generative AI tools such as ChatGPT to produce written work is not permitted unless permission is granted by the instructor for specific circumstances. Any work submitted must be the work of the student in its entirety unless otherwise disclosed. When used, AI tools should be used ethically and responsibly, and students must cite or credit the tools used in line with the expectation to use AI as a tool to learn, not to produce content.
AI Policy for Psychology:
Responsible use of AI is allowed in Psychology. This includes using AI for brainstorming, improving grammar, or doing preliminary/background research on a topic.
AI is not to be used in place of critical thinking.
The misuse of AI undermines the academic values of this course. Relying on AI to create full drafts or fabricate sources is prohibited. You are ultimately responsible for any work submitted, so it is highly advised that you critically review your Generative AI output before incorporating this information into your assignments.
If you use AI, you must clearly explain its role in your work. All written assignments will require an AI Usage Statement, in which you will indicate what tools you have used, what you have used them for, and (broadly) how you have modified this information. Assignments without an AI Usage Statement will not be accepted.
Violations of this policy will be handled according to Western’s scholastic offense policies.
Multiple Choice Exams
Computer-marked multiple-choice tests and/or exams will be subject to submission for similarity review by software that will check for unusual coincidences in answer patterns that may indicate cheating.
Personal Response Systems (“Clickers”)
In classes that involve the use of a personal response system, data collected will only be used in a manner consistent to that described in this outline. It is the instructor’s responsibility to make every effort to ensure that data remain confidential. However, students should be aware that as with all forms of electronic communication, privacy is not guaranteed.
9 Academic Accommodations and Accessible Education
View Western’s policy on academic accommodations for student with disabilities at this link.
Accessible Education provides supports and services to students with disabilities at Western.
If you think you may qualify for ongoing accommodation that will be recognized in all your courses, visit Accessible Education for more information. Email: aew@uwo.ca Phone: 519 661-2147
10 Absence & Academic Consideration
View Western’s policy on academic consideration for medical illnesses this link
Students must submit all required documentation in order to be approved for certain academic considerations (https://registrar.uwo.ca/academics/academic_considerations/index.html). Students must communicate with their instructors no later than 24 hours after the end of the period covered SMC, or immediately upon their return following a documented absence.
Medical Absences
Submit a Student Medical Certificate (SMC) signed by a licensed medical or mental health practitioner to Academic Counselling in your Faculty of registration to be eligible for Academic Consideration.
Nonmedical Absences
Submit appropriate documentation (e.g., obituary, police report, accident report, court order, etc.) to Academic Counselling in your Faculty of registration to be eligible for academic consideration. Students are encouraged to contact their Academic Counselling unit to clarify what documentation is appropriate.
Religious Consideration
Students seeking accommodation for religious purposes are advised to contact Academic Counselling at least three weeks prior to the religious event and as soon as possible after the start of the term.
11 Other Information
- Office of the Registrar: https://registrar.uwo.ca
- Student Development Services: sdc.uwo.ca
- Psychology Undergraduate Program: https://www.psychology.uwo.ca/undergraduate/index.html
Students who are in emotional/mental distress should refer to Health and Wellness@Western https://www.uwo.ca/health/ for a complete list of options about how to obtain help.
Please contact the course instructor if you require material in an alternate format or if you require any other arrangements to make this course more accessible to you.
If you wish to appeal a grade, please read the policy documentation at: https://www.uwo.ca/univsec/pdf/academic_policies/appeals/appealsundergrad.pdf. Please first contact the course instructor. If your issue is not resolved, you may make your appeal in writing to the Undergraduate Chair in Psychology (psyugrd@uwo.ca).
Copyright Statement
Lectures and course materials, including power point presentations, outlines, videos and similar materials, are protected by copyright. You may take notes and make copies of course materials for your own educational use. You may not record lectures, reproduce (or allow others to reproduce), post or distribute any course materials publicly and/or for commercial purposes without the instructor’s written consent.
12 Land Acknowledgement
We acknowledge that Western University is located on the traditional territories of the Anishinaabek, Haudenosaunee, Lūnaapéewak, and Chonnonton. Nations, on lands connected with the London Township and Sombra Treaties of 1796 and the Dish with One Spoon Covenant Wampum. This land continues to be home to diverse Indigenous Peoples (First Nations, Métis and Inuit) whom we recognize as contemporary stewards of the land and vital contributors of our society.
