Psychology 2811A 650 SU24

Statistics for Psychology I

If there is a discrepancy between the outline posted below and the outline posted on the OWL course website, the latter shall prevail.


Western University

London                   Canada


Department of Psychology

Summer 2024


Psychology 2811A    Section 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, Psychology 2855F/G, Psychology 2856F/G, 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): At least 60% in 1.0 credits of Psychology at the 1000 level; Data Science 1000A/B and 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, or registration in Year 2 of an Honours Specialization in Neuroscience with special permission from the program administrator. Mathematics 1228A/B is recommended. In addition to completion of 1.0 Psychology 1000-level course, 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. 


2 lecture hours and 2 laboratory hours, 0.5 course 




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     Course Information


Instructor:                  Krista Macpherson, PhD

Office & Phone:          SSC 7430, ex: 84627

Office Hours:              Thursdays 3pm-4pm (via Zoom)



Time and Location of Classes: Online (Asynchronous); Lecture recordings will be recorded/posted to Brightspace on Wednesdays at 2pm. 


For courses that include an online component, students must have a reliable internet connection and computer that are compatible with online learning system requirements.


3     Course Materials


Cote, L. R., Gordon, R., Randell, C. E., Schmitt, J., & Marvin, H. (2021). Introduction to Statistics in the Psychological Sciences. Open Educational Resources Collection. 25. Available at:  


4     Course Objectives and Learning Outcomes


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.  


Learning Outcome

Learning Activity


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 



Weekly homework; Exams 



Knowledge of Methodologies

·         Produce code to accurately calculate statistical tests and data visualizations. 

Lectures; readings; lab activities 

Weekly homework; Exams 

Application of Knowledge

·         Produce appropriate statistics to describe sample data. 


·         Plot sampling distributions and graphs that show the relationships between continuous and categorical data. 


Lab activities 

Weekly homework; Exams

Communication Skills

·         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 

Weekly homework; Statistics in the News Project; Exams 



Awareness of Limits of Knowledge

·         Explain the strengths and weaknesses of null hypothesis significance testing. 

Lectures; readings 

Weekly homework; Exams

Autonomy and Professional Capacity

·         Demonstrate basic data wrangling skills including outlier exclusion, data cleaning and transformation. 

Lab activities 



Weekly homework; Exams 


5     Evaluation


Weekly Homework 18%  

Statistics in the News Project 15% 

Midterm Exam 30% 

Final Exam 37%  


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


Weekly Homework (18%): Each week, you will complete a set of homework problems in a Jupyter Notebook. These will be based on the lecture material for the week. The Jupyter Notebook with the assignment will be released on Brightspace after lecture each Wednesday. It will be due 9 days later, on Friday at 5pm. You must upload the Notebook to the homework portal on Brightspace. Note that you may not submit the Notebook over email – the UWO email server will reject it because it is an executable file. There are a total of 10 homeworks that you will complete over the course of the term. I will drop your lowest homework score, which means that you can skip the homework once without penalty. Each of the remaining 9 homeworks will count toward 2% of your grade. The solution to the homework will be released on Monday at noon. If your homework has not been submitted before the solution is posted, you will receive a grade of 0.  


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 be true. The statistic should also have a clear source citation (e.g., a research article, published in a scientific journal; a report from StatsCanada). You should then critically evaluate the claim, as well as the original source article, and interpret the news report. Write a 280-character Tweet that states your conclusion. Additional requirements and rubric are available in the assignment guidance on Brightspace. 


Exams (67%): There will be two online exams in the course. The midterm will cover the course material from weeks 1-5. The final will be cumulative (weeks 1-13). Both exams will be completed online using Gradescope, and will consist of  multiple choice, fill in the blank, and some short answer questions.  Exams will be in a timed, linear format.  Although lectures are asynchronous, all students will write exams at the scheduled time.



Policy on Missing Coursework


Weekly Homework: Homework is due at 5pm on Friday evening each week (starting in week 3). The solution to the homework will be released on Monday at noon. For each 24-hour period (or portion thereof) that your homework is late until Monday at noon, it will incur a penalty of 0.5% (out of 2%). There is no need to email the course staff about late homework, as the submission portal will remain available until the answer key is released. The homework mark will automatically reflect the late penalty. If your homework has not been completed by the time the answer key is released, it will receive a score of 0. Because the homeworks are worth only 2% each and the lowest score is dropped, a missed homework will not be accepted under any circumstances. If you miss a series of four or more homeworks in a row, due to a long-term illness or other issue of concern, please contact academic counselling in your home faculty with appropriate documentation to request relief. If academic counselling approves your request, the missed homework marks will be added to the weighting of final exam mark. This will make the final exam worth a larger proportion of the total mark. 


Statistics in the News Project:  For each 24-hour period (or portion thereof) that your project is late, it will incur a penalty of 1.5% (out of 15%) up to a maximum of 36 hours. There is no need to email the course staff about your late project, as the submission portal will remain available for 36 hours after the project is due. After that point, the project will be assigned a score of 0, unless academic counselling in your home faculty approves a request for late submission. In the event that academic counselling approves a late submission request, please contact the course instructor to discuss a revised due date.  



Exams: If you need to miss an exam due to illness or other issue, you MUST request relief from academic counselling. Without an approved accommodation from academic counselling, you will not be allowed to take the make-up exam. There will be one opportunity to make up each missed exam.


The Psychology Department follows Western’s grading guidelines:


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


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


Weekly Quizzes                                          Due at 2pm every Friday                                                    

Written Assignments                                 Due one week from date assigned                      Midterm Examination                               Thurs June 19th @ 7pm 

Final Examination                                      TBA (During final exam period)


7     Class Schedule



Lecture Topic



1 (May 8th)

Course Intro; Descriptive Stats

Intro to Python/Jupyter

Chapter 1-6

2 (May 15th)

Sampling Distributions; Homework 1 Assigned

Distributions and Sampling

Chapter 1-6

3 (May 22nd)

Probability; Homework 2 Assigned

Probability; Homework 1 Due

Chapter 1-6

4 (May 29th)         

Estimation, Effect Size, Precision; Homework 3 Assigned

Estimating differences;

Homework 2 Due

Chapter 1-6

5 (June 5th)

Hypothesis Testing; Homework 4 Assigned

Basics and Limitations of Hypothesis Testing; Homework 3 Due

Chapter 7

6 (June 12th)


Tests of Association; Homework 5 Assigned

Correlation & Chi Square; Homework 4 Due

Chapters 12 & 14

7 (June 19th)


Statistics in the News Assigned; Homework 5 Due

No Readings

8 (June 26th)

Single Sample tests, Homework 7 Assigned

Z-tests and t-tests; Homework 6 Due

Chapters 4 & 8

9 (July 3rd)

Two-Sample Tests; Homework 8 Assigned

Comparing 2 groups; Homework 7 Due

Chapter 10

10 (July 10th)

One-way ANOVA; Homework 9 Assigned

Comparing 2+ groups; Homework 8 Due

Chapter 11

11 (July 17th)

Within Subjects Tests; Homework 10 Assigned

Non-independent data; paired samples t-test; Homework 9 Due

Chapter 9

12 (July 24th)

Exam Review, Q&A

Review; Homework 10 Due

No Readings


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:


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.


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


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.


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.


Exam Proctoring Software


Tests and examinations for online courses may be conducted using a remote proctoring service. More information about this remote proctoring service, including technical requirements, is available on Western’s Remote

Proctoring website at:


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:  Phone: 519 661-2147


10  Absence & Academic Consideration


View Western’s policy on academic consideration for medical illnesses this link


Find your academic counsellor here:


Students must see the Academic Counsellor and submit all required documentation in order to be approved for certain academic considerations. 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



Students who are in emotional/mental distress should refer to Health and Wellness@Western 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: 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 (


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.


Policy on the Recording of Synchronous Sessions


Some or all the learning sessions for this course may be recorded. The data captured during these recordings may include your image, voice recordings, chat logs and personal identifiers. The recordings will be used for educational purposes related to this course, including evaluations. The recordings may be disclosed to other individuals participating in the course for their private or group study purposes. Please contact the instructor if you have any concerns related to session recordings. Participants in this course are not permitted to privately record the sessions, except where recording is an approved accommodation, or the student has the prior written permission of the instructor.


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.