Psychology 2811B 200 FW23

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.



LONDON               CANADA 

Department of Psychology 

Winter 2024 


Psychology 2811B - 200 

Statistics for Psychology I 





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. 


Pre or Corequisites 

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, or registration in Year 2 of an Honours Specialization in Neuroscience with special permission from the program administrator. Mathematics 1228A/B is recommended. 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/BData Science 2000A/B may be substituted for Data Science 1000A/B for students entering the program with 1.0 Year 1 Math courses. 


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. 


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 




Lecture (in person):  see Student Centre timetable

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



Instructor: Dr. Erin Heerey 

Office: SSC 6322 (519-661-2111 ext. 86917) 


Office Hours: Tuesdays, 3pm – 4pm; Zoom (see link on OWL; Passcode: 2811b) 


TAs: See information on OWL for names, email addresses and office hours. 


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. You may also contact Accessible Education at  or 519-661-2147. 




Please note: For courses delivered in an online format, that include an online component, or are required to pivot online, students must have a reliable internet connection and computer that are compatible with online learning system requirements. Some courses may also require the use of a remote proctoring platform to ensure assessments are taken fairly in accordance with Western’s policy on Scholastic Discipline for Undergraduate Students and Scholastic Discipline for Graduate Students 





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. 




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 


Lectures; readings; lab activities 


Bi-Weekly homework; Exams 


Bi-Weekly homework; Exams 

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 


Bi-Weekly homework; Exams 


Bi-Weekly homework; Exams 



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 


Bi-Weekly homework; Statistics in the News Project; Exams 


Bi-Weekly homework; Exams 

Application of Methodologies.  

Produce code in Jupyter Notebook to calculate statistical tests and data visualizations. 



Lectures; readings; lab activities 


Bi-Weekly homework; Exams 


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

Lab activities 


Bi-Weekly homework; Exams 

Awareness of Limits of Knowledge. 

Explain the strengths and weaknesses of null hypothesis significance testing. 


Lectures; readings 


Bi-Weekly homework; Statistics in the News Project; Exams 













Lab/Homework Assignments 15% 

Statistics in the News Project 15% 

Midterm Exam 32% 

Final Exam 38%  


All elements of the coursework (including exams) are necessary for demonstrating your knowledge of the core learning outcomes of the course. 


Bi-weekly Lab/Homework Assignments (15%): 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 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 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). 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 and all your notes must be entirely handwritten. 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 Tuesday, 27 February (12:30pm to 2:30pm). You will have the class period 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% of the final grade): 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. 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 in-person, rather than online studies). However, the bonus will only be added if you have achieved a passing course grade without any bonus credit – in other words before bonus credits are added you must get at least 50% on the regular coursework/exams. 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 I will be given this information at the end of the term. No grade adjustments will appear until after the final grade has been calculated. 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. If you wish to earn extra credit but do not wish to participate in studies, more information about an alternative assignment will be available on OWL. The maximum number of bonus credits you may earn is 3.0. You may include a blend of SONA and alternate assignments in the credits. For each credit you earn (up to 3.0), you will receive an additional 1% in the gradebook. All extra credits must be completed by noon on the last day of term to count toward your grade. Because this coursework is extra credit and will never count against you, there will be absolutely no exceptions to this deadline. 




Weekly Lab/Homework Assignments: Assignments are due at 5:00pm on Friday evening of the week they are due (see schedule below). The solution to the assignment will be released the following 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 3%; equivalent to 2.5 homework points). 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 assignment mark will automatically reflect the late penalty. 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 only 3% each and the lowest score is dropped, I will not accept any excuses for missed assignments. If you must miss a series of three or more consecutive assignments 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 (which will make it worth a larger portion of your total grade) or, if you have completed at least 2 other assignments, will each be given a score equal to the average of all the other homework marks. Simply failing to submit homework assignments during the term without academic consideration will ensure that missed assignment marks remain a 0.  


Statistics in the News Project: The project will be due at 11:55pm on Tuesday 2 April. 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 48 hours. There is no need to email the course staff about your late project, as the submission portal will remain available for 48 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. If academic counselling approves a late submission request, your assignment will be due 48 hours after the expiration of your consideration. You must upload the completed assignment to the assignment portal within that 48-hour period. If your assignment is late, it will be penalized as above.  


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 the exam. There will be one opportunity to make up the final exam. The make-up final exam will be held on MONDAY 9 MAY AT 9:15AM. Note that the make-up exam may include new test questions and may be in a different format from the original exam. Note that if you miss the make up exam, your next opportunity to take the final exam will be during the finals period the next time the course is offered. 

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: 


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 


Note that 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. To maximize your grade, do your best on each and every assessment within the course. 




Bi-Weekly Lab/Homework Assignments 

On Monday every other week, a new lesson, including a lab/homework assignment will be released on OWL, starting in Week 1. The assignment will be related to that week’s lesson and will be due 12 days later on Friday at 5:00pm. You will upload your homework to the Gradescope portal.  


Statistics in the News Project Tuesday, 2 April at 11:55pm 

Midterm Exam Tuesday, 27 February at 12:30pm 

Final Exam TBA (April Exam Period) 




Lecture Topic 

Lab Topic 


9 Jan 

Course introduction and descriptive statistics  

Introduction to Jupyter / Python; Descriptive statistics 

Lab/Homework 1 Assigned (Monday) 


16 Jan 

Sampling distributions  

Lab/Homework 1 Due (Friday) 


23 Jan 


Distributions and sampling; Probability 

Lab/Homework 2 Assigned (Monday) 


30 Jan 

Estimation, effect size and precision 

Lab/Homework 2 Due (Friday) 


6 Feb 

Null hypothesis significance testing (NHST) 

Estimating differences; NHST basics and limitations  

Lab/Homework 3 Assigned (Monday) 


13 Feb 

Tests of association: Continuous data & Categorical data 

Review Stats in the News Assignment Guidelines 

Lab/Homework 3 Due (Friday) 


20 Feb 

Reading week 

No Lab 


27 Feb 

Midterm (pencil and paper format) 

Exam tests content from weeks 1-5 

Simple Correlation/Regression & Chi-squared tests 

Lab/Homework 4 Assigned (Monday) 


5 Mar 

Single sample tests 

Lab/Homework 4 Due (Friday) 


12 Mar 

Two-sample tests 

Z-tests, t-tests; Simple group comparisons 

Lab/Homework 5 Assigned (Monday) 


19 Mar 

One-way ANOVA 

Lab/Homework 5 Due (Friday) 


26 Mar 

Correlated samples tests 

Comparing multiple groups; Non-independent data 

Lab/Homework 6 Assigned (Monday) 


2 Apr 

Exam review and open Q&A 

Lab/Homework 6 Due (Friday) 

Exam Period 

Final Exam (pencil and paper format) 

Exam tests content from weeks 1-12 

Time: TBA 

Location: TBA 




8.0 Land Acknowledgement 


We acknowledge that Western University is located on the traditional lands of the Anishinaabek, Haudenosaunee, Lūnaapéewak and Attawandaron peoples, on lands connected with the London Township and Sombra Treaties of 1796 and the Dish with One Spoon Covenant Wampum. 


With this, we respect the longstanding relationships that Indigenous Nations have to this land, as they are the original caretakers. We acknowledge historical and ongoing injustices that Indigenous Peoples (e.g. First Nations, Métis and Inuit) endure in Canada, and we accept responsibility as a public institution to contribute toward revealing and correcting miseducation, as well as renewing respectful relationships with Indigenous communities through our teaching, research and community service. 




Students are responsible for understanding the nature and avoiding the occurrence of plagiarism and other scholastic offences. Plagiarism and cheating are considered very serious offences because they undermine the integrity of research and education. Actions constituting a scholastic offence are described at the following link: 


As of Sept. 1, 2009, the Department of Psychology will take the following steps to detect scholastic offences. All multiple-choice tests and exams will be checked for similarities in the pattern of responses using reliable software, and records will be made of student seating locations in all tests and exams. All written assignments will be submitted to TurnItIn, a service designed to detect and deter plagiarism by comparing written material to over 5 billion pages of content located on the Internet or in TurnItIn’s databases. 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 ( 


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. 


In classes that involve the use of a personal response system (PRS), data collected using the PRS 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. Your PRS login credentials are for your sole use only. Students attempting to use another student’s credentials to submit data through the PRS may be subject to academic misconduct proceedings.  


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. 



Tests and examinations for online courses will be conducted using a remote proctoring service. By taking this course, you are consenting to the use of this software and acknowledge that you will be required to provide personal information (including some biometric data) and the session will be recorded.  Completion of this course will require you to have a reliable internet connection and a device that meets the technical requirements for this service. More information about this remote proctoring service, including technical requirements, is available on Western’s Remote Proctoring website at: 

In the event that in-person exams are unexpectedly canceled, you may only be given notice of the use of a proctoring service a short time in advance. 




Western’s policy on Accommodation for Medical Illness can be found at: 


If you experience an extenuating circumstance (e.g., illness, injury) sufficiently significant to temporarily make you unable to meet academic requirements, you may request accommodation through the following routes:  

  1. For medical absences, submitting a Student Medical Certificate (SMC) signed by a licensed medical or mental health practitioner in order to be eligible for Academic Consideration;  
  1. For non-medical absences, submitting appropriate documentation (e.g., obituary, police report, accident report, court order, etc.) to Academic Counselling in their Faculty of registration in order to be eligible for academic consideration. Students are encouraged to contact their Academic Counselling unit to clarify what documentation is appropriate. 


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


Students seeking academic consideration: 

  • are advised to consider carefully the implications of postponing tests or midterm exams or delaying handing in work;   
  • 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 


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. 


12.0 Contingency Plan for Return to Lockdown: IN-Person & Blended classes 


In the event of a COVID-19 resurgence or any other event that necessitates the course delivery moving away from face-to-face interaction, all remaining course content will be delivered entirely online, either synchronously (i.e., at the times indicated in the timetable) or asynchronously (e.g., posted on OWL for students to view at their convenience). The grading scheme will not change. Any remaining assessments will also be conducted online, as determined by the course instructor. 




In courses involving online interactions, the Psychology Department expects students to honour the following rules of etiquette: 

  • please “arrive” to class on time 
  • please use your computer and/or laptop if possible (as opposed to a cell phone or tablet) 
  • please ensure that you are in a private location to protect the confidentiality of discussions in the event that a class discussion deals with sensitive or personal material 
  • to minimize background noise, kindly mute your microphone for the entire class until you are invited to speak, unless directed otherwise 
  • In classes larger than 30 participants please turn off your video camera for the entire class unless you are invited to speak 
  • In classes of 30 students or fewer, where video chat procedures are being used, please be prepared to turn your video camera off at the instructor’s request if the internet connection becomes unstable 
  • Unless invited by your instructor, do not share your screen in the meeting 


The course instructor will act as moderator for the class and will deal with any questions from participants. To participate please consider the following: 

  • If you wish to speak, use the “raise hand” function and wait for the instructor to acknowledge you before beginning your comment or question. 
  • Please remember to unmute your microphone and turn on your video camera before speaking. 
  • Self-identify when speaking. 
  • Please remember to mute your mic and turn off your video camera after speaking (unless directed otherwise). 


General considerations of “netiquette”: 

  • Keep in mind the different cultural and linguistic backgrounds of the students in the course. 
  • Be courteous toward the instructor, your colleagues, and authors whose work you are discussing. 
  • Be respectful of the diversity of viewpoints that you will encounter in the class and in your readings. The exchange of diverse ideas and opinions is part of the scholarly environment. “Flaming” is never appropriate. 
  • Be professional and scholarly in all online postings. Use proper grammar and spelling. Cite the ideas of others appropriately. 


Note that disruptive behaviour of any type during online classes, including inappropriate use of the chat function, is unacceptable. Students found guilty of Zoom-bombing a class or of other serious online offenses may be subject to disciplinary measures under the Code of Student Conduct. 






Office of the Registrar:   


Student Development Services:  


Psychology Undergraduate Program: 


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 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 of the remote learning sessions for this course (if scheduled) may be recorded. The data captured during these recordings may include your image, voice recordings, chat logs and personal identifiers (name displayed on the screen). 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.