Psychology 2811A 650 SU25

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 2025

 

Psychology 2811    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,  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; 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.

 

 

 

2 lecture hours; 2 tutorial hours; Course Weight: 0.5

 

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:                  Dr. Krista Macpherson

Office & Phone:          SSC 7230

Office Hours:              By Appointment

Email:                          kmacphe6@uwo.ca

 

 

Time and Location of Classes: Asynchronous

 

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: https://umsystem.pressbooks.pub/isps/  

 

 

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

Assessment

Knowledge Acquisition

Demonstrate basic knowledge of probability as it applies to sampling. 

 

Lectures and Lab activities

Weekly homework; exams

Critical Thinking

Interpret both graphical and statistical evidence to make conclusions about data.

 

 

Lectures and Lab activities

Weekly homework; exams

Problem Solving; Inquiry and Analysis)

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

Lectures and Lab activities

Weekly homework; exams

 

Communication

Explain the strengths and weaknesses of null hypothesis significance testing.

Lectures and Lab activities

 Weekly homework; exams

Critical Thinking; Problem Solving

Recognize from data and/or study design descriptions which statistical tests should be used.

 

Lectures and Lab activities

Weekly homework; exams

 

 

5     Evaluation

 

The following are the dates and materials assigned for each of the designated assessments.

 

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

 

Policy on Missing Coursework

 

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.

 

The Psychology Department follows Western’s grading guidelines:  https://www.uwo.ca/univsec/pdf/academic_policies/general/grades_undergrad.pdf

 

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 Exam: Wednesday June 18th @ 7pm

Final Exam: TBA (During Final Exam Period)

 

7     Class Schedule

 

(Tentative and subject to change—you will be notified via Brightspace of any changes)

 

Week

Date

Topic(s) to be covered

Lab Topic (See Brightspace for Content)

Readings

1

May 5th

Course Intro; Descriptive Stats

Intro to Python/Jupiter

Chapters 1-6

2

 May 12th

Sampling Distributions, Homework 1 Assigned

Distributions and Sampling

Chapters 1-6

3

 May 19th

Probability; Homework 2 Assigned

Probability; Homework 1 Due

Chapters 1-6

4

May 26th

Effects Sizes & Precision; Homework 3 Assigned

Estimating Differences; Homework 2 Due

Chapters 1-6

5

June 2nd

Hypothesis Testing; Homework 4 Assigned

Basics of NHST; Homework 3 Due

Chapter 7

6

June 9th

Tests of Association; Homework 5 Assinged

Correlation & Chi Square; Homework 4 Due

Chapters 12 & 14

7

June 16th

Midterm Wednesday June 18th @ 7pm

 

Statistics in the News Assigned; Homework 5 Due

No Readings

8

June 23rd

Single Sample Tests, Homework 7 Assigned

Z-tests & T-tests; Homework 6 Due

Chapter 4 & 8

9

June 30th

Two-Sample Tests; Homework 8 Assigned

Comparing 2 groups; Homework 7 Due

Chapter 10

10

July 7th

Oneway ANOVA; Homework 9 Assigned

Comparing 2+ Groups, Homework 8 Due

Chapter 11

11

July 14

Within Subjects Tests; Homework 10 Assigned

Non-Independent Data; paired samples t-test; Homework 9 Due

Chapter 9

12

July 21

Exam 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: 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.

 

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.

 

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: https://remoteproctoring.uwo.ca.

 

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

 

Academic Considerations: https://registrar.uwo.ca/academics/academic_considerations/index.html

 

 

11  Other Information

 

 

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.