Course syllabus
Applied Statistics for Life Sciences
Statistics plays a crucial role in the sciences: statistical techniques provide a means of weighing quantitative evidence derived from observation and experimentation while accounting for uncertainty. This class aims to provide a hands-on introduction to common statistical methods used almost universally across the sciences and a primer on statistical concepts. Examples from the life sciences emphasize applications with relevance to students’ majors, and students learn to perform simple analyses in R.
Instructor: Trevor Ruiz (he/him) [email]
Learning assistant: Emi Degembe (she/they) [email]
Class meetings: 2:10pm — 4:00pm TR 005-225
Office hours and learning assistant hours:
- [OH] 1:10pm — 2:00pm TR 025-236 or Zoom [by appointment]
- [LAH] TBD 025-107G or Zoom
Catalog Description: Data collection and experimental design, descriptive statistics, confidence intervals, parametric and nonparametric one- and two-sample hypothesis tests, analysis of variance, correlation, simple linear regression, chi-square tests. Applications of statistics to the life sciences. Substantial use of statistical software. Prerequisite: MATH 96; or MATH 115; or appropriate Math Placement Level. Fulfills GE Area B4 (GE Area B1 for students on the 2019-20 or earlier catalogs); a grade of C- or better is required in one course in this GE area.
Materials
You’ll need an internet-connected laptop. A tablet with a keyboard will work but is not recommended. You should expect to bring your laptop (or tablet) to every class meeting.
Software: use of statistical software (R/RStudio) will be hosted online via a posit.cloud workspace. You’ll need to create a posit.cloud account and purchase a $5/month student plan. [workspace] [setup instructions]
Textbook: Vu and Harrington (2020). Introdutory Statistics for the Life and Biomedical Sciences, First edition. This will be our primary reference and we will cover chapters 1 – 2, 4 – 6, and 8. The book is free; I suggest a $5 donation. [download book]
Course notes: course notes will be posted as slides on the course website.
Other references:
Van Belle, Fisher, Heagerty, and Lumley (2004). Biostatistics: a methodology for the health sciences. This text provides a thorough introduction to biostatistics (statistics for clinical medicine) and is an excellent (if more advanced) reference for those students interested in more breadth and depth of coverage of statistical methods. [get PDF]
Douglas et al. (2023). An Introduction to R. This text provides an introductory-level overview of R as a programming environment and may interest those students keen to learn more about computing with R. [online book]
Learning outcomes
By the end of the course, successful students will be able to:
[L1] design a data collection scheme based on simple random sampling or simple experimental designs
[L2] distinguish between observational studies and experiments and understand the limitations (practical and consequential) of each
[L3] summarize data using graphical and numerical techniques
[L4] construct and interpret confidence intervals for means and differences between means for independent and paired samples
[L5] conduct parametric and non-parametric two-sample hypothesis tests for means
[L6] construct and interpret a confidence interval for a single proportion
[L7] conduct Chi-square goodness-of-fit tests and tests for independence
[L8] distinguish between case-control and cohort studies and compute relative-risk and odds in the appropriate settings
[L9] perform analysis of variance tests and post-hoc comparisons for completely randomized designs
[L10] use simple linear regression to describe relationships between variables
Emphasis is placed on conceptual fluency, application, and interpretation.
Remark on use of software. You will use R extensively throughout the course. Please note that the course description prescribes “substantial use of statistical software” keep in mind that this is not a programming class. My expectation is that you will follow guided examples in order to perform statistical analyses and learn to interpret outputs correctly — you will not be expected to write any codes from scratch.
Assessments
Attainment of learning outcomes will be measured by performance on homework assignments, tests, and a short project with an oral assessment in lieu of a final exam.
Homework assignments (20%) will be given at the end of every class meeting and will comprise 1-3 short practice problems due by the next class meeting. These are your opportunity to practice applying course concepts and methods covered in class and will help you to keep current with the pace and content of the lectures.
Tests (60%) will be given at approximate 3 week intervals in class. These are your opportunity to demonstrate that you’ve synthesized course material and achieved learning outcomes. One round of revisions will be allowed for each test in which you can earn back a portion of missed credit from your initial attempt.
A comprehensive final exam (20%) will be given at the end of the quarter. You will need to be available in person for the final assessment.
Every assessed problem will be matched to one of the learning outcomes L1-L10. All submitted work will be assessed on a question-by-question basis according to whether responses are fully correct. The percentage of problems matched to a particular learning outcome for which you receive a satisfactory assessment provides a measure of your attainment of that learning outcome. These percentages form the basis for determining your course grade (see below).
Letter grades
Students will receive a score for each learning outcome representing the proportion of questions matched with that outcome that received a satisfactory assessment across all assignments. The outcome will be assessed as follows:
‘fully met’ if the proportion is at least 0.8
‘partly met’ if the proportion is between 0.5 and 0.8
‘unmet’ otherwise
To receive a passing grade in the class, at least six outcomes must be either partly or fully met. Subject to this condition, letter grades are then defined as follows:
| Grade | Number of fully met outcomes |
|---|---|
| A | 10 |
| A- | 9 |
| B+ | 8 |
| B | 7 |
| B- | 6 |
| C+ | 5 |
| C | 4 |
| C- | 3 |
| D+ | 2 |
| D | 1 |
| D- | 0 |
Please note that these definitions are tentative and potentially subject to change. Please also note that failure to adhere to course policies may result in a lower letter grade than would otherwise be assigned.
Tentative schedule
Subject to change.
| Week | Topics | Readings (V&H) | Assessments |
|---|---|---|---|
| 1 (1/6/25) | Study design; data semantics; descriptive statistics | 1.1-1.2; 1.4-1.6 | |
| 2 (1/13/25) | Point and interval estimation | 3.3.1-3.3.3; 4.1-4.2 | |
| 3 (1/21/25) | no new content | Test 1 [L1, L2, L3, L4] | |
| 4 (1/28/25) | Hypothesis testing | 4.3.1-4.3.4 | |
| 5 (2/3/25) | Two-sample inference; statistical power | 5.3-5.4 | |
| 6 (2/10/25) | Nonparametric inference | van Belle 8.4-8.5 | Test 2 [L4, L5] |
| 7 (2/17/25) | Analysis of variance (ANOVA) | 5.5.1-5.5.4 | |
| 8 (2/24/25) | Inference for proportions; tests of association | 8.1-8.3 | |
| 9 (3/3/25) | Relative risk and odds ratios | Test 3 [L6, L7, L8, L9] | |
| 10 (3/10/25) | Simple linear regression | 6.1, 6.2, 6.4 | |
| Finals (3/15/24) | N/A | N/A | Final exam [L1-L10] |
Tips for success
I want you to succeed in this course. A few simple practices will help:
- Don’t ignore the reading. I recommend skimming the text before class for a primer on the relevant topic(s) and then reading in full depth after class.
- Form a study group and set a regular time to meet. Having a few peers you can ask for help is a great way to ‘fill in the gaps’ between class meetings and office hours. It also helps morale and is sometimes a good way to make friends.
- If you’re struggling with the class, come talk to me sooner rather than later. Asking for help isn’t always easy, but the sooner you do, the more options we’ll have.
Remember that while I am here to help, you are responsible for your own learning. The increased flexibility and autonomy you have in college carries with it the expectation that you take initiative in making use of the resources available to help you succeed. Please don’t be shy about asking questions in class, coming to office hours, seeking help from our learning assistant, finding help among peers, and using tutoring resources on campus.
Policies
Time commitment
STAT218 is a four-credit course, which corresponds to a minimum time commitment of 12 hours per week, including class meetings, reading, assignments, and study time.
In order to succeed in the course, you should expect to invest between 12 and 16 hours per week on average. Please let me know if you are regularly exceeding this amount or if you need help managing your time efficiently in the course. While I aim to keep the workload fairly even throughout the quarter, you should allow an extra hour or two in your schedule to accommodate week to week variations in workload as needed.
Attendance and absences
Regular attendance is essential for success in the course and required per University policy. Absences should be excusable, but you do not need to notify me unless you anticipate an extended absence or will miss an in-class assessment; I trust you to adhere to Cal Poly norms and policies regarding class attendance. An occasional absence is acceptable, but please keep in mind that each and every class meeting represents more than 5% of total class time for the quarter. If you choose not to come to class on a regular basis, you are not meeting course expectations and I reserve the right to adjust your course grade accordingly at the end of the quarter.
If at any point in the quarter you anticipate an extended period of absence, please let me know so that I can work with you to keep up in the course.
Collaboration
Collaboration with classmates is encouraged on homework assignments. If you work with a group on homework problems, you are expected to be an active contributor and prepare your own solutions in your own words and writing, and by submitting your work you are attesting that you have met this expectation. You should not distribute or accept copies of written solutions or submitted work under any circumstances.
Communication and email
I encourage you to ask questions in class and during office hours, since that is the only certain means of obtaining a response within a guaranteed time frame.
I respond to most email within 24 weekday hours, but I cannot guarantee this response time and I occasionally miss messages altogether (though I try not to). I don’t answer emails at night or on weekends, so while you are welcome to write me outside of business hours, please don’t expect a reply until the following business day. I also sometimes get behind on answering emails, so please wait a few days (preferably one week if it’s not pressing) before sending a follow up or reminder.
Please do not ask technical questions about problems or course material by email.
Late and missing work
A one-hour grace period is applied to all deadlines. Work submitted more than one hour after a deadline is considered late.
I understand that unexpected circumstances may arise and require you to temporarily rearrange your priorities and commitments on occasion during the quarter. You may, at any time during the quarter and without notice or penalty, use the following personal exceptions:
turn in two homework assignments up to one week late
miss one homework assignment altogether
Once your personal exceptions are exhausted, homework assignments turned in up to one week late will be awarded 50% credit unless an extension is granted in advance, and missing assignments will be counted at zero credit.
No other late work will be accepted unless an exception to this policy is granted. I will consider exceptions for personal and medical emergencies or other similarly unforeseeable circumstances.
Grades and assessments
I make my best effort to assess your work fairly and accurately and to apply assessment criteria consistently across the class. While I sometimes do so imperfectly, I am also aware that granting adjustments to scores or grades can disadvantage more reticent students and favor those more comfortable approaching me about credit awarded on course assessments.
So, in consideration of maintaining fairness and consistency, I ask that you limit requests for reassessment to clear mistakes, discrepancies, or oversights; and I also ask that you please do let me know if you think such an error has likely occurred. Please raise any such issues in a timely manner and not at the end of the quarter.
Per University policy, faculty have final responsibility for grading criteria and grading judgment and have the right to alter student assessment or other parts of the syllabus during the term. It is not appropriate to attempt to negotiate scores or final grades. Once the term has concluded, final grades will only be changed in the case of clerical errors, without exception. If you feel your grade is unfairly assigned at the end of the course, you have the right to appeal it according to the procedure outlined here.
Accommodations
It is University policy to provide, on a flexible and individualized basis, reasonable accommodations to students who have disabilities that may affect their ability to participate in course activities or to meet course requirements. Accommodation requests should be made through the Disability Resource Center (DRC).
Conduct and Academic Integrity
You are expected to be aware of and adhere to University policy regarding academic integrity and conduct. Detailed information on these policies, and potential repercussions of policy violations, can be found via the Office of Student Rights & Responsibilities (OSRR).
Remarks on AI and academic integrity. Learning to use AI effectively and responsibly for problem-solving in an academic context is a skill unto itself. ChatGPT and similar tools may be used as resources but are not acceptable substitutes for doing your own work. Use of AI is acceptable but must be documented with a record of prompts and outputs stored as a PDF or plain-text document and uploaded with your homework submission. Failure to provide adequate documentation will result in loss of credit; direct submission of AI-generated material is considered plagiarism and will result in loss of credit and OSSR reporting for the first offense, and course failure for the second offense.
Copyright and distribution of course materials
Students are not permitted to share or distribute any course materials without the written consent of the instructor. This includes, in particular, uploading materials or prepared solutions to online services and sharing materials or prepared solutions with students who may take the course in a future term.