Course syllabus

Applied Statistics for Life Sciences

Course listing

STAT218

Updated

June 2025

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]

Office hours: TWR on Zoom and [by appointment]

Class meetings: asynchronous online [canvas] [youtube] [piazza]

Final exam: in-person Thursday 7/24/25 10:10am – 1:00pm in 38-122

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, mainly owing to the small screen size.

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” but 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 and a final exam.

  • Homework assignments (50%) will be given weekly. 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. You can revise each homework assignment once and submit corrections within one week of the original due date to earn back missed credit.

  • A comprehensive final exam (50%) will be given at the end of the session. 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, or, for a handful of problems, to an extra credit category “LX”. All submitted work will be assessed on a question-by-question basis according to whether responses are satisfactory. 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 score is at least 0.8

  • ‘partly met’ if the score is between 0.5 and 0.8

  • ‘unmet’ otherwise

Your LX score is used as the basis for a balance (typically 10-20% of the raw score) against which you can borrow to meet the thresholds above.

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 9
A- 8
B+ 7
B 6
B- 5
C+ 4
C 3
C- 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 (3/31/25) Data, descriptive statistics, and inference foundations 1.1-1.5, 3.3.1-3.3.3, 4.1-4.2 HW1
2 (4/7/25) Inference for one, two, and many means 4.3, 5.3-5.5 HW2
3 (4/14/25) Nonparametric methods; regression 6.1-6.5 HW3
4 (4/21/25) Inference from categorical data 8.1-8.4 HW4
5 (4/28/25) Relative risk and odds ratios 8.5 Final exam

Tips for success

I want you to succeed in this course. Here are my suggestions for how to get the most out of the class and set yourself up for success:

  1. Come to office hours and/or use Piazza to discuss assigned work. Whether you haven’t figured out where to begin or just want to double-check your work, talking through problems is a great way to learn and creates opportunities to answer questions you may not have known you had.
  2. Find a peer or peer group to work with and make a habit of going over assignments together. Your assigned discussion group in Piazza is there for this purpose to help you connect with other students.
  3. 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.
  4. Try the extra credit problems when they are given. While they have a small numerical impact on your grade, they can make a big difference if you are near a threshold.
  5. If you’re struggling with the class, 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, and I expect you to take initiative in making use of the resources available to help you succeed.

Policies

Time commitment

STAT218 is a four-credit course, which corresponds to a minimum time commitment of 24 hours per week during each week of the 5-week summer session, including lectures, labs, reading, assignments, and study time. In order to succeed in the course, you should expect to invest between 24 and 28 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 across weeks, you should allow an extra hour or two in your schedule to accommodate week to week variations.

Attendance and absences

At Cal Poly, regular attendance is essential for success in the course and required per University policy. For an asynchronous course, “attendance” means engaging with posted material in a timely manner and maintaining access to online resources and communications to a reasonable extent throughout the week. Extended offline periods should be excusable, and due to the accelerated schedule for the course, 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 – not only is this not in keeping with academic integrity policy, but exchanging solutions deprives you and/or your fellow students of learning opportunities.

Communication and email

I encourage you to use Piazza and office hours as a primary means of communication for the course; office hours are in real time and I check Piazza daily TWR (but not MF or weekends). Email should be used for nontechnical communication – it is not the appropriate medium to ask questions about problems or course content and such questions should be redirected to Piazza or discussed during office hours. Otherwise, I respond to most email within 24 weekday hours, but I cannot guarantee this response time and I occasionally miss messages altogether due to high volume (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.

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, turn in one homework assignment up to one week late. Thereafter, homework assignments turned in up to one week late will be awarded 50% credit unless an extension is granted in advance. 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. If such circumstances arise, please communicate with me in a timely manner to arrange exceptions (i.e., not after the fact at the end of the session).

Assessments and grades

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. Direct submission of AI-generated material is considered plagiarism and will result in loss of credit, OSSR reporting, or course failure depending on the severity and frequency of the offense.