Course Information
Course Number: BIOL 683Course Title: Experimental Design in Biology
Time: TR 8:00–9:15
Location: ILCB 237
Credit Hours: 3
Syllabus - 2025
Instructor
Name: Heath BlackmonOffice: BSBW425
Email: blackmon@tamu.edu
Office Hours: TR 9:30–10:30, or by appointment
Goals of the Course
This course prepares PhD students to be rigorous, fast, and AI-savvy scientists. Students will build skills in R for data management, visualization, hypothesis testing, regression, model selection, and advanced methods such as GLMs, PCA, and Bayesian analysis. At the same time, students will learn how to use generative AI as both a coding partner and as a target of critical evaluation.
- Develop proficiency in R and modern data analysis.
- Work effectively with AI tools while recognizing limitations.
- Critically evaluate statistical models and AI-generated output.
- Communicate results clearly in writing, visuals, and discussion.
- Practice responsible, ethical use of AI in research.
Datasets
Method Choice
Statistical Methods
Statistical Methods - more explanation
Download the Distribution Simulator
Download the T-test Simulator
Course Schedule
| Week | Dates | Topic | Resources/Links |
|---|---|---|---|
| 1 | Aug 25–29 | Orientation & The AI Scientist | slides |
| 2 | Sep 1–5 | Foundations of R | intro R |
| 3 | Sep 8–12 | Data Visualization | slides |
| 4 | Sep 15–19 | Statistical Principles and AI Code Generatrion | slides |
| 5 | Sep 22–26 |
Continuous Variables and Simple Tests 1) read data in 2) visualize 3) t-test 4) permutation test 5) Play with chi-square |
slides notes permutation RMD permutation ex. |
| 6 | Sep 29–Oct 3 | Test Week 1 (AI-Critical Thinking Exam) Turn in an annotated copy of the "midterm answers" file. Document what you would have done differently. Turn in a single PDF file. If you email name your file "lastname-firstname.pdf" |
midterm midterm answers in class example |
| 7 | Oct 6–10 | Regression Analysis | |
| 8 | Oct 13–17 | Generalized Linear & Mixed Models – Part 1 | |
| 9 | Oct 20–24 | Generalized Linear & Mixed Models – Part 2 | |
| 10 | Oct 27–31 | Model Selection | |
| 11 | Nov 3–7 | Dimensional Reduction | |
| 12 | Nov 10–14 | Advanced Topics – Bayesian Methods & Simulation | |
| 13 | Nov 17–21 | Flex Week 1 | |
| 14 | Nov 24–28 | Flex Week 2 | |
| 15 | Dec 1–5 | Test Week 2 (AI vs. Human Judgment) |