Experimental Design in Biology

Course Information

Course Number: BIOL 683

Course Title: Experimental Design in Biology

Semester: Fall 2026

Time: TR 8:00–9:15 AM

Location: ILCB 237

Credit Hours: 3

Instructor

Name: Heath Blackmon

Office: BSBW 425

Email: blackmon@tamu.edu

Office Hours: TR 9:30–10:30 AM, or by appointment

Course Goals and Philosophy

This course prepares PhD students to be rigorous, fast, and AI-savvy scientists. Students will learn R programming through a "vibe coding" approach-building intuition and working alongside AI tools-while progressing through a carefully sequenced series of datasets that grow in complexity. Each week builds skills in data visualization, hypothesis testing, model building, and critical evaluation of both statistical methods and AI-generated output.

By the end of the course, you will understand when and how to apply t-tests, chi-square tests, linear models, generalized linear models, mixed effects models, dimensional reduction techniques, and Bayesian MCMC approaches-not just as isolated methods, but as part of a cohesive analytical toolkit.

Learning Objectives

Develop practical proficiency in R for data visualization, manipulation, and analysis.
Learn "vibe coding": using AI as a coding partner while understanding the logic behind your analyses.
Progress through increasing complexity in experimental design and statistical methods.
Master hypothesis testing and model selection frameworks.
Apply mixed effects models, dimensional reduction, and Bayesian methods to real biological data.
Critically evaluate statistical output, model assumptions, and AI-generated suggestions.
Communicate results clearly through code, visualizations, and scientific writing.
Practice responsible and ethical use of AI in research.

Course Resources & Tools

Guides & References

Prompting Guide

Best practices for working effectively with AI tools in data analysis and coding.

Statistical Methods Guide

Reference for when to use different statistical approaches.

Statistical Methods: Deep Dive

Detailed explanations and mathematical foundations of key methods.

Method Choice Tool

Interactive guide to help you choose the right statistical test for your data.

Datasets

Course Datasets

Curated biological datasets used throughout the course, organized by topic and complexity.

Interactive Tools

Distribution Simulator

Interactive R app to explore probability distributions and sampling behavior.

T-test Simulator

Interactive app to visualize how t-tests work with different sample sizes and effect sizes.

Course Schedule – Fall 2026

A dataset-focused progression from foundational R and vibe coding through advanced statistical methods.

Week	Dates	Topic	Dataset Focus	Resources
1	Aug 24–30	Orientation & The AI Scientist	Course philosophy, vibe coding intro, AI tools setup	TBD
2	Aug 31–Sep 6	Foundations of R & Vibe Coding	Base R syntax, reading data, using AI as coding partner	TBD
3	Sep 7–13	Data Visualization with Base R	Morphometric Measurements: Plotting, histograms, boxplots	TBD
4	Sep 14–20	Statistical Thinking & Distributions	Ecological Count Data: Probability, distributions, hypothesis framework	TBD
5	Sep 21–27	T-tests & Permutation Tests	Field Experiment Data: Comparing two treatment groups	TBD
6	Sep 28–Oct 4	Test Week 1: AI-Critical Thinking Exam	Synthesizing weeks 1–5; evaluating AI-generated analyses	TBD
7	Oct 5–11	Chi-Square & Tests of Proportions	Categorical Data: Genetic cross ratios or species surveys	TBD
8	Oct 12–18	Linear Models Part 1: ANOVA & Regression	Multi-factor Experimental Data: Comparing multiple groups and continuous predictors	TBD
9	Oct 19–25	Generalized Linear Models & Model Selection	Count/Binary Response Data: GLMs, AIC-based model selection	TBD
10	Oct 26–Nov 1	Mixed Effects Models	Longitudinal or Multi-site Data: Random effects, nested designs	TBD
11	Nov 2–8	Dimensional Reduction	Multivariate Data: PCA, ordination techniques	TBD
12	Nov 9–15	Bayesian Methods & MCMC	Phylogenetic/Evolutionary Parameters: Priors, posteriors, convergence diagnostics	TBD
13	Nov 16–22	Choosing Your Approach: Final Data Discussion	Integrating methods; planning final project analyses	TBD
14	Nov 23–29	Advanced Plotting & Work on Final	Publication-quality visualizations; refining final analyses	No class Thursday (Thanksgiving)
15	Nov 30–Dec 6	Work on Final Project	Completion and presentation prep	TBD

How This Course Works

Vibe Coding Philosophy

"Vibe coding" is about building intuition and confidence in your data analysis skills. Rather than memorizing syntax, you'll learn to understand the logic of statistical approaches and use AI tools (like ChatGPT or Copilot) as your coding partner-asking them for help with implementation while you focus on the conceptual work. You'll learn to critique AI suggestions, catch errors, and understand why certain approaches work better than others.

Dataset Progression

Each week focuses on a specific biological dataset that demonstrates the week's statistical concept. The datasets increase in complexity: starting with simple morphometric measurements for visualization, progressing through experimental comparisons, categorical data, multi-factor designs, and finally to advanced methods like mixed models and Bayesian approaches. By working with real (or realistic) biological data, you'll see how methods apply in practice.

Assessment Structure

Test Week 1 (Week 6): An AI-Critical Thinking Exam where you'll analyze a dataset and an AI-generated analysis, identifying strengths, weaknesses, and what you would do differently.

Final Project: A comprehensive data analysis project where you choose a dataset of interest, apply appropriate statistical methods learned in the course, and present your findings clearly with publication-quality visualizations.