Student Methodology Workshop Series
Quantitative Study Group 2026
Student Methodology Workshop Series, Pt. II launches the Quantitative Study Group 2026 — a bi-weekly, student-led initiative designed to strengthen quantitative confidence among ReROW graduate students. This series creates a structured space to clarify common quantitative concepts, tools, and conventions encountered in research, while fostering thoughtful reflection on data, interpretation, and ethics. The series prioritizes depth over breadth, focusing on high-leverage concepts that shape interpretation, ethics, and methodological decisions.
- Feb 13: What are numbers actually doing in research?
- Feb 27: P-values, significance, and misuse
- Mar 13: Data, bias, and “garbage in, garbage out”
- Mar 20: Choosing methods that match your question
- Mar 27: AI, statistics, and “black box” tools
- Apr 10: Intro to coding
Interested in attending a session? email Diana at dianasan@student.ubc.ca or Yiayng at yiyang99@student.ubc.ca
The event is open to ReROW student members interested in quantitative methods and all UBC students
The study group aims to collectively build quantitative confidence among graduate students by clarifying common quantitative concepts, tools, and conventions we regularly encounter in research, through:
- Build a shared understanding of what quantitative methods can and cannot do.
- Build a shared understanding of how to critically read quantitative research in our fields.
- 3Support thoughtful method selection in quantitative and mixed-methods research projects.
- Foster shared ethical awareness around data, measurement, and interpretation.
- When: Bi-weekly (Fridays) between 12:00-1:00 PM.
- Where: MacMillan 80 (in-person)
The workshop will be facilitated by graduate students Diana Sanabria, Civil Engineering and Yiyang Wang, Forestry.
| Date | Topic | Why this matters | Key ideas |
| Feb. 13th | What are numbers actually doing in research? | Many grad students treat quantitative methods as “objective truth machines.” This session reframes numbers as arguments, not answers. | * What makes a study “quantitative”? * Variables, samples, and populations (conceptually) * Correlation vs. causation and why they are often confused |
| Feb. 27th | P-values, significance, and why everyone misuses them | p-values are everywhere, poorly understood, and often abused. | * What a p-value does and does not mean * Statistical vs. practical significance * Why “p < 0.05” became a convention *What p-hacking looks like in real life |
| Mar. 13th | Data, bias, and “garbage in, garbage out” | Urban, environmental, and social datasets often reproduce inequality. | * Who is counted in data and who is not * Proxy variables and their risks * Missing data as a political, not just a technical, issue |
| Mar. 20th | Choosing methods that match your question | Students often pick methods for various motives, not fit. | * Types of research questions: description, comparison, causation, prediction * When quantitative methods are not the right tool * Combining qualitative and quantitative methods without forcing coherence |
| Mar. 27th | AI, statistics, and “black box” tools | Students are already using AI but don’t know its limits. | * How machine learning differs from traditional statistics * Prediction vs. explanation * Why AI can appear confident when wrong |
| Apr. 10th | Intro to coding | Many students feel locked out of quant work because of coding fear. | * What scripts do: automating and documenting analytical decisions * How common tools differ in purpose (not superiority) * Why reproducibility matters |