On April 8 I had the pleasure of speaking on a webinar hosted by Maze, alongside the
amazing Brooke Skye, Daniel Soranzo, and Kate Pazoles. We received a ton of thoughtful questions from the audience before and during the session, but we had such a productive discussion that we were only able to get to a handful of them. So I took the liberty of writing up the rest as this FAQ.
Each of these questions could easily be its own blog post, so in the interest of keeping this readable, I took a bit of a lightning round approach — as if I were answering these live on the webinar — intentionally keeping things short and focused on the key points.
Some of the original question phrasing wasn’t entirely clear, so I made some assumptions about intent. If I missed the mark on yours, please reach out. I also left out questions that were too vague or highly dependent on your specific organizational context to answer responsibly. If you don’t see your question here, reach out and I’ll add it to the list.
You can catch up the webinar recording if you missed it.
Here goes.
Career Path & Skill Development
Specialization, transitions, demand, and hybrid roles
What research area should I specialize in?
Assuming here this question is asking about quant vs. qual. If not, whoever asked this — please correct me!
Quantitative research is all about large sample study designs — surveys, structured data, statistical rigour. By design, quant research is good at answering “what” and “how many.” If you’re the kind of person who finds confidence in large data sets and enjoys the precision of numbers telling the story, quant is going to feel like home.
Instead of “what” and “how many,” qual research is great at answering the “how” and “why.” Qualitative research is often faster, more fluid, and deeply human. You’re having direct conversations with customers, iterating quickly, and working with smaller data sets. It’s more intimate and more adaptable — you can shift your approach between sessions, or even on the fly, based on what you’re learning.
Neither is “better.” They answer different questions in different ways, and often they’re complementary. The question to consider is: what kind of thinking energizes you? If you’re drawn to patterns in large data sets, lean into quant. If you’re drawn to the nuance of human conversation, lean into qual. Many researchers I’ve worked with have at least a working appreciation for both.