In research, we often chase after what’s most sophisticated, thinking that if we just layer on more complexity, we’ll finally see the hidden patterns. There’s a constant pull to be “state-of-the-art” or “beyond state-of-the-art.” We hear it everywhere. But when was the last time you read a scientific paper and thought, this is brilliant for its simplicity?

Too often, people lump simple, basic, and unsophisticated together. But they’re not the same. Simple doesn’t mean dumbed-down or lacking sophistication. Basic isn’t always the opposite of advanced, and unsophisticated isn’t a synonym for straightforward.

Sometimes, the most powerful and effective approaches are the ones grounded in the basics.

There’s an allure in complicated methods - shiny, powerful tools that promise to unlock deep insights. But do we fully understand them every time we use them? Do we always need to push for the newest machine learning model to make sense of our data?

Stripping down research to simpler approaches isn’t always easy. It can be the hardest part. In fact, deciding what to leave out, what really matters, and what can be solved with clarity rather than complexity - that’s often where the real sophistication lies. Simplifying research doesn’t mean making it basic; it means cutting through the noise. And that takes work.

Simple approaches - the kind that are clean, clear, and grounded in a strong understanding of the fundamentals - can be incredibly powerful. They strip away the unnecessary, letting you see what’s really there. Simple is not basic, and it’s definitely not unsophisticated. It’s thoughtful. It’s deliberate. It’s about doing less, but achieving more.

-- João