"In theory, there is no difference between theory and practice. In practice, there is." — Yogi Berra

I was an experimental physicist. The thing I loved most about it was how empiricism drove the daily information process. Your job is to listen to the data with tools. This empiricism never disabused me of the theoretical framework. Instead, it told me where to look and minimized the phase space.

A good balance between theory and experiment is hard to achieve. Theory gives you the broad strokes to get in the neighborhood. It provides guidance, constraints, and a sense of what's possible/unlikely. Experiment gives you the address: the nuances and texture that only direct observation reveals. The trouble is that most people lean into one mode and don't realize they've abandoned the other.

Browse any self-help aisle at Barnes & Noble (I have probably read at least 50 books from that shelf!) and you will see how easy it is to theorize improvement. The minute you close the book, it is abundantly clear how hard it is to implement, despite understanding the framework. It didn't help because theory without experiment is just a story you tell yourself.

The inverse is equally dangerous. Someone succeeds at four firms doing the same thing and calls it a proven playbook, but they never asked how much of it was them and how much was the tailwind. Experience without that question isn't empiricism. It's a sample size of one mistaken for a pattern. Without a theoretical framework for why something worked, you can't tell the difference between a useful playbook and exogenous factors.

In the space between is where alpha lives, in both investing (The Most Important Thing, Chapter 2) and in life. Theory says markets are efficient. Empiricism says not all conditions hold for efficiency to be true. The alpha is in being able to operate in both modes simultaneously, which is hard because your brain wants to pick a side.

You have a conviction on a name and the data starts contradicting it, just at the edges. The Bayesian move is to update the thesis. The default move is to explain away the data: noisy quarter, temporary headwind, the market hasn't caught up yet. You protect the theory, and the experiment collects its tuition whether you learned the lesson or not.

The people I've seen consistently outperform in trading, in research, in building things aren't the ones with the best theory or the most data. They're the ones who hold both loosely enough to let one inform the other. They treat every experiment not as a verdict on the theory, but as a refinement of it. This requires living in a permanent state of "I might be wrong".