If there’s one constant in the life of a researcher, it’s this: things rarely go exactly as planned. Experiments fail. Hypotheses break. Data surprise us in all the wrong or the most enlightening ways. But let’s face it, it’s not about always being right. It’s about being willing to learn when you’re wrong. Shifting hypotheses isn’t weakness but strength. 

In many ways, scientific thinking mirrors how artificial intelligence systems learn. An AI doesn’t get insulted when it’s wrong. It doesn’t double down on bad predictions. Instead, it evaluates its performance, updates its internal model, and tries again. That process of „iterate, correct, improve“ is, in my view, also what science demands of us. Stay curious, adaptable, and ready to learn. When the data disagree with us, it’s not a personal attack. It’s an opportunity.

So, the next time an experiment crashes, a reviewer challenges your assumptions, or a student/colleague points out a gap in your logic:

Pause.

Breathe.

And remind yourself that this is not a failure. This is the process working as intended. We are not just allowed to learn, we are supposed to. So change your mind. Improve your model. And enjoy the science.