How I Have Learned to Sit With Scientific Uncertainty

When I began my academic journey, I assumed science was about accumulating facts that would eventually point to some clear understanding of how the world works. I believed, like many do, that each study added a brick to a larger structure we call “truth.”

But over time, I’ve come to appreciate a more complex view.

In many areas, evidence is often ambiguous, methods rest on assumptions, and interpretation depends on context. The questions we ask, and the tools we use to answer them, shape what we’re able to see.

Pluralism is defined as the view that multiple perspectives, methods, or explanations can each contribute meaningful insights to a phenomenon. In my own work, I’ve found that no single model captures everything. Some scholars use experimental design. Others prefer ethnographic immersion, formal modeling, or archival methods. All have their strengths—and their limitations.

Rather than treating one method as “the” path to insight, I’ve come to rely on what you might call a weighted mental model: I take what I can from each approach and try to integrate these insights in a way that makes sense for the specific problem I’m studying. It’s not always elegant, but it reflects the reality that many phenomena are multifaceted and dynamic.

Truth as Process, Not Endpoint

This doesn’t mean there is no truth. But it does suggest that truth in the social sciences often emerges through approximation—through triangulation across methods, perspectives, and disciplines.

Even strong findings can vary across contexts. A causal mechanism that holds in one setting may operate differently elsewhere. What looks like a robust effect in one dataset may fade in another. This doesn’t invalidate our work—it simply reminds us that most knowledge claims are conditional.

Rigor Still Matters

If anything, this view has deepened my respect for rigor. In complex systems, rigor is not about perfection. It’s about careful design, clarity in logic, and transparency in assumptions. It’s about acknowledging limits while still striving for insight.

The challenge is balancing structure with openness, precision with flexibility.

So Where Does That Leave Me?

Honestly? I still wrestle with doubt. There are moments I wonder whether I’m overcomplicating things—or not pushing hard enough for generalizable results. But I’ve come to believe that uncertainty isn’t a weakness in science.

It’s part of what makes this work so important.