Why Science Isn’t About Truth - And That’s Okay

I don’t think science has truth as its objective. Don’t get me wrong, I think science is an amazing tool that yields amazing results, and it works well as it is. But it’s important to understand what science is, and what it isn’t.

For me, science is a framework through which we build models that make the most accurate predictions possible with the least amount of assumptions. These models are based on our current knowledge, and they help us predict how things behave. But we need to be clear: these models are not reality. They are approximations of reality.

Here’s how I see it: a representation is a simplified but accurate model of something, it reflects the structure of reality. An approximation, on the other hand, doesn't have to reflect reality, it just needs to be functional.

So a scientific model can be useful and predictive, without being truthful. It works because the approximation is good enough for the predictions we care about.

Take Ptolemy, for example. He built a model of the solar system assuming the Sun rotates around the Earth. That assumption was wrong. But the model was still functional, it could predict planetary motion fairly accurately. It wasn’t a representation of reality. It was just an approximation that worked well enough.

Today we have similar cases. Dark matter, for example, is not something we have directly observed. It’s a construct, an invention that fills the gap in our current models. When we calculate how galaxies should rotate based on visible matter, the numbers don’t add up. According to our models, the stars at the edges of galaxies should be moving slower or even be flung off into space due to insufficient gravitational pull from visible matter. But in reality, they move faster and stay bound, which our models can't explain without introducing dark matter. Instead of throwing out the model entirely, we add something unseen to make the math work. That’s dark matter. It’s not a discovered thing, it’s a placeholder that makes the model predict better. It doesn’t mean it’s false, but it shows again that science values functional accuracy over truth.

Of course, you can’t make good predictions if you ignore reality entirely. So scientific models tend to align with observable structures. But that alignment is a byproduct, not the goal. Science needs to be close enough to reality to work, but it doesn’t aim to uncover “truth.” It aims to build efficient, predictive approximations.

This isn’t a flaw in science, it’s what makes it powerful. Science can use imperfect or simplified models to generate real results, even when those models don't reflect reality exactly. That’s not a failure — it’s the method. The goal isn’t to describe reality perfectly, but to build models that are accurate enough to make useful predictions. This is what allows science to grow: by gradually replacing one approximation with another that predicts a little better. It’s not about uncovering truth in one leap, but refining function step by step.

Still, it’s a limitation we should be aware of, especially when it comes to the assumptions baked into scientific models. Just because a model makes good predictions doesn’t mean its underlying assumptions are true. The fact that the Ptolemaic model predicted planetary movement didn’t prove that the Sun revolved around the Earth. It only showed that the approximation worked for the limited context it was applied to.

This becomes even more important when we hear statements like “there is no inherent meaning to life,” or “consciousness is just a by-product of brain chemistry.” These are not conclusions of science. They are assumptions made for modelling purposes, assumptions that simplify the system and avoid adding unknowns that would reduce predictive power. In many cases, assuming “there is no meaning” results in a cleaner, more functional model. It’s not because science knows there is no meaning, it’s because including meaning complicates the model in ways that don’t improve prediction.

This is a critical distinction. Science isn’t saying “there is no meaning” as a truth claim, it’s saying, “we’re not including meaning in this model, because it doesn’t help us predict outcomes.” That’s not a discovery, it’s a choice. And it’s important to know when science is operating as a model-builder, rather than offering philosophical conclusions.

So what’s the alternative?

I’d say: philosophy. Philosophy isn’t limited by prediction. It’s not trying to build efficient models, it’s trying to explore the structure of thought, meaning, and being itself. It asks questions science can’t: not how the universe behaves, but why anything should matter at all.

And maybe that’s where we need both: science to navigate the surface, philosophy to ask what's underneath.