Is Science the Answer to Life, the Universe, and Everything? - Part I

Exploring the intricate world of scientific theory and practice.

Photo by Greg Rakozy on Unsplash

When my second daughter was 45 days old, she got a common respiratory virus. Within hours, she got admitted to the UCI, and we spent the scariest fifteen days of our lives watching her fight for hers. Fortunately, she got the best treatment modern medicine can give and the most incredible team of caring, tireless doctors and nurses. She was saved by the doctors, but she is only alive because of modern science.

This is not an isolated story. Most of us alive today would be dead without science. Before the scientific revolution, the odds of living beyond 40 years were abysmal. The leading cause of death in the middle ages was curable diseases like flu, diarrhea, and mild infections, which ravaged populations with little understanding of cause or prevention. Raising one of your children beyond 10 was a coin toss, very biased towards the negative. Let's just say the bets weren't on your side.

The scientific method has enabled many of the most impactful discoveries in the last few centuries—in terms of improving human life—including antibiotics, vaccines, electricity, surgery, brain scans, cars, trains, airplanes, the internet, … you name it. These advancements have transformed humanity, extending longevity and improving quality of life, both physically and intellectually.

Science has allowed us to explore the mysteries of the universe, from the smallest particles to the vast expanse of space. It has expanded our understanding of the natural world to the point we can now summon and control the primal forces that make and break atoms. Science has raised us talking monkeys to heights our hairy ancestors would consider godlike.

Many, including myself, believe that the scientific method is the most important discovery in mankind's history, and the collective body of knowledge it has allowed us to build is our most valuable treasure. Still, some of the most important questions we have are forever outside the realm of what science can answer. And that's a feature, not a bug.

In this article and subsequent articles, I will explore how modern science works, why it is so effective, and its intrinsic limitations, both conceptually and pragmatically. In the end, I aim to convince you that science is our best tool for understanding the natural world, but we still need other tools to have the broadest possible view of the human condition.

For this, we will analyze the nature of knowledge to understand the types of questions we may want to ask about the universe. Then, we'll review how science works at different levels of organization, from the process of making experiments and finding new discoveries to the complexities of publishing research and the biases involved in the industrialization of science. Finally, we'll criticize the scientific method and argue there are some fundamental questions it can't answer. Then, we'll briefly look at alternative epistemic systems, which we may also need to encompass in a fuller understanding of reality.

What is knowledge?

Knowledge is a tricky thing. We all intuitively know what we mean when we say we “know” something, right? But we know all sorts of stuff for different reasons. For example, I know 2+2=4 because that’s basically the definition of those symbols. On the other hand, I know water boils at around 100 C because I’ve seen it happen over and over. I also know that electrons are negatively charged, that the speed of light is constant in a vacuum, and more or less how DNA works, but I have no direct experience with any of those things.

At the most abstract level, the study of knowledge is the job of a field of philosophy called epistemology. A complete introduction to epistemology is far beyond the scope of this article and well above my pay grade, but we can try a very brief overview.

The most commonly accepted definition of knowledge —not without its own problems— is that knowledge is justified true belief. Let’s unpack that.

Obviously, to truly claim you know something, you have to believe it. I mean, you can definitely claim you know something is true even if you believe it is false—you can give a false testament in a trial, but you would be lying. On the other hand, if you believe in something that is objectively false—like the Earth is flat—then you are simply mistaken, no matter how strongly you believe it. You can believe that you know, but you don’t truly know, because you’re wrong. So far so good? Now comes the tricky part.

Say you buy a lottery ticket, and you believe with all your heart that you will win. Then, it so happens that you win the lottery. So you had a true belief. Would you say you knew you were going to win? Most rational people would claim you didn’t actually know. You just got lucky. Whatever reason you had to support that belief—maybe the ticket had your preferred numbers in some specific pattern, or maybe that day you were dressed with your favourite underwear—, that reason is unjustified. The lottery is absolutely random, so you couldn’t know you would win.

Similarly, you might believe you know the solution to a complex math problem, but if you made subtle errors that happened to cancel each other out, you would arrive at the correct answer for the wrong reasons. In this scenario, most rational people would argue that you don't truly know the answer, even if you happen to stumble upon it.

So, justified true belief. The question then becomes, how do we attain knowledge? That is, how can we ensure we are justified in believing that something is indeed true? This is what epistemic systems are for.

Epistemic systems

In short, an epistemic system is a set of rules that determine when a given claim is true. Different epistemic systems assert the truth value of claims with different strategies. For example, you can think of mathematics as an epistemic system that asserts all claims that can be logically deduced—using a very narrow set of inference rules—from a given set of non-contradictory axioms.

This is an example of a formal epistemic system, where the truth or falsity of a given claim is decided only by symbolic manipulation. There is no need to look at the real world. Triangles are abstract things, and we can know everything there is to know about them by thinking really hard. There are no triangles out there in the woods.

Another possible epistemic system is what I call truth-by-authority. This system asserts all claims that some predefined entity—e.g., your mother—decides, regardless of… well, anything. Jokes apart, many religions fall into this broad category, choosing as authorities either ancient scriptures of dubious origins or self-appointed messiahs.

Not all epistemic systems are equally trustworthy in every domain. For instance, I don't rely on my mother's authority for most issues—though there are some cases where she holds the absolute truth—but I trust mathematics in all relevant situations. The critical question is this: How do we determine the applicability of an epistemic system to specific types of claims? What questions can we justifiably believe based on a given system?

You can probably see where I’m going with this. To understand when science is justifiable as an epistemic system, we need to understand what types of claims can be answered with the scientific method. For the purpose of this article, I want to divide claims into three categories, one of which will be the prime domain of scientific inquiry.

First, let’s consider the questions that can be answered via pure reasoning. Questions like “how many prime numbers are there?” or "what is the fastest way to sort these numbers?" fall into this category. These questions can be resolved through mathematical processes and logical deductions without empirical observation. These aren't necessarily easy questions, though. Rather, some of the hardest questions in math and computer science are in this set.

Next, we have claims that rely on empirical evidence, such as “what is the boiling point of water?” or “is the light next room turned on?” These require observational methods and experimentation. You simply cannot reason your way into knowing the precise temperature at which water molecules have enough kinetic energy to escape from each other’s electromagnetic attraction. You have to go out there and measure it, just as you have to open that door to see if the light is turned on. This is the domain of science.

Finally, the third category encompasses questions that can’t be answered either way. These include subjective or normative claims, such as “what is the best ice cream flavor?” and “is there an objective moral system?” which may not be effectively addressed by either logical reasoning or observation alone. This is the domain of philosophy, arts, and spirituality. Some of the most important questions in life, including the meaning of all this absurd existence, are very likely in this realm.

How does science work?

Now that we intuitively understand what types of questions we want to answer with science let’s try to explain how science works from the ground up.

In short, science is a systematic approach to investigating the natural world. It relies on formulating hypotheses based on observed phenomena and testing them through experiments. The results of these experiments either support or refute the initial ideas. This process is iterative, meaning each outcome may lead to new questions and hypotheses, continually refining our understanding of reality.

Crucially, science can almost never give a definite answer. All we can do is get an increasingly more accurate approximation of the true nature of reality. How close we can get is unclear. Some think the universe is fully understandable, and others claim there is some fundamental level of incomprehensibility we may never surpass. But, more importantly, this very question—the limits of science—is, for the most part, outside the realm of the types of questions that science can answer.

But let’s see how it works.

Science as an Epistemic System

At the lowest level, the scientific method is an epistemic system that attempts to assert the truth or falsity of objective claims about the natural world. By objective, I mean claims whose truth value is independent of the observer, so whether chocolate is the best flavor of ice cream, despite being a claim about a very natural thing, is outside the interest and reach of science because its truth value will depend on the observer. Most people would say yes, but some sociopaths might prefer vanilla.

How do we justify beliefs about the natural world? The most obvious answer is to verify them through direct experience. If I claim the light in the next room is on, you just need to open the door and check, right? This is the paradigm of verifiability, formally put forward in the early 20th century by a school of thought called Logical Positivism.

However, this approach has two problems. The easier one is with claims that cannot be directly observed or measured but still have measurable indirect effects. For example, suppose I claim the Moon exerts a small gravitational force over you. In that case, it is hard to conceive of an easily verifiable experiment that can readily convince you this is true. Almost everything we want science to talk about is outside of our direct, shared, macroscopic experience.

The harder problem, though, is much more fundamental: it is about universal claims. For example, if I say all electrons are negatively charged, there is no way to verify that claim. Even if you could directly measure the electric charge of any particular electron, you would still not be able to prove that all electrons in the universe, past, present, and future, have the same properties.

So, instead of verifiability, which seems to impose an absurdly high constraint on the types of claims—about the natural world—we can study, we want a more flexible paradigm that gives us, perhaps not absolute certainty, but as close as pragmatically possible.

This is what Karl Popper proposed with his concept of falsifiability. Rather than requiring claims to be provable, he suggested that scientific theories must be testable and, importantly, falsifiable. This means that in order for a statement to be considered scientific, there must be a possibility of it being shown false through observation or experiment.

According to this view, a theory is scientific if it can, in principle, be tested and potentially refuted by evidence. While no amount of positive evidence can convince us a theory is definitely true, if we test it long and hard enough and never find falsifying evidence, then we have to grant that, as far as evidence goes, this theory is the current best explanation for a given phenomenon. It becomes an accepted scientific consensus.

Shifting from verifiability to falsifiability also shifts the burden of proof from the person who makes the claim to the person trying to falsify it. At face value, this seems like a bad move, right? Now, whenever I make a claim, I don’t have to give you a way to verify it. Instead, it becomes your responsibility to demonstrate that my claim is false.

So what happens if my claim is impossible to falsify? I claim there is an invisible purple unicorn in my garage, undetectable by all instruments and producing no perceivable effect in the environment, yet it is there, watching over you and judging every decision you make. This claim is impossible to prove false, by definition. What can we do about it?

This is the problem of demarcation in the philosophy of science, which basically determines which claims are considered “scientific” to begin with. Simply put, if your claim is untestable or unfalsifiable, the scientific community won’t even pay attention to it. We simply ignore it. It is not in the interest of science to talk about magical, undetectable unicorns, right?

But it sounds easier than it really is because sometimes determining what counts as a possible falsification procedure becomes a heated debate. For example, some serious scientists consider string theory unscientific because, so far, it hasn’t produced any falsifiable predictions. Other prominent scientists instead claim we simply need more sophisticated epistemic principles.

However, barring these extreme cases, for the most part, it is often evident when a simple enough hypothesis is a scientific claim. Your hypothesis needs to make some testable predictions that can be verified. As long as you keep making predictions that are proven right, the trust in that hypothesis grows until it eventually becomes a mainstream confirmed theory. But the minute a prediction fails, your hypothesis is immediately rendered false, right?

Well, not necessarily.

Science as an Iterative Approximation of Truth

One of the most famous scientific theories of all time is classical (e.g., Newton’s) mechanics. It was the dominant explanation for most of the physical world for a long time. It is also one of the most beautiful theories in the mathematical sense, having but a few simple and coherent formulas that unify everything from how apples fall from trees to how the Moon rotates around the Earth.

We lived in the dark ages, and Newton gave us the light. Then Einstein came and made everything dark again.

Around the beginning of the 20th century, a bulk of evidence was piling up showing that something fishy was going on with Newton. The most important: the orbit of Mercury wasn’t precisely as predicted by classical mechanics. However, this had happened before.

The discovery of Uranus in 1781, the first new planet to be discovered since ancient times, was initially celebrated as a triumph of Newtonian mechanics. However, as astronomers observed Uranus's orbit, they noted irregularities that could not be reconciled with Newton's predictions. What is more likely—astronomers of the time asked themselves—that almighty Newton, who has worked perfectly so far, is actually wrong, or that we are missing something here?

This led to the hypothesis that another massive body was exerting gravitational influence on Uranus. Mathematicians Urbain Le Verrier and John Couch Adams calculated where such a planet should be based on the observed perturbations in Uranus's orbit. Their predictions were remarkably accurate, leading to the discovery of Neptune in 1846, which was found within one degree of the predicted position. So, Newton was right all along! The theory predicted that something was missing, and we found it.

Mercury's orbit was a different story, though. The planet's perihelion—the point in its orbit closest to the Sun—was observed to shift over time, a phenomenon known as perihelion precession. According to Newton's laws, this precession should be minimal and consistent; however, observations indicated a tiny discrepancy that no other explanation—like a missing planet—could account for. Something was seemingly wrong with Newton.

Then Einstein came along and presented a much more complex, precise theory that accounted perfectly for all the discrepancies. So, Newton was wrong all along! Right?

Well, the story doesn’t end there, either. In the early 20th century, more precise observations of Neptune revealed that its orbit also exhibited deviations from Newtonian predictions. Again, astronomers said there must be another planet out there, and indeed, they pointed their telescopes and found Pluto in 1930. However, subsequent studies revealed that Pluto's mass was insufficient to account for the observed perturbations in Neptune's orbit. So astronomers thought it must be yet another planet. They called it “Planet X”.

However, the search for planet X was stagnant. All observations of the night sky where planet X should have been failed to show anything as obvious as a missing planet. Then, astronomers went back to the original data that showed Neptune’s irregularities. They discovered one of the key observations was performed after some telescope maintenance, which might have introduced calibration errors. After removing those few noisy data points, the resulting data perfectly matched Newton’s predictions again—no new planet or fancy new theory was necessary.

So, is classical mechanics right or wrong? If we are being absolute, they are indeed wrong. They fail to predict how matter moves close to the speed of light and near supermassive objects like stars and black holes, yes. But, for most practical matters—hell, even for sending astronauts to the Moon—we can still rely on old Papa Newton. We still teach classical mechanics as scientific facts in high school, and that’s because, for 99% of the problems we’ll ever have, it really works!

How can this be? Aren’t falsified theories wrong? Shouldn’t we be done with Newton once and for all, especially since we have a more powerful theory that does all the previous one did, and more?

Well, the answer is that it depends. Some hypotheses are falsified with evidence showing they are way off, totally false, and useless. However, other hypotheses might just be approximations of the true nature of reality, and pretty good approximations if you are congnizant of their limitations. This is the case with classical physics. It isn’t right or wrong. It’s an approximate model of actual physics, and if you use it within reasonable—and well-known—constraints, it really works.

For all we know, general relativity is just another, more precise approximation of reality, but it might also break at some point. Right now, for example, no one knows how to unify general relativity—the physics of the large—with quantum mechanics—the physics of the very small—and it might be the case that these two theories are simply two approximations that break when you stretch them bad enough.

So, truth in science is always approximate. We seek not necessarily the ultimate truth but a better approximation of it, which accounts for all the phenomena our current instruments, math, and intelligence can comprehend.

Moving on

Science is still much more complex than what the previous story shows. Beyond the pure, rational search for truth and knowledge, other forces at play make science a complicated social phenomenon, where the winning theory isn’t always the one that most closely matches reality. And then there are economic incentives, careers at stake, institutions, governments… It’s a huge mess. But this article is already long enough, so I will leave the rest of this story for future entries.

Until then, stay curious.