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| The Atomium in Brussels, Belgium |
We awaken in this world, and try to make sense of it. We see this and that, and think we have figured it out. But the human mind is easy prey to error and bias. So we build up a repertoire of tools to obtain valid data and conclusions, minimizing the window for human interpretation. Science is, in essence, no more than this.
I grew up young-earth Creationist, believing that the Earth and the Universe were around 6000 years old, as you get by counting the begats in the Bible. I believed that biological evolution and the Big Bang theory, along with plenty of other mishmash that were lumped in together as “historical science,” were ad hoc pieces of a disjointed and convoluted attempt to explain the world without allowing for the possibility of a God. When I got to college, I chose to major in physics, thinking about black holes and wave functions, things I was taught were “observational science.”
I learned the concepts of physics, and as I went, they made sense to me. I learned of the Doppler shift, the difference in the pitch of the sound a car makes when it comes toward you or goes away from you. It is due to the peaks of the sound waves being produced at different places, and arriving at your ear stretched or compressed. I learned that the same is true for light, that something moving away from you will be “redshifted” as its wavelength arrives stretched out, and something moving toward you will be “blueshifted,” as its wavelength arrives compressed. Sure, this made sense. I could see it in my head, and could do the calculations. I learned about how we measure distances in space by using “standard candles,” phenomena that always put out the same absolute brightness. By observing how bright a type 1A supernova looks in some galaxy, we can calculate how far away that galaxy is. Then I learned about how Edwin Hubble discovered the universe was expanding. He looked at a number of galaxies and plotted their distance against their redshift, and found that the farther away a galaxy was, the faster it was moving away from us. Made sense to me.
But then the professor did something that blind-sided me like the twist in a Brandon Sanderson novel: he took the slope of the line on the plot, Hubble’s Constant, and inverted it so it had units of time. The value was 20 billion years, and it was the time in the past that every galaxy would have been at the same place. This was the first hint of the model that would later be known as the big bang. My teachers did not try to dogmatically shove it down my throat, but presented a series of logical steps, each simple enough in its own right, the same way they had taught me everything else. Of course this alone isn’t proof of the big bang theory’s validity, but I was forever changed by the realization that “observational science” and “historical science” are exactly the same thing.
In my journey since then, my respect for science has grown into nothing short of awe. Not just for what it has revealed about life, the world, and the universe, but by the methods used and the historical struggle to find new and better ways to study things. In the rest of this discussion, I am going to describe the various philosophical theories of science, and then get nitty and gritty with some of the definitions and tools science uses to uncover the picture of reality in insanely fine detail.
In my “What is Not Science?” post, I mentioned four philosophical theories of science, which I learned about from a YouTube lecture course from the University of Hannover, Germany. I wanted to get onto the rest of the discussion, so I didn’t take the time to explain them. But now I have all the time in the world, so here we go.
Inductivism:
The first theory of science suggests that if we make an observation enough times, we can inductively conclude that it is always true. If we measure objects falling at 9.8 m/s2 enough times, we can conclude that objects will always fall at that acceleration. If every swan we observe is white, we can conclude that all swans are white. Yet Inductivism has a major problem: all it takes is one counterexample to prove an induced conclusion false. All it takes is the discovery of one Australian black swan to show that not all swans are white.
Deductivism:
To remedy the problem of induction, we might turn to Deductivism, which says we start with something we know is true, and follow the logic to predict a conclusion. For example, if Newton’s Law of Gravity is true, then we can predict what the strength and direction of the gravitational field will be at any point in space. But how do we know Newton’s Law of Gravity is accurate to reality? In order to deduce anything, we have to know something to begin with. We could test it with experiments, but how will we know when we are done? Does the result change if I pour a cup of coffee before doing the experiment? What about two cups? If we don’t test every possible variation, we’re left back at Inductivism. Deductivism works well in mathematics, where it is acceptable to define axioms into existence, but the truths of reality are already there, and we cannot deduce them if we have no facts to build from.
Paradigm Theory:
How do we avoid the assumptions of induction, yet have a foundation for deduction? One possibility lies in paradigms. A paradigm is a model that describes something, considered the common knowledge of the day, or a consensus among experts. For instance, Europe went through a paradigm shift 500 years ago from Ptolemy’s model of the heavens with the Earth at the center to Copernicus’s model with the sun at the center.
Paradigm Theory says that science starts from a blank slate. There is a period of exploratory experimentation, from which scientists extrapolate a model via induction. They then agree to take the model as the scientific paradigm, and conduct deductive research assuming it is true. They continue to take the paradigm for granted until too many problems accumulate and the model gets too convoluted, and the field goes back into an exploratory phase, where a new model which can better fit all the data, old and new, is sought. A new paradigm is adopted, and the cycle continues.
Yet Paradigm Theory has its own share of problems. Science can only have revolutions when the current paradigm is challenged, which will not happen very often if the paradigm is merely accepted. In practice, the theory would lead to scientific stagnation.
Systematicity Theory:
Sometimes scientists use induction, sometimes they use deduction, and sometimes adopt a paradigm. Sometimes they do something else entirely. It all depends on what works at the moment. But this begs the question; what is the standard by which we determine the best course of action? Well remember the goal of science: to understand reality to the best of our abilities. The standard is determined by what course of action is the most systematic, that is, what is the best way to gather accurate, relevant information and organize it into a model, which leads to technology and more questions.
For example, Einstein’s General Theory of Relativity is one of the most robust theories in science, standing strong after 100 years of refining fire. Even now new tests are being proposed, with new telescopes and gravitational wave observatories. There are also a collection of alternative theories people work on, on the slim chance that one of them will turn out to more accurately reflect reality than General Relativity.
This may not seem like a very satisfying answer, because it does not give us a simple rule as to how this should be done, but only tells us that it should be done. You may have heard of something called “the Scientific Method,” and expect this is what I am building toward. But the truth is that there is no single scientific method, rather a collection of tools that get updated and improved all the time. What we call science today is the culmination of thousands of years of philosophy, and it is reaching new heights faster today than ever before. So what are those tools? What have our centuries of thought and improvement given us? Here are a few.
Precision:
One of the first things students learn in school science lab is significant figures. No measurable value will be a rational number; it will have an infinite number of decimal places. When you take a measurement, you must know how precise your tool is and where to round. When you make calculations, you have to know which decimal places to keep and which to throw away. Done properly, you get good data. Done improperly, your rounding errors can add up and give you a completely different result. College science students often complain that their online homework programs are too picky, but the students would have no problems if they carried their significant figures properly and avoided rounding errors.
Uncertainty:
Because real values have an infinite number of decimal places, but we can only measure them with some finite precision, we need a way to show the maximum amount our measurement might be off. You might see a number reported as 1.348 ± .0024. The ± .0024 is called the uncertainty, and it is taken from how precise the measuring tool is. For instance, on a ruler that goes to millimeters, the uncertainty would be about 0.7 of a millimeter, perhaps less if you are well-trained at estimating between tick marks.
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| The lines extending upward and downward denote each data points uncertainty. There are a range of possible fits, but the line definitely slopes upward. |
In everyday speech, uncertainty is synonymous with doubt. Saying you are uncertain is like saying you are insecure or worried your conclusion is not true. This leads to a lot of confusion when scientists talk about uncertainty in measurements. The classic example is human-caused climate change. Naysayers will claim that scientists are “uncertain,” and take that to mean they don’t know what they are talking about. But this is scientific uncertainty, the quantifiable spread of each data point. That spread is minimal enough that, though there is wiggle room in exactly how much we are affecting the global climate or exactly what the long-term effects will be, there is no question that we are affecting it, and the average global temperature is rising much faster than usual.
Statistics:
Believe it or not, there is a whole branch of mathematics committed to determining the quality of data, and comparing models to see which fits the data better. New and better methods are being developed every year. I wish I could explain it—this part of the discussion feels incomplete without any of the technical details—but unfortunately I don’t understand it well enough. If you want to do some research on your own, you can look up “5 sigma significance,” “p-value,” or “Bayesian inference,” and see where that takes you.
Computerization:
Back in the day, every calculation had to be done by hand. But now, we have machines to do them for us. In fact, computing technology is so fast today that university supercomputers can simulate the evolution of the universe from an early stage of dust particles to the present-day cobweb-looking galactic supercluster structure of filaments and voids. Computers can save tremendous amounts of time and eliminate human errors, provided the right parameters are entered. Today, we can do things that the scientists of past centuries would never have dreamed of being able to do.
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| Every dot in this picture represents a cluster of galaxies. |
You could be the smartest person alive, and make breakthroughs in all kinds of fields from modern physics to cell biology to statistics to computer science, and yet be blind to some lines of evidence leading in a slightly different direction. We all have biases; it’s part of being human. You can train your mind in logic and reason, and use all of the techniques I’ve talked about, but even all that cannot get you to perfect objectivity. The best way to overcome this is to have other people around who understand your field, and do their best to poke holes in your work from all different angles. This is the peer review process. Whenever someone tries to publish a paper, there is a committee of experts who put it under the knife and make absolutely sure that their work is up to scientific standards. After a paper is published, it is open for criticism by anyone on planet Earth, including every scientist who is working in a similar field. Once the fiery rigors of the review process are over, the ideas that most reflect reality stand tall while the garbage vanishes.
Sometimes people ask why science refuses to study this or that, suggesting scientists might have some kind of conspiratorial agenda. But there are two possibilities that such ideas almost always fall into. Perhaps, like consciousness, scientists don’t yet know how to study it. Or perhaps, like extra-sensory perception, the idea has already been tested and discredited. In any case, the only agenda scientists have is to adhere to the high standards of making sure an idea accurately reflects the relevant data, follows the logic, and addresses any and all assumptions made.
Challenging the norm:
Science is all about following trails of evidence to uncover truth about reality, whatever that may turn out to be. This means that, no matter how well established an idea is, no matter how well a model has held up to experiment so far, it is not beyond question. If an alternative meets all the standards of the time, papers get published. I laugh whenever someone talks about scientists being biased or close-minded, caught in unbreakable tradition. Overturning tradition is what science is all about. The new idea just has to fit into the puzzle better than the old.
Science is not just a collection of knowledge, but an ever-improving set of methods of understanding. The scientific landscape is riddled with checks and balances to further completeness and minimize the influence of human imperfection. I once spoke to a man who thought, since scientists change their minds, that they are unreliable and do not know what they are talking about. “I always take what scientists say with a grain of salt,” he said. I have always wondered who he does trust, if not the people who study things for a living. Science is all about research, and research introduces new information that you have to take into account along with the old. As the economist Paul Samuelson said, “When my information changes, I alter my conclusions. What do you do?” Change is absolutely central to scientific thought, and though it may appear to flit this way and that, in the long term we are brought closer to the truth.
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