The Scientific Method
by Donald E. SimanekToo often the "scientific method" is presented in schools and textbooks as a "recipe" for doing science, with numbered steps even! That's misleading. At the other extreme, someone said that scientific method is "Doing one's damndest with one's mind." I know many have said more profound things about this subject than I will offer, but here's some informal comments about scientific method presented as a set of practical and general guidlines for doing science. Scientists have learned these through trial and error during the entire history of science.
The Methods of Science
Even casual observation shows us that nature, as perceived by our senses, has reliable regularities and patterns of behavior. The use of measuring instruments and scientific apparatus confirms this and reveals even more, and even more detailed, patterns in nature. Through systematic and careful study scientists found that these regularities can be modeled, often as mathematical models of great precision.
Sometimes these models break down when extended (extrapolated). Extrapolation is the process of extending a model or law beyond its known range of applicability. Sometimes extrapolation of a law or model to new situations actually works, but sometimes it fails miserably. This tells us that we had better rigorously test each model for validity, in a wide range of situations, and these tests should be capable of exposing any flaws in the model. That is, they should be capable of demonstrating that the model isn't completely true.
Even when a model survives such testing we should only grant it "provisional" acceptance. In the future, cleverer people with more sophisticated measuring techniques and a more advanced scientific conceptual framework may expose deficiencies of the model that we didn't notice.
When models are discovered to be incomplete or deficient, we often fix them by tweaking the model's details till it works. But when rapid advances in experimental observations occur, a model may be found so seriously inadequate to accommodate the new data that we may scrap a large part of it and start over with a new model. Relativity and Quantum Mechanics are examples. These situations are often called "scientific revolutions."
When such upheavals occur, and old models are replaced with new ones, that doesn't mean the old ones were "wrong". They still work within their original scope of applicability. Newton's physics wasn't suddenly wrong, nor were its predictions found unreliable or incorrect, when we adopted Einstein's relativity. Relativity had greater scope of applicability than Newtonian physics. But it also rested on a different conceptual basis.
When a new model does include significantly new concepts this can provide a tremendous stimulus for further advances. It shakes up our comfortable habits of thought, forcing us to think about nature in new ways. Again, this does not mean the concepts of the old model were wrong, or worse than those of the new one. It just means that the new model's concepts may be more productive and adaptable for further development. But there's always another side to every coin. The new concepts, seemingly "better" may in fact seductively lure us down a dead-end path. Such was the case with the concept of the luminiferous ether. These mistakes are swept under the rug of history when they are replaced.
Past experience has shown that mathematical models of nature have tremendous advantages over the earlier, more appealing, models that used analogies with familiar everyday phenomena of our direct sensory experience. Mathematical models are less burdened with emotional baggage, being more abstract. Also, mathematics is apparently infinitely adaptable and flexible. If a some natural phenomena doesn't yield to known mathematics, we can invent new forms of mathematics to deal with it.
The history of science has been a process of finding successful descriptive models of nature. First we found the easy ones. As science progressed, scientists were forced to tackle the more subtle and difficult problems. So powerful are our models by now that we often delude ourselves into thinking that we are very clever to have been able to figure out how nature "really" works. We may even imagine that we have achieved "understanding". But on sober reflection we realize that we have simply devised a more sophisticated and detailed description.
Whatever models or theories we use, they usually include some details or concepts that do not relate directly to observed or measurable aspects of nature. If a theory is successful we may suppose that its details are matched in nature, and are "real", even when they are not directly verifiable experimentally. Their "reality" is assumed by some people (and most students) to be demonstrated by the fact that the theory "works" to predict things that we can verify and continue to verify in the future. This is not necessarily so. So scientists often speak of energy, momentum, wave functions and force fields as if they had the same status as objects of everyday experience such as rocks, trees and water. In a practical sense (for getting answers) this conflation of concepts from direct sensory experience and invented concepts may not matter. But on another level, a change of scientific model may do away with a force field as an conceptual entity, but it wouldn't do away with a forest, mountain or lake.
The notion that we can find absolute and final truths is naive. If there are any underlying "truths" of nature, our models are just close approximations to them—useful descriptions which "work" by correctly predicting nature's behavior. We are not in a position to answer the philosophical question "Are there any absolute truths?". We can't even determine whether there is an underlying "reality" to be discovered. And, though our laws and models (theories) become better and better, we have no reason to expect they will ever be perfected. So we have no justification for absolute faith or belief in any of them. They may be replaced someday by something quite different in appearance and with different underlying concepts. At least they will be modified. But that won't make the old models "untrue", for the old models will work as well as they always did. All of these reservations and qualifications about truth, reality, and belief, don't matter in a practical sense—such philosophical quibbles aren't relevant to doing science. We can do science quite well without 'answering' these questions—questions that may not even have answers. Science limits itself to more finite questions for which we can arrive at practical answers.
We've learned that not all questions we can ask have answers we can find. Any question that is in principle or in practice untestable is not considered a valid scientific question. We like to think we don't waste time on those, but they seem to pop up in internet and classroom discussions quite often. Many people think unanswerable questions are the most profound and important ones. Questions such as "What is the meaning of it all," "What lies outside the universe," or "What jump-started the universe?" Scientists should set these aside for the philosophers to chew on, and get on with the business of answering more accessible questions.
Science progresses through trial and error, mostly error. Every new theory or law must be skeptically and rigorously tested before acceptance. Most fail, and are swept under the rug, even before publication. Others, like the luminiferous ether, flourish for a while, then their inadequacies accumulate till they are intolerable, and they are quietly abandoned when something better comes along. Such mistakes will be found out. There's always someone who will delight in exposing them. Science progresses by making mistakes, correcting the mistakes, then moving on to other matters. If we stopped making mistakes, scientific progress would stop.
This document, written January 2000 and edited April 26, 2000, is
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