The Science Machine
Richard H. Zander
March 23, 200
Scientists use an orderly process for discovering new things in nature and for developing new ideas that transform the way we see nature and work with the natural world. Basically, scientists build on the work of others by re-examining nature through their own experience in the light of reason. We all do it. We are all scientists to some extent.
THE SCIENCE MACHINE (KID'S VERSION)
Children evaluate and modify information given them by authority (parents, schools, police) to help them get along in the world (don't we all?). They develop new ways of living with the help of others and keep modifying their world views to fit new circumstances.
Example: "You can cross the street when the light is green." Without this authoritative statement, there is no help for dodging in cars at a crossing. Thus, pure discovery by experience can be a problem, and we must build on the work of others. BUT, kids soon learn that some motorists cross the intersection "on the blush." Enough close calls, and the Theory of Crossing the Street is modified to something that fits all of the facts.
Facts are just well documented observations. With facts, we progress from notion, to conjecture, to hypothesis, to theory, and even (if the theory is really, really excellent) to a physical law (like the way gravity acts). We do this by constantly modifying our ways of acting in the world based on observation and reason. We cycle through Theory (first given to us by some authority) to Observation to Modified Theory (when we become the authority ourselves) to New Observations, and so on, figuring out how to cope with life.
THE SCIENCE MACHINE (ADULT'S VERSION)
Adults can deal with a more complicated version of The Science Machine, and can use this to help solve complicated environmental problems, create valuable commercial products, and generally be even more effective in life. The dictionary definition of "science" goes like this:
Science is the gathering, organization and analysis of information about the natural environment, and the inference of new, verifiable theories about the world. The grand cycle goes like this: observation, organization, analysis and synthesis. Once you develop a theory, this suggests new hypotheses (ideas or presently poorly supported theories) and you start over again through the cycle.
Example: A German scientist named Wegener hypothesized that the continents were once all stuck together in one land mass and they drifted apart. Evidence was the fairly nice fit between the coastlines of the New and Old World and some geological data that could be interpreted that way. He was laughed at. Soon, however, the way plant and animal species were distributed in the world was found to be easily explainable by Wegener's hypothesis and new distrilbutions could be accurately predicted with the theory. With additional geological data, Wegener's hypothesis became known as the Theory of Continental Drift. With the discovery of the spreading Atlantic ocean floor shown by the analysis of its magnetism, it was apparent that Wegener was right, there was no simple alternative hypothesis to so easily explain all these data, and we now act as though the theory was a physical law. The Science Machine ground through many cycles to get this far. Recently we found that continental drift happens by means of many drifting tectonic plates. Through re-observation, re-organization, re-analysis and re-synthesis, we constantly refine our scientific theories, sometimes even throwing them out entirely when a new theory explains more data better. But remember, scientists are never sure of any explanations. At any time, a new fact can throw the monkey wrench into the best hypothesis, theory or physical law.
Another example: The English Daisy (Bellis perennis) was unknown in western New York until collected near Niagara Falls in 1888. In 1919 it turned up in Buffalo, and now is often found in Buffalo lawns. A group of elementary school science teachers used this information to develop a hypothesis that it is spreading from the cities into the suburbs. They went on auto trips and found that the English Daisy is spreading along the major highways into the Buffalo suburbs. With this information, one might hypothesize that many European species spread in this same way. Dandelion (Taraxacum officinale) has apparently spread faster than English Daisy, so perhaps the collections in the Buffalo Museum of Science show more exactly how it spread. Alas, few people have collected Dandelion over the years, and we do not have enough collections to show a good progression of spreading from cities. Perhaps we could lump together all the European species now established in western New York and examine their spread over the years as represented in the collections. But what if some species show a spread along major highways and others do not? Can you just ignore the data on species that do not grow when and where you want them to grow? Answer: If you do not have a theory that explains why data can be eliminated, you cannot eliminate the data. Otherwise, you can just juggle data until your hypothesis is well supported. Perhaps you know that some of the species only grow in very wet areas or on limestone? Those you can eliminate because you have a theory and some evidence to back it up. This is how statistical thinking helps refine data.
THE SCIENCE MACHINE AND THE RESEARCH PROGRAM
But using a science machine as a process in your head is not the best way for our large, complicated world society to advance in understanding nature. Scientists have created a process called the "Research Program" that allows fairly efficient coordination of many science machines operating at the same time throughout the world. It is a way to touch all important bases so you can communicate the most important information about your use of the Science Machine.
A Research Program is about the same whether it is part of a grant proposal before any science is done, or part of the publication of a scientific paper (after the main work is accomplished). Basically, these are the parts:
Statement of problem. Why is this problem important enough to study? What are the goals of the scientific work?
Summary of past work on the problem done by others. Exactly what data and theories are we examining?
Materials and methods to be used. How will new data be GATHERED and ORGANIZED? How will it be ANALYZED statistically or by some other rational means?
Results. What exactly was discovered in our work? How were the data determined to be probably due to some newly discovered process in nature and not just generated by some kind of random, chaotic processes?
Discussion. So what? What do you think is the significance to society of your results? How has science been advanced? What new hypotheses were SYNTHESIZED by examination of the results, and what new problems were suggested?
Much has been written on the philosophy and methods of science, for instance, that scientists never say they are absolutely sure of anything, just that they can falsify null hypotheses (look this up in a library or on the Web), or on controlled experiments, or on data collection and analysis. In natural history studies we look for an approximation of a controlled experiment already present in nature. This is called the "quasi-experimental method." It works even if we aren't wearing a white lab coat out in the woods. We are here concerned with overall process, not the exact methods though these are certainly important.
The Research Program summarizes the process that results in new theories that are evaluated in the Marketplace of Ideas. Only those theories will survive that have value in developing new theories and predicting what we observe. Often, theories are successful simply because they are especially elegant (like Einstein's relativity theory) long before they are demonstrated correct by additional observations in nature. But all theories must be verified or be demonstrably correct by being deductions from an already accepted and verified theory.
The Research Program uses the Science Machine to create new knowledge in the context of human society. Only through the operation of reason on fact in an orderly process can human beings solve the pressing problems accompanying our interaction with nature.
Example: Linnaeus, in the 1750's, popularized a special way of naming (ORGANIZING) plants and animals (binomial nomenclature) that popularized his great project, which was to COLLECT and catalogue all the kinds of plants and animals in the world. We are still doing this at the Buffalo Museum of Science and in other collections-based institutions. ANALYZING this data, we can make predictions (SYNTHESES) about how the ecosystems of the world are changing and suggest scenarios about how plants evolved. This is of particular importance nowadays with several global crises looming over us. Although most discoveries made by museum specialists seem to be just isolated facts ("Hey, I found a new species!" or "Here's a list of all the species of plants in Ecuador!") these are part of a world-wide research program of discovery, cataloging, analysis and synthesis that has been going on since Linnaeus' time, almost exactly 250 years!
The philosophy of science is complex but the above fairly simple explanation is in part based on the clear and compelling ideas of Imre Lakatos ("Falsification and the methodology of scientific programmes." Pp. 170-196 in J. A. Kourany, 1987, "Scientific Knowledge: Basic Issues in the Philosophy of Science," Wadsworth Publ. Co., Belmont, Calif.). Other philosophies of Lakatos, however, such as Marxism and adversity to probabilistic thinking, are not recommended. Nobody's perfect.
