The Scientific Method (A Review for the Global Warming crowd)

The scientific method is the process by which scientists, collectively and over time, endeavor to construct an accurate (that is, reliable, consistent and non-arbitrary) representation of the world.

Recognizing that personal and cultural beliefs influence both our perceptions and our interpretations of natural phenomena, we aim through the use of standard procedures and criteria to minimize those influences when developing a theory. As a famous scientist once said, "Smart people (like smart lawyers) can come up with very good explanations for mistaken points of view." In summary, the scientific method attempts to minimize the influence of bias or prejudice in the experimenter when testing an hypothesis or a theory.

2. Formulation of an hypothesis to explain the phenomena. In physics, the hypothesis often takes the form of a causal mechanism or a mathematical relation.

3. Use of the hypothesis to predict the existence of other phenomena, or to predict quantitatively the results of new observations.

4. Performance of experimental tests of the predictions by several independent experimenters and properly performed experiments.

If the experiments bear out the hypothesis it may come to be regarded as a theory or law of nature (more on the concepts of hypothesis, model, theory and law below). If the experiments do not bear out the hypothesis, it must be rejected or modified. What is key in the description of the scientific method just given is the predictive power (the ability to get more out of the theory than you put in; see Barrow, 1991) of the hypothesis or theory, as tested by experiment. It is often said in science that theories can never be proved, only disproved. There is always the possibility that a new observation or a new experiment will conflict with a long-standing theory.

As just stated, experimental tests may lead either to the confirmation of the hypothesis, or to the ruling out of the hypothesis. The scientific method requires that an hypothesis be ruled out or modified if its predictions are clearly and repeatedly incompatible with experimental tests. Further, no matter how elegant a theory is, its predictions must agree with experimental results if we are to believe that it is a valid description of nature. In physics, as in every experimental science, "experiment is supreme" and experimental verification of hypothetical predictions is absolutely necessary. Experiments may test the theory directly (for example, the observation of a new particle) or may test for consequences derived from the theory using mathematics and logic (the rate of a radioactive decay process requiring the existence of the new particle). Note that the necessity of experiment also implies that a theory must be testable. Theories which cannot be tested, because, for instance, they have no observable ramifications (such as, a particle whose characteristics make it unobservable), do not qualify as scientific theories.

If the predictions of a long-standing theory are found to be in disagreement with new experimental results, the theory may be discarded as a description of reality, but it may continue to be applicable within a limited range of measurable parameters. For example, the laws of classical mechanics (Newton's Laws) are valid only when the velocities of interest are much smaller than the speed of light (that is, in algebraic form, when v/c << 1). Since this is the domain of a large portion of human experience, the laws of classical mechanics are widely, usefully and correctly applied in a large range of technological and scientific problems. Yet in nature we observe a domain in which v/c is not small. The motions of objects in this domain, as well as motion in the "classical" domain, are accurately described through the equations of Einstein's theory of relativity. We believe, due to experimental tests, that relativistic theory provides a more general, and therefore more accurate, description of the principles governing our universe, than the earlier "classical" theory. Further, we find that the relativistic equations reduce to the classical equations in the limit v/c << 1. Similarly, classical physics is valid only at distances much larger than atomic scales (x >> 10-8 m). A description which is valid at all length scales is given by the equations of quantum mechanics.

We are all familiar with theories which had to be discarded in the face of experimental evidence. In the field of astronomy, the earth-centered description of the planetary orbits was overthrown by the Copernican system, in which the sun was placed at the center of a series of concentric, circular planetary orbits. Later, this theory was modified, as measurements of the planets motions were found to be compatible with elliptical, not circular, orbits, and still later planetary motion was found to be derivable from Newton's laws.

Error in experiments have several sources. First, there is error intrinsic to instruments of measurement. Because this type of error has equal probability of producing a measurement higher or lower numerically than the "true" value, it is called random error. Second, there is non-random or systematic error, due to factors which bias the result in one direction. No measurement, and therefore no experiment, can be perfectly precise. At the same time, in science we have standard ways of estimating and in some cases reducing errors. Thus it is important to determine the accuracy of a particular measurement and, when stating quantitative results, to quote the measurement error. A measurement without a quoted error is meaningless. The comparison between experiment and theory is made within the context of experimental errors. Scientists ask, how many standard deviations are the results from the theoretical prediction? Have all sources of systematic and random errors been properly estimated? This is discussed in more detail in the appendix on Error Analysis and in Statistics Lab 1.

As stated earlier, the scientific method attempts to minimize the influence of the scientist's bias on the outcome of an experiment. That is, when testing an hypothesis or a theory, the scientist may have a preference for one outcome or another, and it is important that this preference not bias the results or their interpretation. The most fundamental error is to mistake the hypothesis for an explanation of a phenomenon, without performing experimental tests. Sometimes "common sense" and "logic" tempt us into believing that no test is needed. There are numerous examples of this, dating from the Greek philosophers to the present day.

Another common mistake is to ignore or rule out data which do not support the hypothesis. Ideally, the experimenter is open to the possibility that the hypothesis is correct or incorrect. Sometimes, however, a scientist may have a strong belief that the hypothesis is true (or false), or feels internal or external pressure to get a specific result. In that case, there may be a psychological tendency to find "something wrong", such as systematic effects, with data which do not support the scientist's expectations, while data which do agree with those expectations may not be checked as carefully. The lesson is that all data must be handled in the same way.

Another common mistake arises from the failure to estimate quantitatively systematic errors (and all errors). There are many examples of discoveries which were missed by experimenters whose data contained a new phenomenon, but who explained it away as a systematic background. Conversely, there are many examples of alleged "new discoveries" which later proved to be due to systematic errors not accounted for by the "discoverers."

In a field where there is active experimentation and open communication among members of the scientific community, the biases of individuals or groups may cancel out, because experimental tests are repeated by different scientists who may have different biases. In addition, different types of experimental setups have different sources of systematic errors. Over a period spanning a variety of experimental tests (usually at least several years), a consensus develops in the community as to which experimental results have stood the test of time.

In physics and other science disciplines, the words "hypothesis," "model," "theory" and "law" have different connotations in relation to the stage of acceptance or knowledge about a group of phenomena.

An hypothesis is a limited statement regarding cause and effect in specific situations; it also refers to our state of knowledge before experimental work has been performed and perhaps even before new phenomena have been predicted. To take an example from daily life, suppose you discover that your car will not start. You may say, "My car does not start because the battery is low." This is your first hypothesis. You may then check whether the lights were left on, or if the engine makes a particular sound when you turn the ignition key. You might actually check the voltage across the terminals of the battery. If you discover that the battery is not low, you might attempt another hypothesis ("The starter is broken"; "This is really not my car.")

The word model is reserved for situations when it is known that the hypothesis has at least limited validity. A often-cited example of this is the Bohr model of the atom, in which, in an analogy to the solar system, the electrons are described has moving in circular orbits around the nucleus. This is not an accurate depiction of what an atom "looks like," but the model succeeds in mathematically representing the energies (but not the correct angular momenta) of the quantum states of the electron in the simplest case, the hydrogen atom. Another example is Hook's Law (which should be called Hook's principle, or Hook's model), which states that the force exerted by a mass attached to a spring is proportional to the amount the spring is stretched. We know that this principle is only valid for small amounts of stretching. The "law" fails when the spring is stretched beyond its elastic limit (it can break). This principle, however, leads to the prediction of simple harmonic motion, and, as a model of the behavior of a spring, has been versatile in an extremely broad range of applications.

A scientific theory or law represents an hypothesis, or a group of related hypotheses, which has been confirmed through repeated experimental tests. Theories in physics are often formulated in terms of a few concepts and equations, which are identified with "laws of nature," suggesting their universal applicability. Accepted scientific theories and laws become part of our understanding of the universe and the basis for exploring less well-understood areas of knowledge. Theories are not easily discarded; new discoveries are first assumed to fit into the existing theoretical framework. It is only when, after repeated experimental tests, the new phenomenon cannot be accommodated that scientists seriously question the theory and attempt to modify it. The validity that we attach to scientific theories as representing realities of the physical world is to be contrasted with the facile invalidation implied by the expression, "It's only a theory." For example, it is unlikely that a person will step off a tall building on the assumption that they will not fall, because "Gravity is only a theory."

Changes in scientific thought and theories occur, of course, sometimes revolutionizing our view of the world (Kuhn, 1962). Again, the key force for change is the scientific method, and its emphasis on experiment.

V. Are there circumstances in which the Scientific Method is not applicable?

While the scientific method is necessary in developing scientific knowledge, it is also useful in everyday problem-solving. What do you do when your telephone doesn't work? Is the problem in the hand set, the cabling inside your house, the hookup outside, or in the workings of the phone company? The process you might go through to solve this problem could involve scientific thinking, and the results might contradict your initial expectations.

Like any good scientist, you may question the range of situations (outside of science) in which the scientific method may be applied. From what has been stated above, we determine that the scientific method works best in situations where one can isolate the phenomenon of interest, by eliminating or accounting for extraneous factors, and where one can repeatedly test the system under study after making limited, controlled changes in it.

There are, of course, circumstances when one cannot isolate the phenomena or when one cannot repeat the measurement over and over again. In such cases the results may depend in part on the history of a situation. This often occurs in social interactions between people. For example, when a lawyer makes arguments in front of a jury in court, she or he cannot try other approaches by repeating the trial over and over again in front of the same jury. In a new trial, the jury composition will be different. Even the same jury hearing a new set of arguments cannot be expected to forget what they heard before.

The scientific method is intricately associated with science, the process of human inquiry that pervades the modern era on many levels. While the method appears simple and logical in description, there is perhaps no more complex question than that of knowing how we come to know things. In this introduction, we have emphasized that the scientific method distinguishes science from other forms of explanation because of its requirement of systematic experimentation. We have also tried to point out some of the criteria and practices developed by scientists to reduce the influence of individual or social bias on scientific findings. Further investigations of the scientific method and other aspects of scientific practice may be found in the references listed below.

1. Wilson, E. Bright. An Introduction to Scientific Research (McGraw-Hill, 1952).

2. Kuhn, Thomas. The Structure of Scientific Revolutions (Univ. of Chicago Press, 1962).

4. Are we capable of significantly influencing those results positively?

The answer to that is likely yes. To create man-made fog that would cover a square mile of ocean would take about 40 gallons of seawater and less than $1 in energy. Fog can keep the ocean beneath it cooler during the day by reflecting sunlight then burn off and let the ocean radiate heat at night. This web page about cloud water content is interesting: www-das.uwyo.edu

Clouds can also be used as a blanket to keep the ocean warmer at night if wanted. We can increase clouds over the oceans by increasing the availability of condensation nuclei. The salt in seawater can be used to do that. Man-made clouds and snow are likely powerful tools for active climate management. A passive method such as CO2 reduction is probably one of the more inefficient methods.

To create man-made fog that would cover a square mile of ocean would take about 40 gallons of seawater and less than $1 in energy. Fog can keep the ocean beneath it cooler during the day by reflecting sunlight then burn off and let the ocean radiate heat at night.

I'll take your word for it that man-made fog can be created in this way, at this price. I'll take your word for it that this would cool the ocean underneath the fog. (I'm not sure about this. For one thing, water vapor is a greenhouse gas. For another thing, it was my impression that the effects of cloud formation on the greenhouse effect were not well understood.)

My question 4. remains as to whether this action would influence the effects of climate change positively. Even if you prove you've got an easy "cooling" method there are still some missing steps when it comes to proving you've affected the climate positively. At best, if I buy what you're saying, you've essentially created a local heat-sink out in the middle of the ocean somewhere. But climate is a complex system, it's not as easy as "reduce the temperature somewhere". Would your fog-machine really help in the straightforward, linear way you imply, or might it somehow create a feedback of some sort that would negate or even outweigh the local cooling? What would be the effect on weather, on currents? What amount of energy are you talking about reflecting with your method - is it significant, or a drop in the bucket?

And as a starting matter, we haven't even answered my question 3. - is the predicted climate change (global warming) net-bad or net-good for humanity? Even if your method here would work, if global warming is a net-good, then you're harming, not helping.

Anyway, though, you do suggest an interesting technique & I'd be interested to learn more, got a link? (=a link about man-made fog and how/why it would reduce the greenhouse effect)

Again, it was my impression (when I actually worked on climate models, in a former life :) that neither cloud-formation nor what their overall effect was (net reflector, because they're "white"? or do they trap heat, because it's water?) were well understood (or modeled). Maybe things have changed since then (it was probably 10 years ago after all).

To create man-made fog that would cover a square mile of ocean would take about 40 gallons of seawater and less than $1 in energy.

That's one square mile. There are 224,393,542 square miles of water surface on the planet. How much would have to be covered to result in a significant change in water temp? Just 5 percent coverage would require 11.2 million gallons of water and several million dollars worth of energy. One percent would require approximately 2.2 million gallons of water. And even if this could be done, would it be a lasting effect? How long would the fog have to cover the water? Could the fog last long enough to affect the desired change? I doubt that this would be feasible let alone practical.

Just 5 percent coverage would require 11.2 million gallons of water and several million dollars worth of energy.

At that kind of scale it would cost about a penny per gallon to atomize it into fog, which works out to $112,000 per day or $41 million/year in energy costs, less than 1 penny per human per year to increase Earth's sunlight reflectivity 5% and plunge us into an ice age. I've read 1% is all we need to counter the effects of climate change. Solar and wave action could be the source of the energy. The climate change could become self-feeding if more snow forms.

My point is there are many climate management technologies possible. This is just one idea. It might not be practical but shows there could be cost effective methods of active climate management.

The global warming scientists say they don't understand the role of clouds and other aerosols in the climate. I suspect they don't want to know since it short circuits the whole CO2 reduction angle. Until they know exactly the role of clouds in climate change they shouldn't be recommending expensive CO2 reduction solutions that may be much more impractical than a man-made fog idea.

I see. I ran into another posting here the other day that spoke of water in the atmosphere. Here's a piece of that report which supports the notion that man-made Global Warming scientist either don't know or refuse to acknowledge the factor water vapor has on global climate:

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Just how much of the "Greenhouse Effect" is caused by human activity?

It is about 0.28%, if water vapor is taken into account-- about 5.53%, if not.

This point is so crucial to the debate over global warming that how water vapor is or isn't factored into an analysis of Earth's greenhouse gases makes the difference between describing a significant human contribution to the greenhouse effect, or a negligible one.

Water vapor constitutes Earth's most significant greenhouse gas, accounting for about 95% of Earth's greenhouse effect (4). Interestingly, many "facts and figures' regarding global warming completely ignore the powerful effects of water vapor in the greenhouse system, carelessly (perhaps, deliberately) overstating human impacts as much as 20-fold.

Water vapor is 99.999% of natural origin. Other atmospheric greenhouse gases, carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and miscellaneous other gases (CFC's, etc.), are also mostly of natural origin (except for the latter, which is mostly anthropogenic).

Human activites contribute slightly to greenhouse gas concentrations through farming, manufacturing, power generation, and transportation. However, these emissions are so dwarfed in comparison to emissions from natural sources we can do nothing about, that even the most costly efforts to limit human emissions would have a very small-- perhaps undetectable-- effect on global climate.

Click here for the rest of the report.

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