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APPENDIX E: Introduction to the Scientific Method

Introduction to the Scientific Method
o I. The scientific method has four steps
o II. Testing hypotheses
o III. Common Mistakes in Applying the Scientific Method
o IV. Hypotheses, Models, Theories and Laws
o V. Are there circumstances in which the Scientific Method is not applicable?
o VI. Conclusion
o VII. References
Introduction to the Scientific Method
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.
I. The scientific method has four steps
1. Observation and description of a phenomenon or group of phenomena.
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.
II. Testing hypotheses
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.
III. Common Mistakes in Applying the Scientific Method
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.
IV. Hypotheses, Models, Theories and Laws
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.
VI. Conclusion
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.
VII. References
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).
3. Barrow, John. Theories of Everything (Oxford Univ. Press, 1991).
The methodology of scientific research programmes
by
Richard Harter
The methodology of scientific research programmes, Imre Lakatos, Philosophical papers, volume I, edited by John Worrall
and Gregory Currie, Cambridge University Press, 1995 printing, ISBN 0-521-28031-1, pbk.
Imre Lakatos was one of the major modern philosophers of science. His name and work is often placed in contrast with that of
Popper, Kuhn, and Feyerabend. Volume I of the collected papers deals with philosophy of science; volume II contains
collected papers on the philosophy of mathematics.
Volume I consists of an introduction and five papers. The introduction, Science and Pseudoscience, was written in 1973 and
was delivered as a radio address. The papers are:
1. Falsification and the methodology of scientific research programmes
2. History of science and its rational reconstructions
3. Popper on demarcation and induction
4. Why did Copernicus's research programme supersede Ptolemy's
5. Newton's effect on scientific standards
The entirety of this collection is worth reading and rereading very carefully. The first paper is THE paper, his major position
paper. In this review I will go through it and the introduction in some detail.
Introduction
The introduction raises the question: How do we tell science from pseudoscience? It begins with a survey of proposed answers
and the problems with those proposals. Thus, some philosophers have drawn the line by saying "a statement constitutes
knowledge if sufficiently many people believe it sufficiently strongly." It should be clear that this will not do for delimiting
scientific knowledge. He then goes on to quote Hume:
If we take in our hand any volume; of divinity, or school metaphysics, for instance; let us ask, does it contain any abstract
reasoning concerning quantity or number? No. Does it contain any experimental reasoning concerning matter of fact and
existence? No. Commit it then to the flames. For it can contain nothing but sophistry and illusion. [p2]
This sounds very well until one asks, what is the nature of this "experimental reasoning". Lakatos continues:
But what is 'experimental reasoning'? If we look at the vast seventeenth-century literature on witchcraft, it is full of reports of
careful observations and sworn evidence - even of experiments. Glanvill, the house philosopher of the early Royal Society,
regarded witchcraft as the paradigm of experimental reasoning. We have to define experimental reasoning before we start
Humean book burning.
In scientific reasoning, theories are confronted with facts; and one of the central conditions of scientific reasoning is that
theories must be supported by facts. Now how exactly can facts support theory? [p2]
An early answer, favored by Newton who believed himself to have done such, is that one proves theories by deducing them
from facts. This is readily seen to be impossible; one cannot, in general, deduce general laws from a finite number of facts.
Lakatos attributes this false belief in provability to the inheritance of attitudes taken over from theology where provability is in
the cards because theology starts with a presupposition of certain knowledge.
The presumption that provability was attainable was buttressed by the enormous success of Newtonian mechanics - scientists
believed that Newton had deciphered God's ultimate laws. Then came Einstein and provability was recognized as a mirage,
raising (reraising) the question:
If all scientific theories are equally unprovable, what distinguishes scientific knowledge from ignorance, science from
pseudoscience? [p3]
One twentieth century answer was "inductive logic" wherein theories were to be rated by their mathematical probability of
satisfying the available total evidence. This sounds plausible and has the attractive feature of providing a measurement of
quality. Popper, however, argued that the mathematical probability of any theory whatsoever, regardless of the amount of
evidence, is zero. Popper in turn proposed the falsification criterion:
A theory is 'scientific' if one is prepared to specify in advance a crucial experiment (or observation) which can falsify it, and it
is pseudoscientific if one refuses to specify such a 'potential falsifer'. [p3] In other words, we cannot prove scientific theories
but we can disprove them. A pseudoscientific theory is one which admits of no means of disproof.
A major difficulty with Popper's criterion is that science doesn't work that way. Scientific theories have tenacity; theories are
not jettisoned immediately because of facts that which contradict them. Sometimes rescue hypotheses are constructed;
sometimes anomalies are simply ignored or are set aside to be considered later. Quite often crucial falsifying experiments are
only seen to be such well after the fact.
If Popper has been falsified, what then? Lakatos says that Kuhn in turn suggests that "a scientific revolution is just an
irrational change in commitment, that it is a religious conversion". This, in my opinion, is not at all a proper interpretation of
Kuhn's theses although Kuhn is often read that way by post-modernists.
Lakatos proposes the a theory of research programmes. He remarks that research programmes are the unit of scientific
achievement rather than isolated hypotheses. A research programme typically a hard core, a protective belt of auxiliary
hypotheses, and a heuristic, i.e., problem solving machinery. Thus, in the Newtonian programme, the laws of motion and the
universal law of gravitation are the hard core. Anomalies in the motion of planets are dealt with by considering factors that
may affect the apparent motion, e.g., refraction of light or the existence of a hitherto unknown planet. The problem solving
machinery is the vast body of classical mathematical physics.
Research programmes, scientific or pseudoscientific, have, at any stage, both undigested anomalies and unsolved problems.
"All theories, in this sense, are born refuted and die refuted." Lakatos then makes a dubious move, to wit:
But how can one distinguish a scientific or progressive programme from a pseudoscientific or degenerating one? [p5] The
move, here, is to identify "scientific and progressive" and "pseudoscientific and degenerating". He goes on to nominate
"predicting new facts" as a major criterion for distinguishing between progressive and degenerating research programmes:
Thus, in a progressive research programme, theory leads to the discovery of hitherto unknown novel facts. In degenerating
programmes, however, theories are fabricated only in order to accomodate known facts. Has, for instance, Marxism ever
predicted a stunning novel fact successively? Never. [p5]
He was doing so well, up to his rhetorical query about Marxism. He followed this with a list of failed predictions of Marxist
theory. In point of fact, though, Marx made a number of novel predictions which panned out. Thus, Marxist theory predicted
(and it was far from obvious at the time) the consolidation and merging of large firms and the increasingly convulsive cycle of
booms and depressions - capitalism followed the predictions of Marxist theory up to the great depression. This is not to say
that Marxist theory was correct (he was, after all, an economist) or that the Marxist research programme did not degenerate; in
fact, it did because it fundamentally could not take into account the reaction of the industrial nations to economic crises.
Nor is it accurate to say that pseudosciences do not predict novel facts, even stunning natural facts. Velikovsky, for example,
predicted that Venus would be hot and that Jupiter would be a radio source. For that matter, psychic hotline psychics have
been known to make stunningly accurate predictions from time to time.
Lakatos, like many philosophers of science, tends to focus on physics, a focus that tends to be misleading. One of the striking
features of physics as a science is the reduction of the domain of phenomena to be considered. This reduction is an enabler of
the possibility of precise prediction.
Falsification and the methodology of scientific research programmes
This paper is the one that establishes the main force of Lakatos's argument. It begins with the observation that prior to the
twentieth century and Einstein "knowledge meant proven knowledge - proven either by the power of the intellect or by the
evidence of the senses." in response to Einstein's results the notion that scientific knowledge is proven knowledge has pretty
much been abandoned.
He then spends a few paragraphs contrasting Popper and Kuhn before launching into a taxonomy of philosophical positions.
Justificationism:
"According to the 'justificationists' scientific knowledge consisted of proven propositions." Lakatos distinguishes between
Classical Intellectualists who admitted powerful sorts of extralogical proofs, e.g., by revelation and intuition, and Classical
Empiricists who admitted as axioms only a hard core of empirical "proven facts". The latter necessarily augmented classical
deductive logic with "inductive logic". In the long run justificationism failed.
Probabilism (neojustificationism):
This approach treats scientific knowledge as "highly probable but not provable". As noted above Popper established
(according to Lakatos) that this does not work. It would have been nice if Lakatos had fleshed this assertion out - in ordinary
parlance one speaks of various hypotheses being more or less probable.
Dogmatic Falsification:
In turn Popper introduced dogmatic falsification:
Scientific honesty then consists of specifying, in advance, an experiment such that if the result contradicts the theory, the
theory have to be given up. [p13]
Lakatos argues at some length that dogmatic falsification is untenable. The essence of the matter seems to be that the line
between "experimental fact" and "theory" is not absolute. Lakatos also argues that knowing what is "fact" and what is "theory"
presupposes that there is a natural psychological (perceptual) border between them. In turn, this means that one has to be in
one's right mind to make the distinction. In his argument I particularly liked:
... Indeed, all brands of justificationist theories of knowledge which acknowledge the sense as a source (whether as one source
or as the source) of knowledge are bound to contain a psychology of observation.... All schools of modern justificationism can
be characterized by the particular psychotherapy by which they propose to prepare the mind to receive the grace of proven
truth in the course of a mystical experience. [p15]
Irrespective of the difficulties of grounding knowledge in the senses there is a methodological problem which is fatal (and is
central in Lakatos's treatment): Predictions and theory are always subject to an "all other things being equal" clause. Scientific
theories do not embrace all knowledge and the entirety of the universe; they are restricted in applicability.
Having lanced the boils of justificationism, neojustificationism, and dogmatic falsificationism, Lakatos introduces the shining
knight of methodological falsificationism. Before the knight is brought into the arena Lakatos first detours through the thickets
of conventionalism. The path is traced through pairs of alternatives, each pair being presented and one selected for further
exploration.
Choice the first: Passivist versus activist theories of knowledge
'Passivists' hold that true knowledge is Nature's imprint on a perfectly inert mind: mental activity can only result in bias and
distortion. The most influential passivist school is class empiricism. 'Activists' hold that we cannot read the book of nature
without interpreting it in the light of our expectations or theories. [p20]
As an observation, the most influential passivist schools are the various forms of mysticism, for which see Star Wars.
Choice the second: Conservative versus revolutionary activist theories
'Conservative activists' hold that we are born with our basic expectations; with them we turn the world into 'our world' but
must then live for ever in the prison of our world.... But revolutionary activists believe that conceptual frameworks can be
developed and also replaced by new, better ones; it is we who create our 'prisons' and we can also, critically, demolish them.
[p20]
Lakatos instances Kant and Kantians as conservative activists and, in a footnote, also Hegel. Lakatos says that Whewell,
Poincare, Milhaud and Le Roy opened the revolutionary activist door which now, apparently, acquires the label
"conventionalism".
Choice the third: Conservative versus revolutionary conventionalism
Conservative conventionalists hold that there is a standard sequence of stages. The first stage is a period in which theories are
developed by trial and error. The second stage are inductive epochs in which the best theories are 'proved' by a priori
considerations. The third stage is the cumulative development of auxiliary theories. The upshot is that well established
theories are ruled to have been proved by a methodological decision and are not refutable.
Revolutionary conventionalists, on the other hand, hold that theories are not permanent prisons and are always potentially
demolishable.
Choice the fourth: Simplicism versus methodological falsificationism
Lakatos identifies two rival schools of revolutionary conventionalism, Duhem's simplicism and Popper's methodological
falsificationism. The essence of simplicism is that the simpler (more elegant) theory is to be preferred; it is subject to the
objection that the criterion is highly subjective and is a matter of transitory fashion.
We have now arrived at Popper's methodological falsificationism which Lakatos proceeds to explore in more detail.
We may begin with the notion of "unproblematic background knowledge". Scientific knowledge forms a network of theory
and observation which is refined and elaborated over time. All theories and observations are open to question and challenge;
none are taken as being absolutely certain. This testing is done in the context of other scientific knowledge which is
provisionally assumed to be unproblematic.
The difference between dogmatic and methodological falsificationism is that the former treats experiment and observation as
being absolutely reliable as falsifiers whereas the the latter treats them as being provisional. Under methodological
falsificationism theories are marked as rejected, i.e., classified as unscientific; under dogmatic falsificationism they are
marked as falsified. In other words the shift from dogmatic to methodological falsification is a shift from truth to
admissability. Falsification abandons the notion of truth as such and uses the weaker notion of "not known to be false".
Methodological falsification in turn replaces "false" by "rejected".
Choice the fifth: Naive versus sophisticated methodological falsificationism.
The fundamental difference here is that naive methodological falsificationism rejects theories whereas sophisticated
methodological falsificationism replaces. (MF hereafter will stand for methodological falsificationism.) In naive MF theories
are rejected when they are "falsified"; in sophisticated MF theories are replaced by better theories.
The difficulty with falsification is that a "falsified" theory can always be rescued by an auxiliary hypotheses. in naive MF the
falsification decision becomes one of deciding whether to accept the rescuing hypothesis. In other words the decision must be
made as to whether the falsifying "datum" is an anomaly to be provisionally ignored or whether it is a damning crucial
experiment.
In sophisticated MF a replacement theory must meet two acceptibility criteria. The first is that it must have additional
empirical content, i.e., it must lead to the discovery of novel facts. The second is that the some of this excess content must be
verified. In addition it must explain the previous success of the theory it replaces.
Contrary to naive falsificationism, no experiment, experimental report, observation statement, or well-corroborated low-level
falsifying hypothesis alone can lead to falsification. There is no falsification before the emergence of a better theory. [p35]
Lakatos characterizes the 'falsification' situation as needing a pluralistic model. The situation is not of a conflict between
theory and facts but rather one between interpretive theories and explanatory theories.
Sophisticated MF, then, leads to the concept of a series of theories, each an improvement on its predecessors. Counter
evidence is always relative. That is, counter evidence is evidence that serves as a refutation of the theory being replaced and as
confirming evidence for the replacement theory.
The generation of these theories, then, takes place within the context of research programmes.
A research programme will have a negative and a positive heuristic associated with it.
The negative heuristic bars tampering with the 'hard core' - anomalies and counter instances are not accepted as refuting the
hard core, are not immediately taken as being fatal. Instead the core is rescued by auxiliary hypotheses. A programme remains
progressive as long as it continues to increase in empirical content.
The positive heuristic is the research policy of the programme - the puzzles to be solved, the models to be constructed, the
questions to be investigated - in short, the substance of Kuhn's "normal science". Lakatos emphasizes that methodology of
research programmes results in the relative autonomy of theoretical science.
Lakatos raises the question: How do research programmes die? Are their objective criteria for their death or do they simply
die as a consequence of changing scientific fashion?
His answer is that programmes are eliminated when superior programmes supersede them. This is, in his view, the rational
reason for their death. If a programme ceases to be progressive, i.e., it is no longer generating theories with no empirical
content, then it remains a part of the body of science.
Note: programmes do indeed fail for "social" reasons.
In the competition between programmes fledglings are given leeway. New programmes (which are continually being started
up and usually are abandoned) are given a chance to establish what they are good for. The real competition is between
programmes which start with different aspects of a domain and encroach on each other. Where they conflict experiment may
decide between them; then again it may not.
Lakatos says that there are two kinds of crucial experiments - those that decide between theories within a research programme
and those that decide between research programmes. The former are part of the normal process of scientific investigation.
Lakatos holds that real crucial experiments - those which establish one programme over another - are seldom recognized at the
time as being crucial. What is more, experiments intended to be crucial often are not. The essential difficulty is that it is only
after the conflict has been resolved that one can recognize what the experiment signified. Within each research programme the
putative crucial experiment is interpreted differently.
It is notable that Lakatos disparages Kuhn and in nowise represents him correctly (or favorably). Thus in the section on crucial
experiments we have:
One must never allow a research programme to become a weltanschauung, or a sort of scientific rigor, setting itself up as
arbiter between explanation and non-explanation, as mathematical rigor sets itself up as arbiter between proof and non-proof.
Unfortunately this is the position which Kuhn tends to advocate: indeed, what he calls 'normal science' is nothing but a
research program that has achieved monopoly. But, as a matter of fact, research programmes have achieved complete
monopoly only rarely and then only for relatively short periods, in spite of the efforts of some Cartesians, Newtonians and
Bohrians. The history of science has been and ought to be a history of competing research programmes (or, if you wish,
`paradigms') but it has not been and must not become a succession of periods of normal science: the sooner competition
starts, the better for progress. `Theoretical pluralism' is better than `theoretical monism': on this point Popper and Feyerabend
are right and Kuhn is wrong. [pp 68-69]
Now this is quite wrong. Kuhn's `normal science' is very much Lakatos's `positive heuristic'. It is the work that goes on within
the context of a research programme as Lakatos grudgingly admits in a footnote [p91]. Kuhn does not advocate theoretical
monism. One cannot equate paradigms and research programs although they are intimately related. Both Kuhn and Lakatos
make the same mistake on behalf of their intellectual children, Kuhn for his paradigms, Lakatos for his research programs,
which is to fail to recognize that they come in varied sizes, scope, and temporal duration. That is, effects due to these
variations are not reflected in their discussions.
In the conclusion Lakatos distinguishes between mature science, consisting of research programmes, and immature science
which consists of "a mere patched up pattern of trial and error". Mature science has "heuristic power".
The paper has quite a few examples - case studies - which illustrate his various arguments; they constitue much of the text.
These are well worth reading carefully. The section, Kuhn vs Popper, might profitably have been omitted.
Remarks in commentary
In reviewing a major work such as this one looks not only at what is said but also what is not said - what themes, thoughts,
and topics are absent.
One of the striking omissions is a theory of scientific truth. There are fragments of a theory; that is what the progression
through the theories of knowledge is about. It is a curious progression; we move through:
True
Not yet falsified
Not currently admitted to be falsified
In what sense, then, does scientific knowledge have any truth content at all? In what sense, then, can it even be said to be
knowledge? The answer, perhaps, lies in the notion of verisimilitude. Verisimilitude is, however, a notion with serious
problems. Lakatos wrestles with the problem here and there, mostly in the footnotes, but comes to no resolution or even
serious treatment. In practice discarded theories of scientific truth appear as "unproblematic background knowledge".
A second omission is the omission of everything except astronomy and physics. One might well ask whether his structure
applies to anything except physics (notoriously considered the hard, mature science.) Perhaps it does; perhaps it does not; he
does not begin to consider the question.
A third omission is that the question raised in the introduction, "What is the difference between science and pseudoscience" is
never actually answered. The main paper [written earlier] distinguishes between mature and immature science and between
progressive and degenerating research programmes. The introduction [written later] names various pseudosciences, e.g.
Marxism, but `demolishes' it as having a degenerate research programme. In `Real' science, however, a degenerate research
programme lives on as long as nothing better has come along.
The depiction of research programmes and the march of theories is rather schematic and doesn't accord well with actual
practice. Thus:
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In actual research at the forefront of a discipline there is seldom a 'current best' theory; instead there is a plethora of
competing theories.
Research programs usually die as a result of social changes.
Lakatos ignores the interrelationship between theory programmes and experimental programmes, and the essential
tension that exists there. My impression is that philsophers tend to privelege theory over experiment.
My impression, over all, is that he is engaged in a sociological analysis of how the process of science actually works (and
doing a much better job of it than the `science studies' folks do, I might add.) It is not the entirety of science and it is not the
entirety of the process.
He doesn't really come to terms, in my opinion, with why science works. This need not be counted as fault - it is not the
question he was engaged in answering.
Scientific method
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Scientific method Portal
Scientific method is a body of techniques for investigating phenomena and acquiring new knowledge, as well as for
correcting and integrating previous knowledge. It is based on gathering observable, empirical and measurable evidence subject
to specific principles of reasoning,[1] the collection of data through observation and experimentation, and the formulation and
testing of hypotheses.[2]
Although procedures vary from one field of inquiry to another, identifiable features distinguish scientific inquiry from other
methodologies of knowledge. Scientific researchers propose hypotheses as explanations of phenomena, and design
experimental studies that test these hypotheses for accuracy. These steps must be repeatable in order to predict dependably any
future results. Theories that encompass wider domains of inquiry may bind many hypotheses together in a coherent structure.
This in turn may assist in the formation of new hypotheses, as well as in placing groups of hypotheses into a broader context
of understanding.
Among other facets shared by the various fields of inquiry is the conviction that the process must be objective to reduce a
biased interpretation of the results. Another basic expectation is to document, archive and share all data and methodology so it
is available for careful scrutiny by other scientists, thereby allowing other researchers the opportunity to verify results by
attempting to reproduce them. This practice, called "full disclosure", also allows statistical measures of the reliability of
these data to be established.
Contents
[hide]

1 Elements of scientific method
o 1.1 DNA example
o 1.2 Characterizations
 1.2.1 Uncertainty
 1.2.2 Definition
 1.2.3 DNA/characterizations
 1.2.4 Precession of Mercury
1.3 Hypothesis development
 1.3.1 DNA/hypotheses
o 1.4 Predictions from the hypothesis
 1.4.1 DNA/predictions
 1.4.2 General relativity
o 1.5 Experiments
 1.5.1 DNA/experiments
2 Evaluation and iteration
o 2.1 Testing and improvement
 2.1.1 DNA/iterations
o 2.2 Confirmation
3 Models of scientific inquiry
o 3.1 Classical model
o 3.2 Pragmatic model
o 3.3 Computational approaches
4 Philosophy and sociology of science
5 Communication, community, culture
o 5.1 Peer review evaluation
o 5.2 Documentation and replication
 5.2.1 Archiving
 5.2.2 Dearchiving
 5.2.3 Limitations
o 5.3 Dimensions of practice
6 History
7 Relationship with mathematics
8 Notes and references
9 Further reading
10 See also
o 10.1 Synopsis of related topics
o 10.2 Logic, mathematics, methodology
o 10.3 Problems and issues
o 10.4 History, philosophy, sociology
11 External links
o 11.1 Science treatments
o 11.2 Alternative scientific treatments
o
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[edit] Elements of scientific method
There are multiple ways of outlining the basic method shared by all of the fields of scientific inquiry. The following examples
are typical classifications of the most important components of the method on which there is very wide agreement in the
scientific community and among philosophers of science, each of which are subject only to marginal disagreements about a
few very specific aspects.
The scientific method involves the following basic facets:[citation needed]

Observation. A constant feature of scientific inquiry, observation includes both unconditioned observations (prior to
any theory) as well as the observation of the experiment and its results.

Description. Information derived from experiments must be reliable, i.e., replicable (repeatable), as well as valid
(relevant to the inquiry).

Prediction. Information must be valid for observations past, present, and future of given phenomena, i.e., purported
"one shot" phenomena do not give rise to the capability to predict, nor to the ability to repeat an experiment.

Control. Actively and fairly sampling the range of possible occurrences, whenever possible and proper, as opposed
to the passive acceptance of opportunistic data, is the best way to control or counterbalance the risk of empirical bias.

Identification of causes. Identification of the causes of a particular phenomenon to the best achievable extent. For
cause-and-effect relationship to be established, the following must be established:

Time-order relationship. The hypothesized causes must precede the observed effects in time.

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Covariation of events. The hypothesized causes must correlate with observed effects. However,
correlations between events or variables are not necessarily indicative of causation.
Elimination of plausible alternatives. This is a gradual process that requires repeated experiments by
multiple researchers who must be able to replicate results in order to corroborate them.: All hypotheses and
theories are in principle subject to disproof. Thus, there is a point at which there might be a consensus about
a particular hypothesis or theory, yet it must in principle remain tentative. As a body of knowledge grows
and a particular hypothesis or theory repeatedly brings predictable results, confidence in the hypothesis or
theory increases.
Another simplified model sometimes utilized to summarize scientific method is the "operational":
The essential elements of a scientific method are operations, observations, models, and a utility function for evaluating
models.[citation needed]
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operation - Some action done to the system being investigated
Observation - What happens when the operation is done to the system
Model - A fact, hypothesis, theory, or the phenomenon itself at a certain moment
Utility Function - A measure of the usefulness of the model to explain, predict, and control, and of the cost of use of
it
One of the elements of any scientific utility function is the refutability of the model. Another is its simplicity, on the Principle
of Parsimony also known as Occam's Razor.
The following is a more thorough description of the method. This set of methodological elements and organization of
procedures will in general tend to be more characteristic of natural sciences and experimental psychology than of disciplines
commonly categorized as social sciences. Among the latter, methods of verification and testing of hypotheses may involve
less stringent mathematical and statistical interpretations of these elements within the respective disciplines. Nonetheless the
cycle of hypothesis, verification and formulation of new hypotheses will tend to resemble the basic cycle described below.
The essential elements of a scientific method are iterations, recursions, interleavings, and orderings of the following [3], [4], [5],
[6] [7] [8]
, , :
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Characterizations (Quantifications, observations, and measurements)
Hypotheses (theoretical, hypothetical explanations of observations and measurements)
Predictions (reasoning including logical deduction from hypothesis and theory)
Experiments (tests of all of the above)
Imre Lakatos and Thomas Kuhn had done extensive work on the "theory laden" character of observation. Kuhn (1961)
maintained that the scientist generally has a theory in mind before designing and undertaking experiments so as to make
empirical observations, and that the "route from theory to measurement can almost never be traveled backward". This
perspective implies that the way in which theory is tested is dictated by the nature of the theory itself, which led Kuhn (1961,
p. 166) to argue that "once it has been adopted by a profession ... no theory is recognized to be testable by any quantitative
tests that it has not already passed".
Each element of the scientific method is subject to peer review for possible mistakes. These activities do not describe all that
scientists do (see below) but apply mostly to experimental sciences (e.g., physics, chemistry). The elements above are often
taught in the educational system.[9]
The scientific method is not a recipe: it requires intelligence, imagination, and creativity[10]. Further, it is an ongoing cycle,
constantly developing more useful, accurate and comprehensive models and methods. For example, when Einstein developed
the Special and General Theories of Relativity, he did not in any way refute or discount Newton's Principia. On the contrary,
if one reduces out the astronomically large, the vanishingly small, and the extremely fast from Einstein's theories — all
phenomena that Newton could not have observed — one is left with Newton's equations. Einstein's theories are expansions
and refinements of Newton's theories, and the observations that increase our confidence in them also increase our confidence
in Newton's approximations to them.
The Keystones of Science project, sponsored by the journal Science, has selected a number of scientific articles from that
journal and annotated them, illustrating how different parts of each article embody the scientific method. Here is an annotated
example of the scientific method example titled Microbial Genes in the Human Genome: Lateral Transfer or Gene Loss?.
A linearized, pragmatic scheme of the four points above is sometimes offered as a guideline for proceeding: [citation needed]
1.
2.
3.
4.
5.
6.
7.
Define the question
Gather information and resources
Form hypothesis
Perform experiment and collect data
Analyze data
Interpret data and draw conclusions that serve as a starting point for new hypotheses
Publish results
The iterative cycle inherent in this step-by-step methodology goes from point 3 to 6 back to 3 again.
While this schema outlines a typical hypothesis/testing method, [11] it should also be noted that a number of philosophers,
historians and sociologists of science (perhaps most notably Paul Feyerabend) claim that such descriptions of scientific
method have little relation to the ways science is actually practiced.
[edit]
DNA example
Each element of scientific method is illustrated below by an example from the discovery of the structure of DNA:

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
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DNA/characterizations
DNA/hypotheses
DNA/predictions
DNA/experiments
The examples are continued in "Evaluations and iterations" with DNA/iterations.
[edit] Characterizations
The scientific method depends upon increasingly more sophisticated characterizations of subjects of the investigation. (The
subjects can also be called unsolved problems or the unknowns). For example, Benjamin Franklin correctly characterized St.
Elmo's fire as electrical in nature, but it has taken a long series of experiments and theory to establish this. While seeking the
pertinent properties of the subjects, this careful thought may also entail some definitions and observations; the observations
often demand careful measurements and/or counting.
The systematic, careful collection of measurements or counts of relevant quantities is often the critical difference between
pseudo-sciences, such as alchemy, and a science, such as chemistry or biology. Scientific measurements taken are usually
tabulated, graphed, or mapped, and statistical manipulations, such as correlation and regression, performed on them. The
measurements might be made in a controlled setting, such as a laboratory, or made on more or less inaccessible or
unmanipulatable objects such as stars or human populations. The measurements often require specialized scientific
instruments such as thermometers, spectroscopes, or voltmeters, and the progress of a scientific field is usually intimately tied
to their invention and development.
[edit] Uncertainty
Measurements in scientific work are also usually accompanied by estimates of their uncertainty. The uncertainty is often
estimated by making repeated measurements of the desired quantity. Uncertainties may also be calculated by consideration of
the uncertainties of the individual underlying quantities that are used. Counts of things, such as the number of people in a
nation at a particular time, may also have an uncertainty due to limitations of the method used. Counts may only represent a
sample of desired quantities, with an uncertainty that depends upon the sampling method used and the number of samples
taken.
[edit] Definition
Measurements demand the use of operational definitions of relevant quantities. That is, a scientific quantity is described or
defined by how it is measured, as opposed to some more vague, inexact or "idealized" definition. For example, electrical
current, measured in amperes, may be operationally defined in terms of the mass of silver deposited in a certain time on an
electrode in an electrochemical device that is described in some detail. The operational definition of a thing often relies on
comparisons with standards: the operational definition of "mass" ultimately relies on the use of an artifact, such as a certain
kilogram of platinum-iridium kept in a laboratory in France.
The scientific definition of a term sometimes differs substantially from their natural language usage. For example, mass and
weight overlap in meaning in common discourse, but have distinct meanings in mechanics. Scientific quantities are often
characterized by their units of measure which can later be described in terms of conventional physical units when
communicating the work.
New theories sometimes arise upon realizing that certain terms had not previously been sufficiently clearly defined. For
example, Albert Einstein's first paper on relativity begins by defining simultaneity and the means for determining length.
These ideas were skipped over by Isaac Newton with, "I do not define time, space, place and motion, as being well known to
all." Einstein's paper then demonstrates that they (viz., absolute time and length independent of motion) were approximations.
Francis Crick cautions us that when characterizing a subject, however, it can be premature to define something when it
remains ill-understood.[12] In Crick's study of consciousness, he actually found it easier to study awareness in the visual
system, rather than to study Free Will, for example. His cautionary example was the gene; the gene was much more poorly
understood before Watson and Crick's pioneering discovery of the structure of DNA; it would have been counterproductive to
spend much time on the definition of the gene, before them.
[edit]
DNA/characterizations
The history of the discovery of the structure of DNA is a classic example of the elements of scientific method: in
1950 it was known that genetic inheritance had a mathematical description, starting with the studies of Gregor
Mendel. But the mechanism of the gene was unclear. Researchers in Bragg's laboratory at Cambridge University
made X-ray diffraction pictures of various molecules, starting with crystals of salt, and proceeding to more
complicated substances. Using clues which were painstakingly assembled over the course of decades, beginning with
its chemical composition, it was determined that it should be possible to characterize the physical structure of DNA,
and the X-ray images would be the vehicle.
[edit] Precession of Mercury
Precession of the perihelion (exaggerated)
The characterization element can require extended and extensive study, even centuries. It took thousands of years of
measurements, from the Chaldean, Indian, Persian, Greek, Arabic and European astronomers, to record the motion of
planet Earth. Newton was able to condense these measurements into consequences of his laws of motion. But the
perihelion of the planet Mercury's orbit exhibits a precession which is not fully explained by Newton's laws of
motion. The observed difference for Mercury's precession, between Newtonian theory and relativistic theory
(approximately 43 arc-seconds per century), was one of the things that occurred to Einstein as a possible early test of
his theory of General Relativity.
[edit] Hypothesis development
A hypothesis is a suggested explanation of a phenomenon, or alternately a reasoned proposal suggesting a possible correlation
between or among a set of phenomena.
Normally hypotheses have the form of a mathematical model. Sometimes, but not always, they can also be formulated as
existential statements, stating that some particular instance of the phenomenon being studied has some characteristic and
causal explanations, which have the general form of universal statements, stating that every instance of the phenomenon has a
particular characteristic.
Scientists are free to use whatever resources they have — their own creativity, ideas from other fields, induction, Bayesian
inference, and so on — to imagine possible explanations for a phenomenon under study. Charles Sanders Peirce, borrowing a
page from Aristotle (Prior Analytics, 2.25) described the incipient stages of inquiry, instigated by the "irritation of doubt" to
venture a plausible guess, as abductive reasoning. The history of science is filled with stories of scientists claiming a "flash of
inspiration", or a hunch, which then motivated them to look for evidence to support or refute their idea. Michael Polanyi made
such creativity the centrepiece of his discussion of methodology.
Karl Popper, following others, developing and inverting the views of the Austrian logical positivists, has argued that a
hypothesis must be falsifiable, and that a proposition or theory cannot be called scientific if it does not admit the possibility of
being shown false. It must at least in principle be possible to make an observation that would show the proposition to be false,
even if that observation had not yet been made.
William Glen observes that the success of a hypothesis, or its service to science, lies not simply in its perceived "truth", or
power to displace, subsume or reduce a predecessor idea, but perhaps more in its ability to stimulate the research that will
illuminate … bald suppositions and areas of vagueness.[13]
In general scientists tend to look for theories that are "elegant" or "beautiful". In contrast to the usual English use of these
terms, they here refer to a theory in accordance with the known facts, which is nevertheless relatively simple and easy to
handle. Occam's Razor serves as a rule of thumb for making these determinations.
[edit]
DNA/hypotheses
Linus Pauling proposed that DNA was a triple helix. Francis Crick and James Watson learned of Pauling's
hypothesis, understood from existing data that Pauling was wrong and realized that Pauling would soon realize his
mistake. So the race was on to figure out the correct structure. Except that Pauling did not realize at the time that he
was in a race!
[edit] Predictions from the hypothesis
Any useful hypothesis will enable predictions, by reasoning including deductive reasoning. It might predict the outcome of an
experiment in a laboratory setting or the observation of a phenomenon in nature. The prediction can also be statistical and only
talk about probabilities.
It is essential that the outcome be currently unknown. Only in this case does the eventuation increase the probability that the
hypothesis be true. If the outcome is already known, it's called a consequence and should have already been considered while
formulating the hypothesis.
If the predictions are not accessible by observation or experience, the hypothesis is not yet useful for the method, and must
wait for others who might come afterward, and perhaps rekindle its line of reasoning. For example, a new technology or
theory might make the necessary experiments feasible.
[edit]
DNA/predictions
When Watson and Crick hypothesized that DNA was a double helix, Francis Crick predicted that an X-ray
diffraction image of DNA would show an X-shape. Also in their first paper they predicted that the double helix
structure that they discovered would prove important in biology, writing "It has not escaped our notice that the
specific pairing we have postulated immediately suggests a possible copying mechanism for the genetic material".
[edit] General relativity
Einstein's prediction (1907): Light bends in a gravitational field
Einstein's theory of General Relativity makes several specific predictions about the observable structure of spacetime, such as a prediction that light bends in a gravitational field and that the amount of bending depends in a precise
way on the strength of that gravitational field. Arthur Eddington's observations made during a 1919 solar eclipse
supported General Relativity rather than Newtonian gravitation.
[edit] Experiments
Main article: Experiments
Once predictions are made, they can be tested by experiments. If test results contradict predictions, then the hypotheses are
called into question and explanations may be sought. Sometimes experiments are conducted incorrectly and are at fault. If the
results confirm the predictions, then the hypotheses are considered likely to be correct but might still be wrong and are subject
to further testing.
Depending on the predictions, the experiments can have different shapes. It could be a classical experiment in a laboratory
setting, a double-blind study or an archaeological excavation. Even taking a plane from New York to Paris is an experiment
which tests the aerodynamical hypotheses used for constructing the plane.
Scientists assume an attitude of openness and accountability on the part of those conducting an experiment. Detailed record
keeping is essential, to aid in recording and reporting on the experimental results, and providing evidence of the effectiveness
and integrity of the procedure. They will also assist in reproducing the experimental results. This tradition can be seen in the
work of Hipparchus (190 BCE - 120 BCE), when determining a value for the precession of the Earth over 2100 years ago, and
1000 years before Al-Batani (853 CE – 929 CE).
[edit]
DNA/experiments
Before proposing their model Watson and Crick had previously seen x-ray diffraction images by Rosalind Franklin,
Maurice Wilkins, and Raymond Gosling. However, they later reported that Franklin initially rebuffed their
suggestion that DNA might be a double helix. Franklin had immediately spotted flaws in the initial hypotheses about
the structure of DNA by Watson and Crick. The X-shape in X-ray images helped confirm the helical structure of
DNA[14].
[edit] Evaluation and iteration
[edit] Testing and improvement
The scientific process is iterative. At any stage it is possible that some consideration will lead the scientist to repeat an earlier
part of the process. Failure to develop an interesting hypothesis may lead a scientist to re-define the subject they are
considering. Failure of a hypothesis to produce interesting and testable predictions may lead to reconsideration of the
hypothesis or of the definition of the subject. Failure of the experiment to produce interesting results may lead the scientist to
reconsidering the experimental method, the hypothesis or the definition of the subject.
Other scientists may start their own research and enter the process at any stage. They might adopt the characterization and
formulate their own hypothesis, or they might adopt the hypothesis and deduce their own predictions. Often the experiment is
not done by the person who made the prediction and the characterization is based on experiments done by someone else.
Published results of experiments can also serve as a hypothesis predicting their own reproducibility.
[edit]
DNA/iterations
After considerable fruitless experimentation, being discouraged by their superior from continuing, and numerous
false starts, Watson and Crick were able to infer the essential structure of DNA by concrete modeling of the physical
shapes of the nucleotides which comprise it. They were guided by the bond lengths which had been deduced by
Linus Pauling and the X-ray diffraction images of Rosalind Franklin.
[edit] Confirmation
Science is a social enterprise, and scientific work tends to be accepted by the community when it has been confirmed.
Crucially, experimental and theoretical results must be reproduced by others within the science community. Researchers have
given their lives for this vision; Georg Wilhelm Richmann was killed by lightning (1753) when attempting to replicate the
1752 kite-flying experiment of Benjamin Franklin.[15]
To protect against bad science and fraudulent data, government research granting agencies like NSF and science journals like
Nature and Science have a policy that researchers must archive their data and methods so other researchers can access it, test
the data and methods and build on the research that has gone before. Scientific data archiving can be done at a number of
national archives in the U.S. or in the World Data Center.
[edit] Models of scientific inquiry
Main article: Models of scientific inquiry
[edit] Classical model
The classical model of scientific inquiry derives from Aristotle[16], who distinguished the forms of approximate and exact
reasoning, set out the threefold scheme of abductive, deductive, and inductive inference, and also treated the compound forms
such as reasoning by analogy.
[edit] Pragmatic model
Main article: Pragmatic theory of truth
Charles Peirce considered scientific inquiry to be a species of the genus inquiry, which he defined as any means of fixing
belief, that is, any means of arriving at a settled opinion on a matter in question. He observed that inquiry in general begins
with a state of uncertainty and moves toward a state of certainty, sufficient at least to terminate the inquiry for the time being.
He graded the prevalent forms of inquiry according to their evident success in achieving their common objective, scoring
scientific inquiry at the high end of this scale. At the low end he placed what he called the method of tenacity, a die-hard
attempt to deny uncertainty and fixate on a favored belief. Next in line he placed the method of authority, a determined
attempt to conform to a chosen source of ready-made beliefs. After that he placed what might be called the method of
congruity, also called the a priori, the dilettante, or the what is agreeable to reason method. Peirce observed the fact of human
nature that almost everybody uses almost all of these methods at one time or another, and that even scientists, being human,
use the method of authority far more than they like to admit. But what recommends the specifically scientific method of
inquiry above all others is the fact that it is deliberately designed to arrive at the ultimately most secure beliefs, upon which
the most successful actions can be based.[17]
[edit] Computational approaches
Many subspecialties of applied logic and computer science, to name a few, artificial intelligence, machine learning,
computational learning theory, inferential statistics, and knowledge representation, are concerned with setting out
computational, logical, and statistical frameworks for the various types of inference involved in scientific inquiry, in
particular, hypothesis formation, logical deduction, and empirical testing. Some of these applications draw on measures of
complexity from algorithmic information theory to guide the making of predictions from prior distributions of experience, for
example, see the complexity measure called the speed prior from which a computable strategy for optimal inductive reasoning
can be derived.
[edit] Philosophy and sociology of science
Main article: Philosophy of science
While the philosophy of science has limited direct impact on day-to-day scientific practice, it plays a vital role in justifying
and defending the scientific approach. Philosophy of science looks at the underpinning logic of the scientific method, at what
separates science from non-science,and the ethic that is implicit in science.
We find ourselves in a world that is not directly understandable. We find that we sometimes disagree with others as to the
facts of the things we see in the world around us, and we find that there are things in the world that are at odds with our
present understanding. The scientific method attempts to provide a way in which we can reach agreement and understanding.
A "perfect" scientific method might work in such a way that rational application of the method would always result in
agreement and understanding; a perfect method would arguably be algorithmic, and so not leave any room for rational agents
to disagree. As with all philosophical topics, the search has been neither straightforward nor simple. Logical Positivist,
empiricist, falsificationist, and other theories have claimed to give a definitive account of the logic of science, but each has in
turn been criticized.
Thomas Samuel Kuhn examined the history of science in his The Structure of Scientific Revolutions, and found that the actual
method used by scientists differed dramatically from the then-espoused method.
Paul Feyerabend similarly examined the history of science, and was led to deny that science is genuinely a methodological
process. In his book Against Method he argues that scientific progress is not the result of applying any particular method. In
essence, he says that "anything goes", by which he meant that for any specific methodology or norm of science, successful
science has been done in violation of it. Criticisms such as his led to the strong programme, a radical approach to the
sociology of science.
In his 1958 book, Personal Knowledge, chemist and philosopher Michael Polanyi (1891-1976) criticized the common view
that the scientific method is purely objective and generates objective knowledge. Polanyi cast this view as a misunderstanding
of the scientific method and of the nature of scientific inquiry, generally. He argued that scientists do and must follow
personal passions in appraising facts and in determining which scientific questions to investigate. He concluded that a
structure of liberty is essential for the advancement of science - that the freedom to pursue science for its own sake is a
prerequisite for the production of knowledge through peer review and the scientific method.
The postmodernist critiques of science have themselves been the subject of intense controversy and heated dialogue. This
ongoing debate, known as the science wars, is the result of the conflicting values and assumptions held by the postmodernist
and realist camps. Whereas postmodernists assert that scientific knowledge is simply another discourse and not representative
of any form of fundamental truth, realists in the scientific community maintain that scientific knowledge does reveal real and
fundamental truths about reality. Many books have been written by scientists which take on this problem and challenge the
assertions of the postmodernists while defending science as a legitimate method of deriving truth. [18][19][20][21][22]
[edit] Communication, community, culture
Frequently the scientific method is not employed by a single person, but by several people cooperating directly or indirectly.
Such cooperation can be regarded as one of the defining elements of a scientific community. Various techniques have been
developed to ensure the integrity of the scientific method within such an environment.
[edit] Peer review evaluation
Scientific journals use a process of peer review, in which scientists' manuscripts are submitted by editors of scientific journals
to (usually one to three) fellow (usually anonymous) scientists familiar with the field for evaluation. The referees may or may
not recommend publication, publication with suggested modifications, or, sometimes, publication in another journal. This
serves to keep the scientific literature free of unscientific or crackpot work, helps to cut down on obvious errors, and generally
otherwise improve the quality of the scientific literature. Work announced in the popular press before going through this
process is generally frowned upon. Sometimes peer review inhibits the circulation of unorthodox work, especially if it
undermines the establishment in the particular field, and at other times may be too permissive. Other drawbacks includes
cronyism and favoritism. The peer review process is not always successful, but has been very widely adopted by the scientific
community.
[edit] Documentation and replication
Sometimes experimenters may make systematic errors during their experiments, unconsciously veer from the scientific
method (Pathological science) for various reasons, or (in rare cases) deliberately falsify their results. Consequently, it is a
common practice for other scientists to attempt to repeat the experiments in order to duplicate the results, thus further
validating the hypothesis.
[edit] Archiving
As a result, researchers are expected to practice scientific data archiving in compliance with the policies of government
funding agencies and scientific journals. Detailed records of their experimental procedures, raw data, statistical analyses and
source code are preserved in order to provide evidence of the effectiveness and integrity of the procedure and assist in
reproduction. These procedural records may also assist in the conception of new experiments to test the hypothesis, and may
prove useful to engineers who might examine the potential practical applications of a discovery.
[edit] Dearchiving
When additional information is needed before a study can be reproduced, the author of the study is expected to provide it
promptly - although a small charge may apply. If the author refuses to share data, appeals can be made to the journal editors
who published the study or to the institution who funded the research.
[edit] Limitations
Note that it is not possible for a scientist to record everything that took place in an experiment. He must select the facts he
believes to be relevant to the experiment and report them. This may lead, unavoidably, to problems later if some supposedly
irrelevant feature is questioned. For example, Heinrich Hertz did not report the size of the room used to test Maxwell's
equations, which later turned out to account for a small deviation in the results. The problem is that parts of the theory itself
need to be assumed in order to select and report the experimental conditions. The observations are hence sometimes described
as being 'theory-laden'.
[edit] Dimensions of practice
The primary constraints on contemporary western science are:
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Publication, i.e. Peer review
Resources (mostly funding)
It has not always been like this: in the old days of the "gentleman scientist" funding (and to a lesser extent publication) were
far weaker constraints.
Both of these constraints indirectly bring in a scientific method — work that too obviously violates the constraints will be
difficult to publish and difficult to get funded. Journals do not require submitted papers to conform to anything more specific
than "good scientific practice" and this is mostly enforced by peer review. Originality, importance and interest are more
important - see for example the author guidelines for Nature.
Criticisms (see Critical theory) of these restraints are that they are so nebulous in definition (e.g. "good scientific practice")
and open to ideological, or even political, manipulation apart from a rigorous practice of a scientific method, that they often
serve to censor rather than promote scientific discovery. [citation needed] Apparent censorship through refusal to publish ideas
unpopular with mainstream scientists (unpopular because of ideological reasons and/or because they seem to contradict long
held scientific theories) has soured the popular perception of scientists as being neutral or seekers of truth and often denigrated
popular perception of science as a whole.[citation needed]
[edit] History
Main article: History of scientific method
See also: Timeline of the history of scientific method
The development of the scientific method is inseparable from the history of science itself. Ancient Egyptian documents, such
as early papyri, describe methods of medical diagnosis. In ancient Greek culture, the method of empiricism was described.
The experimental scientific method was developed by Muslim scientists, who introduced the use of experiments to distinguish
between competing scientific theories set within a generally empirical orientation, which emerged with Alhazen's optical
experiments in his Book of Optics (c. 1000).[23][24] The fundamental tenets of the modern scientific method crystallized no later
than the rise of the modern physical sciences, in the 17th and 18th centuries. In his work Novum Organum (1620) — a
reference to Aristotle's Organon — Francis Bacon outlined a new system of logic to improve upon the old philosophical
process of syllogism. Then, in 1637, René Descartes established the framework for a scientific method's guiding principles in
his treatise, Discourse on Method. These writings are considered critical in the historical development of the scientific
method.
In the late 19th century, Charles Sanders Peirce proposed a schema that would turn out to have considerable influence in the
development of current scientific method generally. Peirce accelerated the progress on several fronts. Firstly, speaking in
broader context in "How to Make Our Ideas Clear" (1878) [2], Peirce outlined an objectively verifiable method to test the
truth of putative knowledge on a way that goes beyond mere foundational alternatives, focusing upon both deduction and
induction. He thus placed induction and deduction in a complementary rather than competitive context (the latter of which had
been the primary trend at least since David Hume, who wrote in the mid-to-late 18th century). Secondly, and of more direct
importance to modern method, Peirce put forth the basic schema for hypothesis/testing that continues to prevail today.
Extracting the theory of inquiry from its raw materials in classical logic, he refined it in parallel with the early development of
symbolic logic to address the then-current problems in scientific reasoning. Peirce examined and articulated the three
fundamental modes of reasoning that, as discussed above in this article, play a role in inquiry today, the processes that are
currently known as abductive, deductive, and inductive inference. Thirdly, he played a major role in the progress of symbolic
logic itself — indeed this was his primary specialty.
Karl Popper (1902–1994), beginning in the 1930s and with increased vigor after World War II, argued that a hypothesis must
be falsifiable and, following Peirce and others, that science would best progress using deductive reasoning as its primary
emphasis, known as critical rationalism. His astute formulations of logical procedure helped to rein in excessive use of
inductive speculation upon inductive speculation, and also strengthened the conceptual foundation for today's peer review
procedures.
[edit] Relationship with mathematics
Science is the process of gathering, comparing, and evaluating proposed models against observables. A model can be a
simulation, mathematical or chemical formula, or set of proposed steps. Science is like mathematics in that researchers in both
disciplines can clearly distinguish what is known from what is unknown at each stage of discovery. Models, in both science
and mathematics, need to be internally consistent and also ought to be falsifiable (capable of disproof). In mathematics, a
statement need not yet be proven; at such a stage, that statement would be called a conjecture. But when a statement has
attained mathematical proof, that statement gains a kind of immortality which is highly prized by mathematicians, and for
which some mathematicians devote their lives[25].
Mathematical work and scientific work can inspire each other. For example, the concept of time arose in science, and
timelessness was a hallmark of a mathematical topic. But today, the Poincaré conjecture is in the process of being proven,
using time as a mathematical concept, in which objects can flow (see Ricci flow).
[edit] Notes and references
1.
^ Isaac Newton (1687, 1713, 1726). "[4] Rules for the study of natural philosophy", Philosophiae Naturalis
Principia Mathematica, Third edition. The General Scholium containing the 4 rules follows Book 3, The System of
2.
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9.
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the World. Reprinted on pages 794-796 of I. Bernard Cohen and Anne Whitman's 1999 translation, University of
California Press ISBN 0-520-08817-4, 974 pages.
^ scientific method, Merriam-Webster Dictionary.
^ Galileo, Two New Sciences
^ William Glen, Mass-Extinction Debates: How science works in a crisis
^ Andrew J. Galambos, Sic Itur ad Astra (who learned it from Felix Ehrenhaft)
^ William Stanley Jevons, The principles of science: a treatise on logic and scientific method
^ Ørsted, Selected Scientific Works of Hans Christian Ørsted
^ Max Born, Natural Philosophy of Cause and Chance
^ In the inquiry-based education paradigm, the stage of "characterization, observation, definition, …" is more briefly
summed up under the rubric of a Question.
^ "To raise new questions, new possibilities, to regard old problems from a new angle, requires creative imagination
and marks real advance in science." p.92, Albert Einstein and Leopold Infeld (1938), The Evolution of Physics: from
early concepts to relativity and quanta ISBN0-671-20156-5
^ See, e.g., Gauch, Hugh G., Jr., Scientific Method in Practice (2003), esp. chapters 5-8
^ Crick, Francis (1994), The Astonishing Hypothesis ISBN 0-684-19431-7 p.20
^ Glen,William (ed.), The Mass-Extinction Debates: How Science Works in a Crisis, Stanford University Press,
Stanford, CA, 1994. ISBN 0-8047-2285-4. pp. 37-38.
^ "The instant I saw the picture my mouth fell open and my pulse began to race." -- James D. Watson (1968), The
Double Helix, page 167. New York: Atheneum, Library of Congress card number 68-16217. Page 168 shows the Xshaped pattern of the B-form of DNA, clearly indicating crucial details of its helical structure to Watson and Crick.
^ See, e.g., Physics Today, Vol. 59, #1, p42. [1]
^ Aristotle, "Prior Analytics", Hugh Tredennick (trans.), pp. 181-531 in Aristotle, Volume 1, Loeb Classical Library,
William Heinemann, London, UK, 1938.
^ Peirce, C.S., "Lectures on Pragmatism", Cambridge, MA, March 26 – May 17, 1903. Reprinted in part, Collected
Papers, CP 5.14–212. Reprinted with Introduction and Commentary, Patricia Ann Turisi (ed.), Pragmatism as a
Principle and a Method of Right Thinking: The 1903 Harvard "Lectures on Pragmatism", State University of New
York Press, Albany, NY, 1997. Reprinted, pp. 133–241, Peirce Edition Project (eds.), The Essential Peirce, Selected
Philosophical Writings, Volume 2 (1893–1913), Indiana University Press, Bloomington, IN, 1998.
^ Higher Superstition: The Academic Left and Its Quarrels with Science, The Johns Hopkins University Press, 1997
^ Fashionable Nonsense: Postmodern Intellectuals' Abuse of Science, Picador; 1st Picador USA Pbk. Ed edition,
1999
^ The Sokal Hoax: The Sham That Shook the Academy, University of Nebraska Press, 2000 ISBN 0803279957
^ A House Built on Sand: Exposing Postmodernist Myths About Science, Oxford University Press, 2000
^ Intellectual Impostures, Economist Books, 2003
^ Rosanna Gorini (2003), "Al-Haytham the Man of Experience, First Steps in the Science of Vision", International
Society for the History of Islamic Medicine, Institute of Neurosciences, Laboratory of Psychobiology and
Psychopharmacology, Rome, Italy:
"According to the majority of the historians al-Haytham was the pioneer of the modern scientific method. With his
book he changed the meaning of the term optics and established experiments as the norm of proof in the field. His
investigations are based not on abstract theories, but on experimental evidences and his experiments were systematic
and repeatable."
24. ^ David Agar (2001). Arabic Studies in Physics and Astronomy During 800 - 1400 AD. University of Jyväskylä.
25. ^ "When we are working intensively, we feel keenly the progress of our work; we are elated when our progress is
rapid, we are depressed when it is slow." page 131, in the section on 'Modern heuristic'-- the mathematician George
Polya (1957), How to solve it, Second edition.
[edit] Further reading
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Bacon, Francis Novum Organum (The New Organon), 1620. Bacon's work described many of the accepted
principles, underscoring the importance of theory, empirical results, data gathering, experiment, and independent
corroboration.
Bauer, Henry H., Scientific Literacy and the Myth of the Scientific Method, University of Illinois Press, Champaign,
IL, 1992
Beveridge, William I. B., The Art of Scientific Investigation, Vintage/Alfred A. Knopf, 1957.
Bernstein, Richard J., Beyond Objectivism and Relativism: Science, Hermeneutics, and Praxis, University of
Pennsylvania Press, Philadelphia, PA, 1983.
Bozinovski, Stevo, Consequence Driven Systems: Teaching, Learning, and Self-Learning Agents, GOCMAR
Publishers, Bitola, Macedonia, 1991.
Brody, Baruch A., and Grandy, Richard E., Readings in the Philosophy of Science, 2nd edition, Prentice Hall,
Englewood Cliffs, NJ, 1989.
Burks, Arthur W., Chance, Cause, Reason — An Inquiry into the Nature of Scientific Evidence, University of
Chicago Press, Chicago, IL, 1977.
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Chomsky, Noam, Reflections on Language, Pantheon Books, New York, NY, 1975.
Dewey, John, How We Think, D.C. Heath, Lexington, MA, 1910. Reprinted, Prometheus Books, Buffalo, NY, 1991.
Earman, John (ed.), Inference, Explanation, and Other Frustrations: Essays in the Philosophy of Science, University
of California Press, Berkeley & Los Angeles, CA, 1992.
Fraassen, Bas C. van, The Scientific Image, Oxford University Press, Oxford, UK, 1980.
Feyerabend, Paul K., Against Method, Outline of an Anarchistic Theory of Knowledge, 1st published, 1975.
Reprinted, Verso, London, UK, 1978.
Gadamer, Hans-Georg, Reason in the Age of Science, Frederick G. Lawrence (trans.), MIT Press, Cambridge, MA,
1981.
Giere, Ronald N. (ed.), Cognitive Models of Science, vol. 15 in 'Minnesota Studies in the Philosophy of Science',
University of Minnesota Press, Minneapolis, MN, 1992.
Hacking, Ian, Representing and Intervening, Introductory Topics in the Philosophy of Natural Science, Cambridge
University Press, Cambridge, UK, 1983.
Heisenberg, Werner, Physics and Beyond, Encounters and Conversations, A.J. Pomerans (trans.), Harper and Row,
New York, NY 1971, pp. 63–64.
Holton, Gerald, Thematic Origins of Scientific Thought, Kepler to Einstein, 1st edition 1973, revised edition, Harvard
University Press, Cambridge, MA, 1988.
Jevons, William Stanley, The Principles of Science: A Treatise on Logic and Scientific Method, 1874, 1877, 1879.
Reprinted with a foreword by Ernst Nagel, Dover Publications, New York, NY, 1958.
Kuhn, Thomas S., "The Function of Measurement in Modern Physical Science", ISIS 52(2), 161–193, 1961.
Kuhn, Thomas S., The Structure of Scientific Revolutions, University of Chicago Press, Chicago, IL, 1962. 2nd
edition 1970. 3rd edition 1996.
Kuhn, Thomas S., The Essential Tension, Selected Studies in Scientific Tradition and Change, University of Chicago
Press, Chicago, IL, 1977.
Latour, Bruno, Science in Action, How to Follow Scientists and Engineers through Society, Harvard University Press,
Cambridge, MA, 1987.
Losee, John, A Historical Introduction to the Philosophy of Science, Oxford University Press, Oxford, UK, 1972. 2nd
edition, 1980.
Maxwell, Nicholas, The Comprehensibility of the Universe: A New Conception of Science, Oxford University Press,
Oxford, 1998. Paperback 2003.
McComas, William F., ed. The Principle Elements of the Nature of Science: Dispelling the MythsPDF (189 KiB),
from The Nature of Science in Science Education, pp53-70, Kluwer Academic Publishers, Netherlands 1998.
Misak, Cheryl J., Truth and the End of Inquiry, A Peircean Account of Truth, Oxford University Press, Oxford, UK,
1991.
Newell, Allen, Unified Theories of Cognition, Harvard University Press, Cambridge, MA, 1990.
Peirce, C.S., Essays in the Philosophy of Science, Vincent Tomas (ed.), Bobbs–Merrill, New York, NY, 1957.
Peirce, C.S., "Lectures on Pragmatism", Cambridge, MA, March 26 – May 17, 1903. Reprinted in part, Collected
Papers, CP 5.14–212. Reprinted with Introduction and Commentary, Patricia Ann Turisi (ed.), Pragmatism as a
Principle and a Method of Right Thinking: The 1903 Harvard "Lectures on Pragmatism", State University of New
York Press, Albany, NY, 1997. Reprinted, pp. 133–241, Peirce Edition Project (eds.), The Essential Peirce, Selected
Philosophical Writings, Volume 2 (1893–1913), Indiana University Press, Bloomington, IN, 1998.
Peirce, C.S., Collected Papers of Charles Sanders Peirce, vols. 1-6, Charles Hartshorne and Paul Weiss (eds.), vols.
7-8, Arthur W. Burks (ed.), Harvard University Press, Cambridge, MA, 1931-1935, 1958. Cited as CP vol.para.
Piattelli-Palmarini, Massimo (ed.), Language and Learning, The Debate between Jean Piaget and Noam Chomsky,
Harvard University Press, Cambridge, MA, 1980.
Poincaré, Henri, Science and Hypothesis, 1905, Eprint
Popper, Karl R., The Logic of Scientific Discovery, 1934, 1959.[3]
Popper, Karl R., Unended Quest, An Intellectual Autobiography, Open Court, La Salle, IL, 1982.
Putnam, Hilary, Renewing Philosophy, Harvard University Press, Cambridge, MA, 1992.
Rorty, Richard, Philosophy and the Mirror of Nature, Princeton University Press, Princeton, NJ, 1979.
Salmon, Wesley C., Four Decades of Scientific Explanation, University of Minnesota Press, Minneapolis, MN, 1990.
Shimony, Abner, Search for a Naturalistic World View: Vol. 1, Scientific Method and Epistemology, Vol. 2, Natural
Science and Metaphysics, Cambridge University Press, Cambridge, UK, 1993.
Thagard, Paul, Conceptual Revolutions, Princeton University Press, Princeton, NJ, 1992.
Ziman, John (2000). Real Science: what it is, and what it means. Cambridge, Uk: Cambridge University Press.
[edit] See also
[edit] Synopsis of related topics
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Confirmability
Contingency
Falsifiability
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Inquiry
Reproducibility
Research
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Tautology
Testability
Theory
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Hypothesis
Hypothesis testing
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Statistics
Strong inference
Verification and Validation
[edit] Logic, mathematics, methodology
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Inference
o Abductive reasoning
o Deductive reasoning
o Inductive reasoning
Information theory
Logic
Mathematics
Methodology
[edit] Problems and issues
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Ockham's razor
Poverty of the stimulus
Reference class problem
Underdetermination
Holistic science
[edit] History, philosophy, sociology
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Cudos
Epistemology
Epistemic truth
History of science
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History of scientific method
Philosophy of science
Science studies
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Social research
Sociology of scientific knowledge
Timeline of scientific method
[edit] External links
Wikibooks has a book on the topic of
The Scientific Method
[edit] Science treatments
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An Introduction to Science: Scientific Thinking and a scientific method by Steven D. Schafersman.
Introduction to a scientific method
Theory-ladenness by Paul Newall at The Galilean Library
Lecture on Scientific Method by Greg Anderson
Analysis and Synthesis: On Scientific Method based on a study by Bernhard RiemannPDF (181 KiB) From the
Swedish Morphological Society
Using the scientific method for designing science fair projects from Science Made Simple
[edit] Alternative scientific treatments
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Research Methods Resources by the ICRAF-ILRI Research Methods Group
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Category:Scientific method
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Scientific method Portal
The scientific method is a sequence or collection of processes that are considered characteristic of scientific investigation and
the acquisition of new scientific knowledge based upon physical evidence.
The main article for this category is Scientific method.
Subcategories
There are 11 subcategories in this category, which are shown below. More may be shown on subsequent pages.
E
H
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Q cont.
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[+] Experimental design
[+] Experimental mathematics
[+] Science experiments
[+] Heuristics
[+] History of scientific method
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[+] Quantitative research
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[+] Research
[+] Research methods
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[+] Types of scientific fallacy
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[+] Observation
Q
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[+] Qualitative research
Pages in category "Scientific method"
There are 57 pages in this section of this category.
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Scientific method
History of scientific method
Timeline of the history of scientific
method
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Hypothetico-deductive model
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Reproducibility
Retrodiction
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Scientific consensus
Scientific control
Scientific enterprise
Scientific hypothesis
Scientific literacy
Videovoice
Selection bias
Self-experimentation
Self-experimentation in
medicine
Serendipity
Skepticism
Strong inference
Study design
Suspension of judgment
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Theory
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Visualized Experimental
Biology
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Inverse-square law
Adversarial review
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Blind taste test
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Case study
Critical-Creative Thinking and
Behavioral Research Laboratory
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Laboratory experimentation
List of scientific method
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Mature technology
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OGHET
Observational science
Observational study
Operationalization
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Data analysis
Data sharing
Deductive-nomological
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Empirical validation
Evidence based practice
Experimenter's regress
Experimentum crucis
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Peer review
Personoid
Physical law
Post-normal science
Prediction
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Free parameter
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Hypothesis
Hypothetical construct
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Qualitative psychological
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Qualitative research
Quantitative research
Quasi-empirical method
Quietism (philosophy)
Expert elicitation
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