## 1. Overview and organizing themes



This entry could have been given the title Scientific Methods and gone
on to fill volumes, or it could have been extremely short, consisting
of a brief summary rejection of the idea that there is any such thing
as a unique Scientific Method at all. Both unhappy prospects are due
to the fact that scientific activity varies so much across
disciplines, times, places, and scientists that any account which
manages to unify it all will either consist of overwhelming
descriptive detail, or trivial generalizations.



The choice of scope for the present entry is more optimistic, taking a
cue from the recent movement in philosophy of science toward a greater
attention to practice: to what scientists actually do. This
"turn to practice" can be seen as the latest form of
studies of methods in science, insofar as it represents an attempt at
understanding scientific activity, but through accounts that are
neither meant to be universal and unified, nor singular and narrowly
descriptive. To some extent, different scientists at different times
and places can be said to be using the same method even though, in
practice, the details are different.



Whether the context in which methods are carried out is relevant, or
to what extent, will depend largely on what one takes the aims of
science to be and what one's own aims are. For most of the
history of scientific methodology the assumption has been that the
most important output of science is knowledge and so the aim of
methodology should be to discover those methods by which scientific
knowledge is generated.



Science was seen to embody the most successful form of reasoning (but
which form?) to the most certain knowledge claims (but how certain?)
on the basis of systematically collected evidence (but what counts as
evidence, and should the evidence of the senses take precedence, or
rational insight?)
 Section 2
 surveys some of the history, pointing to two major themes. One theme
is seeking the right balance between observation and reasoning (and
the attendant forms of reasoning which employ them); the other is how
certain scientific knowledge is or can be.



Section 3
 turns to 20th century debates on scientific method. In the
second half of the 20th century the epistemic privilege of
science faced several challenges and many philosophers of science
abandoned the reconstruction of the logic of scientific method. Views
changed significantly regarding which functions of science ought to be
captured and why. For some, the success of science was better
identified with social or cultural features. Historical and
sociological turns in the philosophy of science were made, with a
demand that greater attention be paid to the non-epistemic aspects of
science, such as sociological, institutional, material, and political
factors. Even outside of those movements there was an increased
specialization in the philosophy of science, with more and more focus
on specific fields within science. The combined upshot was very few
philosophers arguing any longer for a grand unified methodology of
science. Sections 3 and 4 surveys the main positions on scientific
method in 20th century philosophy of science, focusing on
where they differ in their preference for confirmation or
falsification or for waiving the idea of a special scientific method
altogether.



In recent decades, attention has primarily been paid to scientific
activities traditionally falling under the rubric of method, such as
experimental design and general laboratory practice, the use of
statistics, the construction and use of models and diagrams,
interdisciplinary collaboration, and science communication. Sections
4-6 attempt to construct a map of the current domains of the
study of methods in science.



As these sections illustrate, the question of method is still central
to the discourse about science. Scientific method remains a topic for
education, for science policy, and for scientists. It arises in the
public domain where the demarcation or status of science is at issue.
Some philosophers have recently returned, therefore, to the question
of what it is that makes science a unique cultural product. This entry
will close with some of these recent attempts at discerning and
encapsulating the activities by which scientific knowledge is
achieved.


## 2. Historical Review: Aristotle to Mill



Attempting a history of scientific method compounds the vast scope of
the topic. This section briefly surveys the background to modern
methodological debates. What can be called the classical view goes
back to antiquity, and represents a point of departure for later
 divergences.[1]



We begin with a point made by Laudan (1968) in his historical survey
of scientific method:



> 
> 
> Perhaps the most serious inhibition to the emergence of the history of
> theories of scientific method as a respectable area of study has been
> the tendency to conflate it with the general history of epistemology,
> thereby assuming that the narrative categories and classificatory
> pigeon-holes applied to the latter are also basic to the former.
> (1968: 5)
> 
> 
> 



To see knowledge about the natural world as falling under knowledge
more generally is an understandable conflation. Histories of theories
of method would naturally employ the same narrative categories and
classificatory pigeon holes. An important theme of the history of
epistemology, for example, is the unification of knowledge, a theme
reflected in the question of the unification of method in science.
Those who have identified differences in kinds of knowledge have often
likewise identified different methods for achieving that kind of
knowledge (see the entry on the
 unity of science).



Different views on what is known, how it is known, and what can be
known are connected. Plato distinguished the realms of things into the
visible and the intelligible (*The Republic*, 510a, in Cooper
1997). Only the latter, the Forms, could be objects of knowledge. The
intelligible truths could be known with the certainty of geometry and
deductive reasoning. What could be observed of the material world,
however, was by definition imperfect and deceptive, not ideal. The
Platonic way of knowledge therefore emphasized reasoning as a method,
downplaying the importance of observation. Aristotle disagreed,
locating the Forms in the natural world as the fundamental principles
to be discovered through the inquiry into nature (*Metaphysics
Z*, in Barnes 1984).



Aristotle is recognized as giving the earliest systematic treatise on
the nature of scientific inquiry in the western tradition, one which
embraced observation and reasoning about the natural world. In the
*Prior* and *Posterior Analytics*, Aristotle reflects
first on the aims and then the methods of inquiry into nature. A
number of features can be found which are still considered by most to
be essential to science. For Aristotle, empiricism, careful
observation (but passive observation, not controlled experiment), is
the starting point. The aim is not merely recording of facts, though.
For Aristotle, science (*episteme*) is a body of
properly arranged knowledge or learning--the empirical facts, but
also their ordering and display are of crucial importance. The aims of
discovery, ordering, and display of facts partly determine the methods
required of successful scientific inquiry. Also determinant is the
nature of the knowledge being sought, and the explanatory causes
proper to that kind of knowledge (see the discussion of the four
causes in the entry on
 Aristotle on causality).



In addition to careful observation, then, scientific method requires a
logic as a system of reasoning for properly arranging, but also
inferring beyond, what is known by observation. Methods of reasoning
may include induction, prediction, or analogy, among others.
Aristotle's system (along with his catalogue of fallacious
reasoning) was collected under the title the *Organon*. This
title would be echoed in later works on scientific reasoning, such as
*Novum Organon* by Francis Bacon, and *Novum Organon
Restorum* by William Whewell (see below). In Aristotle's
*Organon* reasoning is divided primarily into two forms, a
rough division which persists into modern times. The division, known
most commonly today as deductive versus inductive method, appears in
other eras and methodologies as analysis/ synthesis,
non-ampliative/ ampliative, or even
confirmation/ verification. The basic idea is there are two
"directions" to proceed in our methods of inquiry: one
away from what is observed, to the more fundamental, general, and
encompassing principles; the other, from the fundamental and general
to instances or implications of principles.



The basic aim and method of inquiry identified here can be seen as a
theme running throughout the next two millennia of reflection on the
correct way to seek after knowledge: carefully observe nature and then
seek rules or principles which explain or predict its operation. The
Aristotelian corpus provided the framework for a commentary tradition
on scientific method independent of science itself (cosmos versus
physics.) During the medieval period, figures such as Albertus Magnus
(1206-1280), Thomas Aquinas (1225-1274), Robert
Grosseteste (1175-1253), Roger Bacon (1214/1220-1292),
William of Ockham (1287-1347), Andreas Vesalius
(1514-1546), Giacomo Zabarella (1533-1589) all worked to
clarify the kind of knowledge obtainable by observation and induction,
the source of justification of induction, and best rules for its
 application.[2]
 Many of their contributions we now think of as essential to science
(see also Laudan 1968). As Aristotle and Plato had employed a
framework of reasoning either "to the forms" or
"away from the forms", medieval thinkers employed
directions away from the phenomena or back to the phenomena. In
analysis, a phenomena was examined to discover its basic explanatory
principles; in synthesis, explanations of a phenomena were constructed
from first principles.



During the Scientific Revolution these various strands of argument,
experiment, and reason were forged into a dominant epistemic
authority. The 16th-18th centuries were a
period of not only dramatic advance in knowledge about the operation
of the natural world--advances in mechanical, medical,
biological, political, economic explanations--but also of
self-awareness of the revolutionary changes taking place, and intense
reflection on the source and legitimation of the method by which the
advances were made. The struggle to establish the new authority
included methodological moves. The Book of Nature, according to the
metaphor of Galileo Galilei (1564-1642) or Francis Bacon
(1561-1626), was written in the language of mathematics, of
geometry and number. This motivated an emphasis on mathematical
description and mechanical explanation as important aspects of
scientific method. Through figures such as Henry More and Ralph
Cudworth, a neo-Platonic emphasis on the importance of metaphysical
reflection on nature behind appearances, particularly regarding the
spiritual as a complement to the purely mechanical, remained an
important methodological thread of the Scientific Revolution (see the
entries on
 Cambridge platonists;
 Boyle;
 Henry More;
 Galileo).



In *Novum Organum* (1620), Bacon was critical of the
Aristotelian method for leaping from particulars to universals too
quickly. The syllogistic form of reasoning readily mixed those two
types of propositions. Bacon aimed at the invention of new arts,
principles, and directions. His method would be grounded in methodical
collection of observations, coupled with correction of our senses (and
particularly, directions for the avoidance of the Idols, as he called
them, kinds of systematic errors to which naive observers are
prone.) The community of scientists could then climb, by a careful,
gradual and unbroken ascent, to reliable general claims.



Bacon's method has been criticized as impractical and too
inflexible for the practicing scientist. Whewell would later criticize
Bacon in his *System of Logic* for paying too little attention
to the practices of scientists. It is hard to find convincing examples
of Bacon's method being put in to practice in the history of
science, but there are a few who have been held up as real examples of
16th century scientific, inductive method, even if not in
the rigid Baconian mold: figures such as Robert Boyle
(1627-1691) and William Harvey (1578-1657) (see the entry
on
 Bacon).



It is to Isaac Newton (1642-1727), however, that historians of
science and methodologists have paid greatest attention. Given the
enormous success of his *Principia Mathematica* and
*Opticks*, this is understandable. The study of Newton's
method has had two main thrusts: the implicit method of the
experiments and reasoning presented in the Opticks, and the explicit
methodological rules given as the Rules for Philosophising (the
Regulae) in Book III of the
 *Principia*.[3]
 Newton's law of gravitation, the linchpin of his new cosmology,
broke with explanatory conventions of natural philosophy, first for
apparently proposing action at a distance, but more generally for not
providing "true", physical causes. The argument for his
System of the World (*Principia*, Book III) was based on
phenomena, not reasoned first principles. This was viewed (mainly on
the continent) as insufficient for proper natural philosophy. The
Regulae counter this objection, re-defining the aims of natural
philosophy by re-defining the method natural philosophers should
follow. (See the entry on
 Newton's philosophy.)



To his list of methodological prescriptions should be added
Newton's famous phrase "*hypotheses non
fingo*" (commonly translated as "I frame no
hypotheses".) The scientist was not to invent systems but infer
explanations from observations, as Bacon had advocated. This would
come to be known as inductivism. In the century after Newton,
significant clarifications of the Newtonian method were made. Colin
Maclaurin (1698-1746), for instance, reconstructed the essential
structure of the method as having complementary analysis and synthesis
phases, one proceeding away from the phenomena in generalization, the
other from the general propositions to derive explanations of new
phenomena. Denis Diderot (1713-1784) and editors of the
*Encyclopedie* did much to consolidate and popularize
Newtonianism, as did Francesco Algarotti (1721-1764). The
emphasis was often the same, as much on the character of the scientist
as on their process, a character which is still commonly assumed. The
scientist is humble in the face of nature, not beholden to dogma,
obeys only his eyes, and follows the truth wherever it leads. It was
certainly Voltaire (1694-1778) and du Chatelet (1706-1749)
who were most influential in propagating the latter vision of the
scientist and their craft, with Newton as hero. Scientific method
became a revolutionary force of the Enlightenment. (See also the
entries on
 Newton,
 Leibniz,
 Descartes,
 Boyle,
 Hume,
 enlightenment, as well as Shank 2008 for a historical overview.)



Not all 18th century reflections on scientific method were
so celebratory. Famous also are George Berkeley's
(1685-1753) attack on the mathematics of the new science, as
well as the over-emphasis of Newtonians on observation; and David
Hume's (1711-1776) undermining of the warrant offered for
scientific claims by inductive justification (see the entries on:
 George Berkeley;
 David Hume;
 Hume's Newtonianism and Anti-Newtonianism).
 Hume's problem of induction motivated Immanuel Kant
(1724-1804) to seek new foundations for empirical method, though
as an epistemic reconstruction, not as any set of practical guidelines
for scientists. Both Hume and Kant influenced the methodological
reflections of the next century, such as the debate between Mill and
Whewell over the certainty of inductive inferences in science.



The debate between John Stuart Mill (1806-1873) and William
Whewell (1794-1866) has become the canonical methodological
debate of the 19th century. Although often characterized as
a debate between inductivism and hypothetico-deductivism, the role of
the two methods on each side is actually more complex. On the
hypothetico-deductive account, scientists work to come up with
hypotheses from which true observational consequences can be
deduced--hence, hypothetico-deductive. Because Whewell emphasizes
both hypotheses and deduction in his account of method, he can be seen
as a convenient foil to the inductivism of Mill. However, equally if
not more important to Whewell's portrayal of scientific method
is what he calls the "fundamental antithesis". Knowledge
is a product of the objective (what we see in the world around us) and
subjective (the contributions of our mind to how we perceive and
understand what we experience, which he called the Fundamental Ideas).
Both elements are essential according to Whewell, and he was therefore
critical of Kant for too much focus on the subjective, and John Locke
(1632-1704) and Mill for too much focus on the senses.
Whewell's fundamental ideas can be discipline relative. An idea
can be fundamental even if it is necessary for knowledge only within a
given scientific discipline (e.g., chemical affinity for chemistry).
This distinguishes fundamental ideas from the forms and categories of
intuition of Kant. (See the entry on
 Whewell.)



Clarifying fundamental ideas would therefore be an essential part of
scientific method and scientific progress. Whewell called this process
"Discoverer's Induction". It was induction,
following Bacon or Newton, but Whewell sought to revive Bacon's
account by emphasising the role of ideas in the clear and careful
formulation of inductive hypotheses. Whewell's induction is not
merely the collecting of objective facts. The subjective plays a role
through what Whewell calls the Colligation of Facts, a creative act of
the scientist, the invention of a theory. A theory is then confirmed
by testing, where more facts are brought under the theory, called the
Consilience of Inductions. Whewell felt that this was the method by
which the true laws of nature could be discovered: clarification of
fundamental concepts, clever invention of explanations, and careful
testing. Mill, in his critique of Whewell, and others who have cast
Whewell as a fore-runner of the hypothetico-deductivist view, seem to
have under-estimated the importance of this discovery phase in
Whewell's understanding of method (Snyder 1997a,b, 1999).
Down-playing the discovery phase would come to characterize
methodology of the early 20th century (see
 section 3).



Mill, in his *System of Logic*, put forward a narrower view of
induction as the essence of scientific method. For Mill, induction is
the search first for regularities among events. Among those
regularities, some will continue to hold for further observations,
eventually gaining the status of laws. One can also look for
regularities among the laws discovered in a domain, i.e., for a law of
laws. Which "law law" will hold is time and discipline
dependent and open to revision. One example is the Law of Universal
Causation, and Mill put forward specific methods for identifying
causes--now commonly known as Mill's methods. These five
methods look for circumstances which are common among the phenomena of
interest, those which are absent when the phenomena are, or those for
which both vary together. Mill's methods are still seen as
capturing basic intuitions about experimental methods for finding the
relevant explanatory factors (*System of Logic* (1843), see
 Mill
 entry). The methods advocated by Whewell and Mill, in the end, look
similar. Both involve inductive generalization to covering laws. They
differ dramatically, however, with respect to the necessity of the
knowledge arrived at; that is, at the meta-methodological level (see
the entries on
 Whewell
 and
 Mill
 entries).


## 3. Logic of method and critical responses



The quantum and relativistic revolutions in physics in the early
20th century had a profound effect on methodology.
Conceptual foundations of both theories were taken to show the
defeasibility of even the most seemingly secure intuitions about
space, time and bodies. Certainty of knowledge about the natural world
was therefore recognized as unattainable. Instead a renewed empiricism
was sought which rendered science fallible but still rationally
justifiable.



Analyses of the reasoning of scientists emerged, according to which
the aspects of scientific method which were of primary importance were
the means of testing and confirming of theories. A distinction in
methodology was made between the contexts of discovery and
justification. The distinction could be used as a wedge between the
particularities of where and how theories or hypotheses are arrived
at, on the one hand, and the underlying reasoning scientists use
(whether or not they are aware of it) when assessing theories and
judging their adequacy on the basis of the available evidence. By and
large, for most of the 20th century, philosophy of science
focused on the second context, although philosophers differed on
whether to focus on confirmation or refutation as well as on the many
details of how confirmation or refutation could or could not be
brought about. By the mid-20th century these attempts at
defining the method of justification and the context distinction
itself came under pressure. During the same period, philosophy of
science developed rapidly, and from
 section 4
 this entry will therefore shift from a primarily historical treatment
of the scientific method towards a primarily thematic one.


### 3.1 Logical constructionism and Operationalism



Advances in logic and probability held out promise of the possibility
of elaborate reconstructions of scientific theories and empirical
method, the best example being Rudolf Carnap's *The Logical
Structure of the World* (1928). Carnap attempted to show that a
scientific theory could be reconstructed as a formal axiomatic
system--that is, a logic. That system could refer to the world
because some of its basic sentences could be interpreted as
observations or operations which one could perform to test them. The
rest of the theoretical system, including sentences using theoretical
or unobservable terms (like electron or force) would then either be
meaningful because they could be reduced to observations, or they had
purely logical meanings (called analytic, like mathematical
identities). This has been referred to as the verifiability criterion
of meaning. According to the criterion, any statement not either
analytic or verifiable was strictly meaningless. Although the view was
endorsed by Carnap in 1928, he would later come to see it as too
restrictive (Carnap 1956). Another familiar version of this idea is
operationalism of Percy William Bridgman. In *The Logic of Modern
Physics* (1927) Bridgman asserted that every physical concept
could be defined in terms of the operations one would perform to
verify the application of that concept. Making good on the
operationalisation of a concept even as simple as length, however, can
easily become enormously complex (for measuring very small lengths,
for instance) or impractical (measuring large distances like light
years.)



Carl Hempel's (1950, 1951) criticisms of the verifiability
criterion of meaning had enormous influence. He pointed out that
universal generalizations, such as most scientific laws, were not
strictly meaningful on the criterion. Verifiability and operationalism
both seemed too restrictive to capture standard scientific aims and
practice. The tenuous connection between these reconstructions and
actual scientific practice was criticized in another way. In both
approaches, scientific methods are instead recast in methodological
roles. Measurements, for example, were looked to as ways of giving
meanings to terms. The aim of the philosopher of science was not to
understand the methods *per se*, but to use them to reconstruct
theories, their meanings, and their relation to the world. When
scientists perform these operations, however, they will not report
that they are doing them to give meaning to terms in a formal
axiomatic system. This disconnect between methodology and the details
of actual scientific practice would seem to violate the empiricism the
Logical Positivists and Bridgman were committed to. The view that
methodology should correspond to practice (to some extent) has been
called historicism, or intuitionism. We turn to these criticisms and
responses in
 section 3.4.[4]



Positivism also had to contend with the recognition that a purely
inductivist approach, along the lines of Bacon-Newton-Mill, was
untenable. There was no pure observation, for starters. All
observation was theory laden. Theory is required to make any
observation, therefore not all theory can be derived from observation
alone. (See the entry on
 theory and observation in science.)
 Even granting an observational basis, Hume had already pointed out
that one could not deductively justify inductive conclusions without
begging the question by presuming the success of the inductive method.
Likewise, positivist attempts at analyzing how a generalization can be
confirmed by observations of its instances were subject to a number of
criticisms. Goodman (1965) and Hempel (1965) both point to paradoxes
inherent in standard accounts of confirmation. Recent attempts at
explaining how observations can serve to confirm a scientific theory
are discussed in
 section 4
 below.


### 3.2. H-D as a logic of confirmation



The standard starting point for a non-inductive analysis of the logic
of confirmation is known as the Hypothetico-Deductive (H-D) method. In
its simplest form, a sentence of a theory which expresses some
hypothesis is confirmed by its true consequences. As noted in
 section 2,
 this method had been advanced by Whewell in the 19th
century, as well as Nicod (1924) and others in the 20th
century. Often, Hempel's (1966) description of the H-D method,
illustrated by the case of Semmelweiss' inferential procedures
in establishing the cause of childbed fever, has been presented as a
key account of H-D as well as a foil for criticism of the H-D account
of confirmation (see, for example, Lipton's (2004) discussion of
inference to the best explanation; also the entry on
 confirmation).
 Hempel described Semmelsweiss' procedure as examining various
hypotheses explaining the cause of childbed fever. Some hypotheses
conflicted with observable facts and could be rejected as false
immediately. Others needed to be tested experimentally by deducing
which observable events should follow if the hypothesis were true
(what Hempel called the test implications of the hypothesis), then
conducting an experiment and observing whether or not the test
implications occurred. If the experiment showed the test implication
to be false, the hypothesis could be rejected. If the experiment
showed the test implications to be true, however, this did not prove
the hypothesis true. The confirmation of a test implication does not
verify a hypothesis, though Hempel did allow that "it provides
at least some support, some corroboration or confirmation for
it" (Hempel 1966: 8). The degree of this support then depends on
the quantity, variety and precision of the supporting evidence.


### 3.3. Popper and falsificationism



Another approach that took off from the difficulties with inductive
inference was
 Karl Popper's
 critical rationalism or falsificationism (Popper 1959, 1963).
Falsification is deductive and similar to H-D in that it involves
scientists deducing observational consequences from the hypothesis
under test. For Popper, however, the important point was not the
degree of confirmation that successful prediction offered to a
hypothesis. The crucial thing was the logical asymmetry between
confirmation, based on inductive inference, and falsification, which
can be based on a deductive inference. (This simple opposition was
later questioned, by Lakatos, among others. See the entry on
 historicist theories of scientific rationality.)



Popper stressed that, regardless of the amount of confirming evidence,
we can never be certain that a hypothesis is true without committing
the fallacy of affirming the consequent. Instead, Popper introduced
the notion of corroboration as a measure for how well a theory or
hypothesis has survived previous testing--but without implying
that this is also a measure for the probability that it is true.



Popper was also motivated by his doubts about the scientific status of
theories like the Marxist theory of history or psycho-analysis, and so
wanted to demarcate between science and pseudo-science. Popper saw
this as an importantly different distinction than demarcating science
from metaphysics. The latter demarcation was the primary concern of
many logical empiricists. Popper used the idea of falsification to
draw a line instead between pseudo and proper science. Science was
science because its method involved subjecting theories to rigorous
tests which offered a high probability of failing and thus refuting
the theory.



A commitment to the risk of failure was important. Avoiding
falsification could be done all too easily. If a consequence of a
theory is inconsistent with observations, an exception can be added by
introducing auxiliary hypotheses designed explicitly to save the
theory, so-called *ad hoc* modifications. This Popper saw done
in pseudo-science where ad hoc theories appeared capable of explaining
anything in their field of application. In contrast, science is risky.
If observations showed the predictions from a theory to be wrong, the
theory would be refuted. Hence, scientific hypotheses must be
falsifiable. Not only must there exist some possible observation
statement which could falsify the hypothesis or theory, were it
observed, (Popper called these the hypothesis' potential
falsifiers) it is crucial to the Popperian scientific method that such
falsifications be sincerely attempted on a regular basis.



The more potential falsifiers of a hypothesis, the more falsifiable it
would be, and the more the hypothesis claimed. Conversely, hypotheses
without falsifiers claimed very little or nothing at all. Originally,
Popper thought that this meant the introduction of *ad hoc*
hypotheses only to save a theory should not be countenanced as good
scientific method. These would undermine the falsifiabililty of a
theory. However, Popper later came to recognize that the introduction
of modifications (immunizations, he called them) was often an
important part of scientific development. Responding to surprising or
apparently falsifying observations often generated important new
scientific insights. Popper's own example was the observed
motion of Uranus which originally did not agree with Newtonian
predictions. The *ad hoc* hypothesis of an outer planet
explained the disagreement and led to further falsifiable predictions.
Popper sought to reconcile the view by blurring the distinction
between falsifiable and not falsifiable, and speaking instead of
degrees of testability (Popper 1985: 41f.).


### 3.4 Meta-methodology and the end of method



From the 1960s on, sustained meta-methodological criticism emerged
that drove philosophical focus away from scientific method. A brief
look at those criticisms follows, with recommendations for further
reading at the end of the entry.



Thomas Kuhn's *The Structure of Scientific Revolutions*
(1962) begins with a well-known shot across the bow for philosophers
of science:



> 
> 
> History, if viewed as a repository for more than anecdote or
> chronology, could produce a decisive transformation in the image of
> science by which we are now possessed. (1962: 1)
> 
> 
> 



The image Kuhn thought needed transforming was the a-historical,
rational reconstruction sought by many of the Logical Positivists,
though Carnap and other positivists were actually quite sympathetic to
Kuhn's views. (See the entry on the
 Vienna Circle.)
 Kuhn shares with other of his contemporaries, such as Feyerabend and
Lakatos, a commitment to a more empirical approach to philosophy of
science. Namely, the history of science provides important data, and
necessary checks, for philosophy of science, including any theory of
scientific method.



The history of science reveals, according to Kuhn, that scientific
development occurs in alternating phases. During normal science, the
members of the scientific community adhere to the paradigm in place.
Their commitment to the paradigm means a commitment to the puzzles to
be solved and the acceptable ways of solving them. Confidence in the
paradigm remains so long as steady progress is made in solving the
shared puzzles. Method in this normal phase operates within a
disciplinary matrix (Kuhn's later concept of a paradigm) which
includes standards for problem solving, and defines the range of
problems to which the method should be applied. An important part of a
disciplinary matrix is the set of values which provide the norms and
aims for scientific method. The main values that Kuhn identifies are
prediction, problem solving, simplicity, consistency, and
plausibility.



An important by-product of normal science is the accumulation of
puzzles which cannot be solved with resources of the current paradigm.
Once accumulation of these anomalies has reached some critical mass,
it can trigger a communal shift to a new paradigm and a new phase of
normal science. Importantly, the values that provide the norms and
aims for scientific method may have transformed in the meantime.
Method may therefore be relative to discipline, time or place



Feyerabend also identified the aims of science as progress, but argued
that any methodological prescription would only stifle that progress
(Feyerabend 1988). His arguments are grounded in re-examining accepted
"myths" about the history of science. Heroes of science,
like Galileo, are shown to be just as reliant on rhetoric and
persuasion as they are on reason and demonstration. Others, like
Aristotle, are shown to be far more reasonable and far-reaching in
their outlooks then they are given credit for. As a consequence, the
only rule that could provide what he took to be sufficient freedom was
the vacuous "anything goes". More generally, even the
methodological restriction that science is the best way to pursue
knowledge, and to increase knowledge, is too restrictive. Feyerabend
suggested instead that science might, in fact, be a threat to a free
society, because it and its myth had become so dominant (Feyerabend
1978).



An even more fundamental kind of criticism was offered by several
sociologists of science from the 1970s onwards who rejected the
methodology of providing philosophical accounts for the rational
development of science and sociological accounts of the irrational
mistakes. Instead, they adhered to a symmetry thesis on which any
causal explanation of how scientific knowledge is established needs to
be symmetrical in explaining truth and falsity, rationality and
irrationality, success and mistakes, by the same causal factors (see,
e.g., Barnes and Bloor 1982, Bloor 1991). Movements in the Sociology
of Science, like the Strong Programme, or in the social dimensions and
causes of knowledge more generally led to extended and close
examination of detailed case studies in contemporary science and its
history. (See the entries on
 the social dimensions of scientific knowledge
 and
 social epistemology.)
 Well-known examinations by Latour and Woolgar (1979/1986),
Knorr-Cetina (1981), Pickering (1984), Shapin and Schaffer (1985) seem
to bear out that it was social ideologies (on a macro-scale) or
individual interactions and circumstances (on a micro-scale) which
were the primary causal factors in determining which beliefs gained
the status of scientific knowledge. As they saw it therefore,
explanatory appeals to scientific method were not empirically
grounded.



A late, and largely unexpected, criticism of scientific method came
from within science itself. Beginning in the early 2000s, a number of
scientists attempting to replicate the results of published
experiments could not do so. There may be close conceptual connection
between reproducibility and method. For example, if reproducibility
means that the same scientific methods ought to produce the same
result, and all scientific results ought to be reproducible, then
whatever it takes to reproduce a scientific result ought to be called
scientific method. Space limits us to the observation that, insofar as
reproducibility is a desired outcome of proper scientific method, it
is not strictly a part of scientific method. (See the entry on
 reproducibility of scientific results.)



By the close of the 20th century the search for the
scientific method was flagging. Nola and Sankey (2000b) could
introduce their volume on method by remarking that "For some,
the whole idea of a theory of scientific method is yester-year's
debate ...".


## 4. Statistical methods for hypothesis testing



Despite the many difficulties that philosophers encountered in trying
to providing a clear methodology of conformation (or refutation),
still important progress has been made on understanding how
observation can provide evidence for a given theory. Work in
statistics has been crucial for understanding how theories can be
tested empirically, and in recent decades a huge literature has
developed that attempts to recast confirmation in Bayesian terms. Here
these developments can be covered only briefly, and we refer to the
entry on
 confirmation
 for further details and references.



Statistics has come to play an increasingly important role in the
methodology of the experimental sciences from the 19th
century onwards. At that time, statistics and probability theory took
on a methodological role as an analysis of inductive inference, and
attempts to ground the rationality of induction in the axioms of
probability theory have continued throughout the 20th
century and in to the present. Developments in the theory of
statistics itself, meanwhile, have had a direct and immense influence
on the experimental method, including methods for measuring the
uncertainty of observations such as the Method of Least Squares
developed by Legendre and Gauss in the early 19th century,
criteria for the rejection of outliers proposed by Peirce by the
mid-19th century, and the significance tests developed by
Gosset (a.k.a. "Student"), Fisher, Neyman & Pearson
and others in the 1920s and 1930s (see, e.g., Swijtink 1987 for a
brief historical overview; and also the entry on
 C.S. Peirce).



These developments within statistics then in turn led to a reflective
discussion among both statisticians and philosophers of science on how
to perceive the process of hypothesis testing: whether it was a
rigorous statistical inference that could provide a numerical
expression of the degree of confidence in the tested hypothesis, or if
it should be seen as a decision between different courses of actions
that also involved a value component. This led to a major controversy
among Fisher on the one side and Neyman and Pearson on the other (see
especially Fisher 1955, Neyman 1956 and Pearson 1955, and for analyses
of the controversy, e.g., Howie 2002, Marks 2000, Lenhard 2006). On
Fisher's view, hypothesis testing was a methodology for when to
accept or reject a statistical hypothesis, namely that a hypothesis
should be rejected by evidence if this evidence would be unlikely
relative to other possible outcomes, given the hypothesis were true.
In contrast, on Neyman and Pearson's view, the consequence of
error also had to play a role when deciding between hypotheses.
Introducing the distinction between the error of rejecting a true
hypothesis (type I error) and accepting a false hypothesis (type II
error), they argued that it depends on the consequences of the error
to decide whether it is more important to avoid rejecting a true
hypothesis or accepting a false one. Hence, Fisher aimed for a theory
of inductive inference that enabled a numerical expression of
confidence in a hypothesis. To him, the important point was the search
for truth, not utility. In contrast, the Neyman-Pearson approach
provided a strategy of inductive behaviour for deciding between
different courses of action. Here, the important point was not whether
a hypothesis was true, but whether one should act as if it was.



Similar discussions are found in the philosophical literature. On the
one side, Churchman (1948) and Rudner (1953) argued that because
scientific hypotheses can never be completely verified, a complete
analysis of the methods of scientific inference includes ethical
judgments in which the scientists must decide whether the evidence is
sufficiently strong or that the probability is sufficiently high to
warrant the acceptance of the hypothesis, which again will depend on
the importance of making a mistake in accepting or rejecting the
hypothesis. Others, such as Jeffrey (1956) and Levi (1960) disagreed
and instead defended a value-neutral view of science on which
scientists should bracket their attitudes, preferences, temperament,
and values when assessing the correctness of their inferences. For
more details on this value-free ideal in the philosophy of science and
its historical development, see Douglas (2009) and Howard (2003). For
a broad set of case studies examining the role of values in science,
see e.g. Elliott & Richards 2017.



In recent decades, philosophical discussions of the evaluation of
probabilistic hypotheses by statistical inference have largely focused
on Bayesianism that understands probability as a measure of a
person's degree of belief in an event, given the available
information, and frequentism that instead understands probability as a
long-run frequency of a repeatable event. Hence, for Bayesians
probabilities refer to a state of knowledge, whereas for frequentists
probabilities refer to frequencies of events (see, e.g., Sober 2008,
chapter 1 for a detailed introduction to Bayesianism and frequentism
as well as to likelihoodism). Bayesianism aims at providing a
quantifiable, algorithmic representation of belief revision, where
belief revision is a function of prior beliefs (i.e., background
knowledge) and incoming evidence. Bayesianism employs a rule based on
Bayes' theorem, a theorem of the probability calculus which
relates conditional probabilities. The probability that a particular
hypothesis is true is interpreted as a degree of belief, or credence,
of the scientist. There will also be a probability and a degree of
belief that a hypothesis will be true conditional on a piece of
evidence (an observation, say) being true. Bayesianism proscribes that
it is rational for the scientist to update their belief in the
hypothesis to that conditional probability should it turn out that the
evidence is, in fact, observed (see, e.g., Sprenger & Hartmann
2019 for a comprehensive treatment of Bayesian philosophy of science).
Originating in the work of Neyman and Person, frequentism aims at
providing the tools for reducing long-run error rates, such as the
error-statistical approach developed by Mayo (1996) that focuses on
how experimenters can avoid both type I and type II errors by building
up a repertoire of procedures that detect errors if and only if they
are present. Both Bayesianism and frequentism have developed over
time, they are interpreted in different ways by its various
proponents, and their relations to previous criticism to attempts at
defining scientific method are seen differently by proponents and
critics. The literature, surveys, reviews and criticism in this area
are vast and the reader is referred to the entries on
 Bayesian epistemology
 and
 confirmation.


## 5. Method in Practice



Attention to scientific practice, as we have seen, is not itself new.
However, the turn to practice in the philosophy of science of late can
be seen as a correction to the pessimism with respect to method in
philosophy of science in later parts of the 20th century,
and as an attempted reconciliation between sociological and
rationalist explanations of scientific knowledge. Much of this work
sees method as detailed and context specific problem-solving
procedures, and methodological analyses to be at the same time
descriptive, critical and advisory (see Nickles 1987 for an exposition
of this view). The following section contains a survey of some of the
practice focuses. In this section we turn fully to topics rather than
chronology.


### 5.1 Creative and exploratory practices



A problem with the distinction between the contexts of discovery and
justification that figured so prominently in philosophy of science in
the first half of the 20th century (see
 section 2)
 is that no such distinction can be clearly seen in scientific
activity (see Arabatzis 2006). Thus, in recent decades, it has been
recognized that study of conceptual innovation and change should not
be confined to psychology and sociology of science, but are also
important aspects of scientific practice which philosophy of science
should address (see also the entry on
 scientific discovery).
 Looking for the practices that drive conceptual innovation has led
philosophers to examine both the reasoning practices of scientists and
the wide realm of experimental practices that are not directed
narrowly at testing hypotheses, that is, exploratory
experimentation.



Examining the reasoning practices of historical and contemporary
scientists, Nersessian (2008) has argued that new scientific concepts
are constructed as solutions to specific problems by systematic
reasoning, and that of analogy, visual representation and
thought-experimentation are among the important reasoning practices
employed. These ubiquitous forms of reasoning are reliable--but
also fallible--methods of conceptual development and change. On
her account, model-based reasoning consists of cycles of construction,
simulation, evaluation and adaption of models that serve as interim
interpretations of the target problem to be solved. Often, this
process will lead to modifications or extensions, and a new cycle of
simulation and evaluation. However, Nersessian also emphasizes
that



> 
> 
> creative model-based reasoning cannot be applied as a simple recipe,
> is not always productive of solutions, and even its most exemplary
> usages can lead to incorrect solutions. (Nersessian 2008: 11)
> 
> 
> 



Thus, while on the one hand she agrees with many previous philosophers
that there is no logic of discovery, discoveries can derive from
reasoned processes, such that a large and integral part of scientific
practice is



> 
> 
> the creation of concepts through which to comprehend, structure, and
> communicate about physical phenomena .... (Nersessian 1987:
> 11)
> 
> 
> 



Similarly, work on heuristics for discovery and theory construction by
scholars such as Darden (1991) and Bechtel & Richardson (1993)
present science as problem solving and investigate scientific problem
solving as a special case of problem-solving in general. Drawing
largely on cases from the biological sciences, much of their focus has
been on reasoning strategies for the generation, evaluation, and
revision of mechanistic explanations of complex systems.



Addressing another aspect of the context distinction, namely the
traditional view that the primary role of experiments is to test
theoretical hypotheses according to the H-D model, other philosophers
of science have argued for additional roles that experiments can play.
The notion of exploratory experimentation was introduced to describe
experiments driven by the desire to obtain empirical regularities and
to develop concepts and classifications in which these regularities
can be described (Steinle 1997, 2002; Burian 1997; Waters 2007)).
However the difference between theory driven experimentation and
exploratory experimentation should not be seen as a sharp distinction.
Theory driven experiments are not always directed at testing
hypothesis, but may also be directed at various kinds of
fact-gathering, such as determining numerical parameters. *Vice
versa*, exploratory experiments are usually informed by theory in
various ways and are therefore not theory-free. Instead, in
exploratory experiments phenomena are investigated without first
limiting the possible outcomes of the experiment on the basis of
extant theory about the phenomena.



The development of high throughput instrumentation in molecular
biology and neighbouring fields has given rise to a special type of
exploratory experimentation that collects and analyses very large
amounts of data, and these new 'omics' disciplines are
often said to represent a break with the ideal of hypothesis-driven
science (Burian 2007; Elliott 2007; Waters 2007; O'Malley 2007)
and instead described as data-driven research (Leonelli 2012; Strasser
2012) or as a special kind of "convenience
experimentation" in which many experiments are done simply
because they are extraordinarily convenient to perform (Krohs
2012).


### 5.2 Computer methods and 'new ways' of doing science



The field of omics just described is possible because of the ability
of computers to process, in a reasonable amount of time, the huge
quantities of data required. Computers allow for more elaborate
experimentation (higher speed, better filtering, more variables,
sophisticated coordination and control), but also, through modelling
and simulations, might constitute a form of experimentation
themselves. Here, too, we can pose a version of the general question
of method versus practice: does the practice of using computers
fundamentally change scientific method, or merely provide a more
efficient means of implementing standard methods?



Because computers can be used to automate measurements,
quantifications, calculations, and statistical analyses where, for
practical reasons, these operations cannot be otherwise carried out,
many of the steps involved in reaching a conclusion on the basis of an
experiment are now made inside a "black box", without the
direct involvement or awareness of a human. This has epistemological
implications, regarding what we can know, and how we can know it. To
have confidence in the results, computer methods are therefore
subjected to tests of verification and validation.



The distinction between verification and validation is easiest to
characterize in the case of computer simulations. In a typical
computer simulation scenario computers are used to numerically
integrate differential equations for which no analytic solution is
available. The equations are part of the model the scientist uses to
represent a phenomenon or system under investigation. Verifying a
computer simulation means checking that the equations of the model are
being correctly approximated. Validating a simulation means checking
that the equations of the model are adequate for the inferences one
wants to make on the basis of that model.



A number of issues related to computer simulations have been raised.
The identification of validity and verification as the testing methods
has been criticized. Oreskes et al. (1994) raise concerns that
"validiation", because it suggests deductive inference,
might lead to over-confidence in the results of simulations. The
distinction itself is probably too clean, since actual practice in the
testing of simulations mixes and moves back and forth between the two
(Weissart 1997; Parker 2008a; Winsberg 2010). Computer simulations do
seem to have a non-inductive character, given that the principles by
which they operate are built in by the programmers, and any results of
the simulation follow from those in-built principles in such a way
that those results could, in principle, be deduced from the program
code and its inputs. The status of simulations as experiments has
therefore been examined (Kaufmann and Smarr 1993; Humphreys 1995;
Hughes 1999; Norton and Suppe 2001). This literature considers the
epistemology of these experiments: what we can learn by simulation,
and also the kinds of justifications which can be given in applying
that knowledge to the "real" world. (Mayo 1996; Parker
2008b). As pointed out, part of the advantage of computer simulation
derives from the fact that huge numbers of calculations can be carried
out without requiring direct observation by the
experimenter/ simulator. At the same time, many of these
calculations are approximations to the calculations which would be
performed first-hand in an ideal situation. Both factors introduce
uncertainties into the inferences drawn from what is observed in the
simulation.



For many of the reasons described above, computer simulations do not
seem to belong clearly to either the experimental or theoretical
domain. Rather, they seem to crucially involve aspects of both. This
has led some authors, such as Fox Keller (2003: 200) to argue that we
ought to consider computer simulation a "qualitatively different
way of doing science". The literature in general tends to follow
Kaufmann and Smarr (1993) in referring to computer simulation as a
"third way" for scientific methodology (theoretical
reasoning and experimental practice are the first two ways.). It
should also be noted that the debates around these issues have tended
to focus on the form of computer simulation typical in the physical
sciences, where models are based on dynamical equations. Other forms
of simulation might not have the same problems, or have problems of
their own (see the entry on
 computer simulations in science).



In recent years, the rapid development of machine learning techniques
has prompted some scholars to suggest that the scientific method has
become "obsolete" (Anderson 2008, Carrol and Goodstein
2009). This has resulted in an intense debate on the relative merit of
data-driven and hypothesis-driven research (for samples, see e.g.
Mazzocchi 2015 or Succi and Coveney 2018). For a detailed treatment of
this topic, we refer to the entry
 scientific research and big data.


## 6. Discourse on scientific method



Despite philosophical disagreements, the idea of *the*
scientific method still figures prominently in contemporary discourse
on many different topics, both within science and in society at large.
Often, reference to scientific method is used in ways that convey
either the legend of a single, universal method characteristic of all
science, or grants to a particular method or set of methods privilege
as a special 'gold standard', often with reference to
particular philosophers to vindicate the claims. Discourse on
scientific method also typically arises when there is a need to
distinguish between science and other activities, or for justifying
the special status conveyed to science. In these areas, the
philosophical attempts at identifying a set of methods characteristic
for scientific endeavors are closely related to the philosophy of
science's classical problem of demarcation (see the entry on
 science and pseudo-science)
 and to the philosophical analysis of the social dimension of
scientific knowledge and the role of science in democratic
society.


### 6.1 "The scientific method" in science education and as seen by scientists



One of the settings in which the legend of a single, universal
scientific method has been particularly strong is science education
(see, e.g., Bauer 1992; McComas 1996; Wivagg & Allchin
 2002).[5]
 Often, 'the scientific method' is presented in textbooks
and educational web pages as a fixed four or five step procedure
starting from observations and description of a phenomenon and
progressing over formulation of a hypothesis which explains the
phenomenon, designing and conducting experiments to test the
hypothesis, analyzing the results, and ending with drawing a
conclusion. Such references to a universal scientific method can be
found in educational material at all levels of science education
(Blachowicz 2009), and numerous studies have shown that the idea of a
general and universal scientific method often form part of both
students' and teachers' conception of science (see, e.g.,
Aikenhead 1987; Osborne et al. 2003). In response, it has been argued
that science education need to focus more on teaching about the nature
of science, although views have differed on whether this is best done
through student-led investigations, contemporary cases, or historical
cases (Allchin, Andersen & Nielsen 2014)



Although occasionally phrased with reference to the H-D method,
important historical roots of the legend in science education of a
single, universal scientific method are the American philosopher and
psychologist Dewey's account of inquiry in *How We Think*
(1910) and the British mathematician Karl Pearson's account of
science in *Grammar of Science* (1892). On Dewey's
account, inquiry is divided into the five steps of



> 
> 
> (i) a felt difficulty, (ii) its location and definition, (iii)
> suggestion of a possible solution, (iv) development by reasoning of
> the bearing of the suggestions, (v) further observation and experiment
> leading to its acceptance or rejection. (Dewey 1910: 72)
> 
> 
> 



Similarly, on Pearson's account, scientific investigations start
with measurement of data and observation of their correction and
sequence from which scientific laws can be discovered with the aid of
creative imagination. These laws have to be subject to criticism, and
their final acceptance will have equal validity for "all
normally constituted minds". Both Dewey's and
Pearson's accounts should be seen as generalized abstractions of
inquiry and not restricted to the realm of science--although both
Dewey and Pearson referred to their respective accounts as 'the
scientific method'.



Occasionally, scientists make sweeping statements about a simple and
distinct scientific method, as exemplified by Feynman's
simplified version of a conjectures and refutations method presented,
for example, in the last of his 1964 Cornell Messenger
 lectures.[6]
 However, just as often scientists have come to the same conclusion as
recent philosophy of science that there is not any unique, easily
described scientific method. For example, the physicist and Nobel
Laureate Weinberg described in the paper "The Methods of Science
... And Those By Which We Live" (1995) how



> 
> 
> The fact that the standards of scientific success shift with time does
> not only make the philosophy of science difficult; it also raises
> problems for the public understanding of science. We do not have a
> fixed scientific method to rally around and defend. (1995: 8)
> 
> 
> 



Interview studies with scientists on their conception of method shows
that scientists often find it hard to figure out whether available
evidence confirms their hypothesis, and that there are no direct
translations between general ideas about method and specific
strategies to guide how research is conducted (Schickore & Hangel
2019, Hangel & Schickore 2017)


### 6.2 Privileged methods and 'gold standards'



Reference to the scientific method has also often been used to argue
for the scientific nature or special status of a particular activity.
Philosophical positions that argue for a simple and unique scientific
method as a criterion of demarcation, such as Popperian falsification,
have often attracted practitioners who felt that they had a need to
defend their domain of practice. For example, references to
conjectures and refutation as the scientific method are abundant in
much of the literature on complementary and alternative medicine
(CAM)--alongside the competing position that CAM, as an
alternative to conventional biomedicine, needs to develop its own
methodology different from that of science.



Also within mainstream science, reference to the scientific method is
used in arguments regarding the internal hierarchy of disciplines and
domains. A frequently seen argument is that research based on the H-D
method is superior to research based on induction from observations
because in deductive inferences the conclusion follows necessarily
from the premises. (See, e.g., Parascandola 1998 for an analysis of
how this argument has been made to downgrade epidemiology compared to
the laboratory sciences.) Similarly, based on an examination of the
practices of major funding institutions such as the National
Institutes of Health (NIH), the National Science Foundation (NSF) and
the Biomedical Sciences Research Practices (BBSRC) in the UK,
O'Malley et al. (2009) have argued that funding agencies seem to
have a tendency to adhere to the view that the primary activity of
science is to test hypotheses, while descriptive and exploratory
research is seen as merely preparatory activities that are valuable
only insofar as they fuel hypothesis-driven research.



In some areas of science, scholarly publications are structured in a
way that may convey the impression of a neat and linear process of
inquiry from stating a question, devising the methods by which to
answer it, collecting the data, to drawing a conclusion from the
analysis of data. For example, the codified format of publications in
most biomedical journals known as the IMRAD format (Introduction,
Method, Results, Analysis, Discussion) is explicitly described by the
journal editors as "not an arbitrary publication format but
rather a direct reflection of the process of scientific
discovery" (see the so-called "Vancouver
Recommendations", ICMJE 2013: 11). However, scientific
publications do not in general reflect the process by which the
reported scientific results were produced. For example, under the
provocative title "Is the scientific paper a fraud?",
Medawar argued that scientific papers generally misrepresent how the
results have been produced (Medawar 1963/1996). Similar views have
been advanced by philosophers, historians and sociologists of science
(Gilbert 1976; Holmes 1987; Knorr-Cetina 1981; Schickore 2008; Suppe
1998) who have argued that scientists' experimental practices
are messy and often do not follow any recognizable pattern.
Publications of research results, they argue, are retrospective
reconstructions of these activities that often do not preserve the
temporal order or the logic of these activities, but are instead often
constructed in order to screen off potential criticism (see Schickore
2008 for a review of this work).


### 6.3 Scientific method in the court room



Philosophical positions on the scientific method have also made it
into the court room, especially in the US where judges have drawn on
philosophy of science in deciding when to confer special status to
scientific expert testimony. A key case is *Daubert vs Merrell Dow
Pharmaceuticals* (92-102, 509 U.S. 579, 1993). In this case,
the Supreme Court argued in its 1993 ruling that trial judges must
ensure that expert testimony is reliable, and that in doing this the
court must look at the expert's methodology to determine whether
the proffered evidence is actually scientific knowledge. Further,
referring to works of Popper and Hempel the court stated that



> 
> 
> ordinarily, a key question to be answered in determining whether a
> theory or technique is scientific knowledge ... is whether it can
> be (and has been) tested. (Justice Blackmun, Daubert v. Merrell Dow
> Pharmaceuticals; see Other Internet Resources for a link to the
> opinion)
> 
> 
> 



But as argued by Haack (2005a,b, 2010) and by Foster & Hubner
(1999), by equating the question of whether a piece of testimony is
reliable with the question whether it is scientific as indicated by a
special methodology, the court was producing an inconsistent mixture
of Popper's and Hempel's philosophies, and this has later
led to considerable confusion in subsequent case rulings that drew on
the Daubert case (see Haack 2010 for a detailed exposition).


### 6.4 Deviating practices



The difficulties around identifying the methods of science are also
reflected in the difficulties of identifying scientific misconduct in
the form of improper application of the method or methods of science.
One of the first and most influential attempts at defining misconduct
in science was the US definition from 1989 that defined misconduct
as



> 
> 
> fabrication, falsification, plagiarism, *or other practices that
> seriously deviate from those that are commonly accepted within the
> scientific community*. (Code of Federal Regulations, part 50,
> subpart A., August 8, 1989, italics added)
> 
> 
> 



However, the "other practices that seriously deviate"
clause was heavily criticized because it could be used to suppress
creative or novel science. For example, the National Academy of
Science stated in their report *Responsible Science* (1992)
that it



> 
> 
> wishes to discourage the possibility that a misconduct complaint could
> be lodged against scientists based solely on their use of novel or
> unorthodox research methods. (NAS: 27)
> 
> 
> 



This clause was therefore later removed from the definition. For an
entry into the key philosophical literature on conduct in science, see
Shamoo & Resnick (2009).


## 7. Conclusion



The question of the source of the success of science has been at the
core of philosophy since the beginning of modern science. If viewed as
a matter of epistemology more generally, scientific method is a part
of the entire history of philosophy. Over that time, science and
whatever methods its practitioners may employ have changed
dramatically. Today, many philosophers have taken up the banners of
pluralism or of practice to focus on what are, in effect, fine-grained
and contextually limited examinations of scientific method. Others
hope to shift perspectives in order to provide a renewed general
account of what characterizes the activity we call science.



One such perspective has been offered recently by Hoyningen-Huene
(2008, 2013), who argues from the history of philosophy of science
that after three lengthy phases of characterizing science by its
method, we are now in a phase where the belief in the existence of a
positive scientific method has eroded and what has been left to
characterize science is only its fallibility. First was a phase from
Plato and Aristotle up until the 17th century where the
specificity of scientific knowledge was seen in its absolute certainty
established by proof from evident axioms; next was a phase up to the
mid-19th century in which the means to establish the
certainty of scientific knowledge had been generalized to include
inductive procedures as well. In the third phase, which lasted until
the last decades of the 20th century, it was recognized
that empirical knowledge was fallible, but it was still granted a
special status due to its distinctive mode of production. But now in
the fourth phase, according to Hoyningen-Huene, historical and
philosophical studies have shown how "scientific methods with
the characteristics as posited in the second and third phase do not
exist" (2008: 168) and there is no longer any consensus among
philosophers and historians of science about the nature of science.
For Hoyningen-Huene, this is too negative a stance, and he therefore
urges the question about the nature of science anew. His own answer to
this question is that "scientific knowledge differs from other
kinds of knowledge, especially everyday knowledge, primarily by being
more systematic" (Hoyningen-Huene 2013: 14). Systematicity can
have several different dimensions: among them are more systematic
descriptions, explanations, predictions, defense of knowledge claims,
epistemic connectedness, ideal of completeness, knowledge generation,
representation of knowledge and critical discourse. Hence, what
characterizes science is the greater care in excluding possible
alternative explanations, the more detailed elaboration with respect
to data on which predictions are based, the greater care in detecting
and eliminating sources of error, the more articulate connections to
other pieces of knowledge, etc. On this position, what characterizes
science is not that the methods employed are unique to science, but
that the methods are more carefully employed.



Another, similar approach has been offered by Haack (2003). She sets
off, similar to Hoyningen-Huene, from a dissatisfaction with the
recent clash between what she calls Old Deferentialism and New
Cynicism. The Old Deferentialist position is that science progressed
inductively by accumulating true theories confirmed by empirical
evidence or deductively by testing conjectures against basic
statements; while the New Cynics position is that science has no
epistemic authority and no uniquely rational method and is merely just
politics. Haack insists that contrary to the views of the New Cynics,
there are objective epistemic standards, and there is something
epistemologically special about science, even though the Old
Deferentialists pictured this in a wrong way. Instead, she offers a
new Critical Commonsensist account on which standards of good, strong,
supportive evidence and well-conducted, honest, thorough and
imaginative inquiry are not exclusive to the sciences, but the
standards by which we judge all inquirers. In this sense, science does
not differ in kind from other kinds of inquiry, but it may differ in
the degree to which it requires broad and detailed background
knowledge and a familiarity with a technical vocabulary that only
specialists may possess.