## 1. Introduction



Objectivity is a value. To call a thing objective implies that it has
a certain importance to us and that we approve of it. Objectivity
comes in degrees. Claims, methods, results, and scientists can be more
or less objective, and, other things being equal, the more objective,
the better. Using the term "objective" to describe
something often carries a special rhetorical force with it. The
admiration of science among the general public and the authority
science enjoys in public life stems to a large extent from the view
that science is objective or at least more objective than other modes
of inquiry. Understanding scientific objectivity is therefore central
to understanding the nature of science and the role it plays in
society.



If what is so great about science is its objectivity, then objectivity
should be worth defending. The close examinations of scientific
practice that philosophers of science have undertaken in the past
fifty years have shown, however, that several conceptions of the ideal
of objectivity are either questionable or unattainable. The prospects
for a science providing a non-perspectival "view from
nowhere" or for proceeding in a way uninformed by human goals
and values are fairly slim, for example.



This article discusses several proposals to characterize the idea and
ideal of objectivity in such a way that it is both strong enough to be
valuable, and weak enough to be attainable and workable in practice.
We begin with a natural conception of objectivity:
**faithfulness to facts**. We motivate the intuitive
appeal of this conception, discuss its relation to scientific method
and discuss arguments challenging both its attainability as well as
its desirability. We then move on to a second conception of
objectivity as **absence of normative commitments and
value-freedom**, and once more we contrast arguments in favor
of such a conception with the challenges it faces. A third conception
of objectivity which we discuss at length is the idea of
**absence of personal bias**.



Finally there is the idea that objectivity is anchored in
**scientific communities and their practices**. After
discussing three **case studies** from economics, social
science and medicine, we address the **conceptual unity of
scientific objectivity**: Do the various conceptions have a
common valid core, such as promoting trust in science or minimizing
relevant epistemic risks? Or are they rivaling and only loosely
related accounts? Finally we present some conjectures about what
aspects of objectivity remain defensible and desirable in the light of
the difficulties we have encountered.


## 2. Objectivity as Faithfulness to Facts



The basic idea of this first conception of objectivity is that
scientific claims are objective in so far as they faithfully describe
facts about the world. The philosophical rationale underlying this
conception of objectivity is the view that there are facts "out
there" in the world and that it is the task of scientists to
discover, analyze, and systematize these facts.
"Objective" then becomes a success word: if a claim is
objective, it correctly describes some aspect of the world.



In this view, science is objective to the degree that it succeeds at
discovering and generalizing facts, abstracting from the perspective
of the individual scientist. Although few philosophers have fully
endorsed such a conception of scientific objectivity, the idea figures
recurrently in the work of prominent twentieth-century philosophers of
science such as Carnap, Hempel, Popper, and Reichenbach.


### 2.1 The View From Nowhere



Humans experience the world from a perspective. The contents of an
individual's experiences vary greatly with his perspective,
which is affected by his personal situation, and the details of his
perceptual apparatus, language and culture. While the experiences
vary, there seems to be something that remains constant. The
appearance of a tree will change as one approaches it
but--according to common sense and most philosophers--the
tree itself doesn't. A room may feel hot or cold for different
persons, but its temperature is independent of their experiences. The
object in front of me does not disappear just because the lights are
turned off.



These examples motivate a distinction between qualities that vary with
one's perspective, and qualities that remain constant through
changes of perspective. The latter are the objective qualities. Thomas
Nagel explains that we arrive at the idea of objective qualities in
three steps (Nagel 1986: 14). The first step is to realize (or
postulate) that our perceptions are caused by the actions of things
around us, through their effects on our bodies. The second step is to
realize (or postulate) that since the same qualities that cause
perceptions in us also have effects on other things and can exist
without causing any perceptions at all, their true nature must be
detachable from their perspectival appearance and need not resemble
it. The final step is to form a conception of that "true
nature" independently of any perspective. Nagel calls that
conception the "view from nowhere", Bernard Williams the
"absolute conception" (Williams 1985 [2011]). It
represents the world as it is, unmediated by human minds and other
"distortions".



This absolute conception lies at the basis of scientific realism (for
a detailed discussion, see the entry on
 scientific realism)
 and it is attractive in so far as it provides a basis for arbitrating
between conflicting viewpoints (e.g., two different observations).
Moreover, the absolute conception provides a simple and unified
account of the world. Theories of trees will be very hard to come by
if they use predicates such as "height as seen by an
observer" and a hodgepodge if their predicates track the habits
of ordinary language users rather than the properties of the world. To
the extent, then, that science aims to provide explanations for
natural phenomena, casting them in terms of the absolute conception
would help to realize this aim. A scientific account cast in the
language of the absolute conception may not only be able to explain
why a tree is as tall as it is but also why we see it in one way when
viewed from one standpoint and in a different way when viewed from
another. As Williams (1985 [2011: 139]) puts it,



> 
> 
> [the absolute conception] nonvacuously explain[s] how it itself, and
> the various perspectival views of the world, are possible.
> 
> 
> 



A third reason to find the view from nowhere attractive is that if the
world came in structures as characterized by it and we did have access
to it, we could use our knowledge of it to ground predictions (which,
to the extent that our theories do track the absolute structures, will
be borne out). A fourth and related reason is that attempts to
manipulate and control phenomena can similarly be grounded in our
knowledge of these structures. To attain any of the four
purposes--settling disagreements, explaining the world,
predicting phenomena, and manipulation and control--the absolute
conception is at best sufficient but not necessary. We can, for
instance, settle disagreements by imposing the rule that the person
with higher social rank or greater experience is always right. We can
explain the world and our image of it by means of theories that do not
represent absolute structures and properties, and there is no need to
get things (absolutely) right in order to predict successfully.
Nevertheless, there is something appealing in the idea that factual
disagreements can be settled by the very facts themselves, that
explanations and predictions grounded in what's really there
rather than in a distorted image of it.



No matter how desirable, our ability to use scientific claims to
represent facts about the world depends on whether these claims can
unambiguously be established on the basis of evidence, and of evidence
alone. Alas, the relation between evidence and scientific hypothesis
is not straightforward.
 Subsection 2.2
 and
 subsection 2.3
 will look at two challenges of the idea that even the best scientific
method will yield claims that describe an aperspectival view from
nowhere.
 Section 5.2
 will deal with socially motivated criticisms of the view from
nowhere.


### 2.2 Theory-Ladenness and Incommensurability



According to a popular picture, all scientific theories are false and
imperfect. Yet, as we add true and eliminate false beliefs, our best
scientific theories become more *truthlike* (e.g., Popper 1963,
1972). If this picture is correct, then scientific knowledge grows by
gradually approaching the truth and it will become more objective over
time, that is, more faithful to facts. However, scientific theories
often change, and sometimes several theories compete for the place of
the best scientific account of the world.



It is inherent in the above picture of scientific objectivity that
observations can, at least in principle, decide between competing
theories. If they did not, the conception of objectivity as
faithfulness would be pointless to have as we would not be in a
position to verify it. This position has been adopted by Karl R.
Popper, Rudolf Carnap and other leading figures in (broadly)
empiricist philosophy of science. Many philosophers have argued that
the relation between observation and theory is way more complex and
that influences can actually run both ways (e.g., Duhem 1906 [1954];
Wittgenstein 1953 [2001]). The most lasting criticism, however, was
delivered by Thomas S. Kuhn (1962 [1970]) in his book "The
Structure of Scientific Revolutions".



Kuhn's analysis is built on the assumption that scientists
always view research problems through the lens of a paradigm, defined
by set of relevant problems, axioms, methodological presuppositions,
techniques, and so forth. Kuhn provided several historical examples in
favor of this claim. Scientific progress--and the practice of
normal, everyday science--happens within a paradigm that guides
the individual scientists' puzzle-solving work and that sets the
community standards.



Can observations undermine such a paradigm, and speak for a different
one? Here, Kuhn famously stresses that **observations are
"theory-laden"** (cf. also Hanson 1958): they
depend on a body of theoretical assumptions through which they are
perceived and conceptualized. This hypothesis has two important
aspects.



First, the *meaning* of observational concepts is influenced by
theoretical assumptions and presuppositions. For example, the concepts
"mass" and "length" have different meanings in
Newtonian and relativistic mechanics; so does the concept
"temperature" in thermodynamics and statistical mechanics
(cf. Feyerabend 1962). In other words, Kuhn denies that there is a
theory-independent observation language. The "faithfulness to
reality" of an observation report is always mediated by a
theoretical *uberbau*, disabling the role of observation
reports as an impartial, merely fact-dependent arbiter between
different theories.



Second, not only the observational concepts, but also the
*perception* of a scientist depends on the paradigm she is
working in.



> 
> 
> Practicing in different worlds, the two groups of scientists [who work
> in different paradigms, J.R./J.S.] see different things when they look
> from the same point in the same direction. (Kuhn 1962 [1970: 150])
> 
> 
> 



That is, our own sense data are shaped and structured by a theoretical
framework, and may be fundamentally distinct from the sense data of
scientists working in another one. Where a Ptolemaic astronomer like
Tycho Brahe sees a sun setting behind the horizon, a Copernican
astronomer like Johannes Kepler sees the horizon moving up to a
stationary sun. If this picture is correct, then it is hard to assess
which theory or paradigm is more faithful to the facts, that is, more
objective.



The thesis of the theory-ladenness of observation has also been
extended to the **incommensurability of different paradigms or
scientific theories**, problematized independently by Thomas S.
Kuhn (1962 [1970]) and Paul Feyerabend (1962). Literally, this concept
means "having no measure in common", and it figures
prominently in arguments against a linear and standpoint-independent
picture of scientific progress. For instance, the Special Theory of
Relativity appears to be more faithful to the facts and therefore more
objective than Newtonian mechanics because it reduces, for low speeds,
to the latter, and it accounts for some additional facts that are not
predicted correctly by Newtonian mechanics. This picture is
undermined, however, by two central aspects of incommensurability.
First, not only do the observational concepts in both theories differ,
but the principles for specifying their meaning may be inconsistent
with each other (Feyerabend 1975: 269-270). Second, scientific
research methods and standards of evaluation change with the theories
or paradigms. Not all puzzles that could be tackled in the old
paradigm will be solved by the new one--this is the phenomenon of
"Kuhn loss".



A meaningful use of objectivity presupposes, according to Feyerabend,
to perceive and to describe the world from a specific perspective,
e.g., when we try to verify the referential claims of a scientific
theory. Only *within* a peculiar scientific worldview, the
concept of objectivity may be applied meaningfully. That is,
scientific method cannot free itself from the particular scientific
theory to which it is applied; the door to standpoint-independence is
locked. As Feyerabend puts it:



> 
> 
> our epistemic activities may have a decisive influence even upon the
> most solid piece of cosmological furniture--they make gods
> disappear and replace them by heaps of atoms in empty space. (1978:
> 70)
> 
> 
> 



Kuhn and Feyerabend's theses about theory-ladenness of
observation, and their implications for the objectivity of scientific
inquiry have been much debated afterwards, and have often been
misunderstood in a social constructivist sense. Therefore Kuhn later
returned to the topic of scientific objectivity, of which he gives his
own characterization in terms of the shared cognitive values of a
scientific community. We discuss Kuhn's later view in
 section 3.1.
 For a more thorough coverage, see the entries on
 theory and observation in science,
 the incommensurability of scientific theories and
 Thomas S. Kuhn.


### 2.3 Underdetermination, Values, and the Experimenters' Regress



Scientific theories are tested by comparing their implications with
the results of observations and experiments. Unfortunately, neither
positive results (when the theory's predictions are borne out in
the data) nor negative results (when they are not) allow unambiguous
inferences about the theory. A positive result can obtain even though
the theory is false, due to some alternative that makes the same
predictions. Finding suspect Jones' fingerprints on the murder
weapon is consistent with his innocence because he might have used it
as a kitchen knife. A negative result might be due not to the
falsehood of the theory under test but due to the failing of one or
more auxiliary assumptions needed to derive a prediction from the
theory. Testing, let us say, the implications of Newton's laws
for movements in our planetary system against observations requires
assumptions about the number of planets, the sun's and the
planets' masses, the extent to which the earth's
atmosphere refracts light beams, how telescopes affect the results and
so on. Any of these may be false, explaining an inconsistency. The
*locus classicus* for these observations is Pierre
Duhem's *The Aim and Structure of Physical Theory* (Duhem
1906 [1954]). Duhem concluded that there was no "crucial
experiment", an experiment that conclusively decides between two
alternative theories, in physics (1906 [1954: 188ff.]), and that
physicists had to employ their expert judgment or what Duhem called
"good sense" to determine what an experimental result
means for the truth or falsehood of a theory (1906 [1954:
216ff.]).



In other words, there is a gap between the evidence and the theory
supported by it. It is important to note that the alleged gap is more
profound than the gap between the premisses of *any* inductive
argument and its conclusion, say, the gap between "All hitherto
observed ravens have been black" and "All ravens are
black". The latter gap could be bridged by an agreed upon rule
of inductive reasoning. Alas, all attempts to find an analogous rule
for theory choice have failed (e.g., Norton 2003). Various
philosophers, historians, and sociologists of science have responded
that theory appraisal is "a complex form of value
judgment" (McMullin 1982: 701; see also Kuhn 1977; Hesse 1980;
Bloor 1982).



In
 section 3.1
 below we will discuss the nature of the value judgments in more
detail. For now the important lesson is that if these philosophers,
historians, and sociologists are correct, the "faithfulness to
facts" ideal is untenable. As the scientific image of the world
is a joint product of the facts and scientists' value judgments,
that image cannot be said to be aperspectival. Science does not eschew
the human perspective. There are of course ways to escape this
conclusion. If, as John Norton (2003; ms.--see Other Internet
Resources) has argued, it is material facts that power and justify
inductive inferences, and not value judgments, we can avoid the
negative conclusion regarding the view from nowhere. Unsurprisingly,
Norton is also critical of the idea that evidence generally
underdetermines theory (Norton 2008). However, there are good reasons
to mistrust Norton's optimism regarding the ineliminability of
values and other non-factual elements in inductive inferences (Reiss
2020).



There is another, closely related concern. Most of the earlier critics
of "objective" verification or falsification focused on
the relation between evidence and scientific theories. There is a
sense in which the claim that this relation is problematic is not so
surprising. Scientific theories contain highly abstract claims that
describe states of affairs far removed from the immediacy of sense
experience. This is for a good reason: sense experience is necessarily
perspectival, so to the extent to which scientific theories are to
track the absolute conception, they must describe a world different
from that of sense experience. But surely, one might think, the
evidence itself is objective. So even if we do have reasons to doubt
that abstract theories faithfully represent the world, we should stand
on firmer grounds when it comes to the evidence against which we test
abstract theories.



Theories are seldom tested against brute observations, however. Simple
generalizations such as "all swans are white" are directly
learned from observations (say, of the color of swans) but they do not
represent the view from nowhere (for one thing, the view from nowhere
doesn't have colors). Genuine scientific theories are tested
against experimental facts or phenomena, which are themselves
unobservable to the unaided senses. Experimental facts or phenomena
are instead established using intricate procedures of measurement and
experimentation.



We therefore need to ask whether the results of scientific
measurements and experiments can be aperspectival. In an important
debate in the 1980s and 1990s some commentators answered that question
with a resounding "no", which was then rebutted by others.
The debate concerns the so-called "experimenter's
regress" (Collins 1985). Collins, a prominent sociologist of
science, claims that in order to know whether an experimental result
is correct, one first needs to know whether the apparatus producing
the result is reliable. But one doesn't know whether the
apparatus is reliable unless one knows that it produces correct
results in the first place and so on and so on *ad infinitum*.
Collins' main case concerns attempts to detect gravitational
waves, which were very controversially discussed among physicists in
the 1970s.



Collins argues that the circle is eventually broken not by the
"facts" themselves but rather by factors having to do with
the scientist's career, the social and cognitive interests of
his community, and the expected fruitfulness for future work. It is
important to note that in Collins's view these factors do not
necessarily make scientific results arbitrary. But what he does argue
is that the experimental results do not represent the world according
to the absolute conception. Rather, they are produced jointly by the
world, scientific apparatuses, and the psychological and sociological
factors mentioned above. The facts and phenomena of science are
therefore necessarily perspectival.



In a series of contributions, Allan Franklin, a
physicist-turned-philosopher of science, has tried to show that while
there are indeed no algorithmic procedures for establishing
experimental facts, disagreements can nevertheless be settled by
*reasoned judgment* on the basis of *bona fide*
epistemological criteria such as experimental checks and calibration,
elimination of possible sources of error, using apparatuses based on
well-corroborated theory and so on (Franklin 1994, 1997). Collins
responds that "reasonableness" is a social category that
is not drawn from physics (Collins 1994).



The main issue for us in this debate is whether there are any reasons
to believe that experimental results provide an aperspectival view on
the world. According to Collins, experimental results are
co-determined by the facts as well as social and psychological
factors. According to Franklin, whatever else influences experimental
results other than facts is not arbitrary but instead based on
reasoned judgment. What he has not shown is that reasoned judgment
guarantees that experimental results reflect the facts alone and are
therefore aperspectival in any interesting sense. Another important
challenge for the aperspectival account comes from feminist
epistemology and other accounts that stress the importance of the
construction of scientific knowledge through epistemic communities.
These accounts are reviewed in
 section 5.


## 3. Objectivity as Absence of Normative Commitments and the Value-Free Ideal



In the previous section we have presented arguments against the view
of objectivity as faithfulness to facts and an impersonal "view
from nowhere". An alternative view is that science is objective
to the extent that it is *value-free*. Why would we identify
objectivity with value-freedom or regard the latter as a prerequisite
for the former? Part of the answer is empiricism. If science is in the
business of producing empirical knowledge, and if differences about
value judgments cannot be settled by empirical means, values should
have no place in science. In the following we will try to make this
intuition more precise.


### 3.1 Epistemic and Contextual Values



Before addressing what we will call the "value-free
ideal", it will be helpful to distinguish four stages at which
values may affect science. They are: (i) the choice of a scientific
research problem; (ii) the gathering of evidence in relation to the
problem; (iii) the acceptance of a scientific hypothesis or theory as
an adequate answer to the problem on the basis of the evidence; (iv)
the proliferation and application of scientific research results
(Weber 1917 [1949]).



Most philosophers of science would agree that the role of values in
science is contentious only with respect to dimensions (ii) and (iii):
the **gathering of evidence** and the **acceptance
of scientific theories**. It is almost universally accepted
that the choice of a research problem is often influenced by interests
of individual scientists, funding parties, and society as a whole.
This influence may make science more shallow and slow down its
long-run progress, but it has benefits, too: scientists will focus on
providing solutions to those intellectual problems that are considered
urgent by society and they may actually improve people's lives.
Similarly, the proliferation and application of scientific research
results is evidently affected by the personal values of journal
editors and end users, and little can be done about this. The real
debate is about whether or not the "core" of scientific
reasoning--the gathering of evidence and the assessment and
acceptance scientific theories--is, and should be,
value-free.



We have introduced the problem of the underdetermination of theory by
evidence above. The problem does not stop, however, at values being
required for filling the gap between theory and evidence. A further
complication is that these values can conflict with each other.
Consider the classical problem of fitting a mathematical function to a
data set. The researcher often has the choice between using a complex
function, which makes the relationship between the variables less
*simple* but fits the data more *accurately*, or
postulating a *simpler* relationship that is less
*accurate*. Simplicity and accuracy are both important
cognitive values, and trading them off requires a careful value
judgment. However, philosophers of science tend to regard
value-ladenness in this sense as benign. **Cognitive
values** (sometimes also called "epistemic" or
"constitutive" values) such as predictive accuracy, scope,
unification, explanatory power, simplicity and coherence with other
accepted theories are taken to be indicative of the truth of a theory
and therefore provide reasons for preferring one theory over another
(McMullin 1982, 2009; Laudan 1984; Steel 2010). Kuhn (1977) even
claims that cognitive values define the shared commitments of science,
that is, the standards of theory assessment that characterize the
scientific approach as a whole. Note that not every philosopher
entertains the same list of cognitive values: subjective differences
in ranking and applying cognitive values do not vanish, a point Kuhn
made emphatically.



In most views, the objectivity and authority of science is not
threatened by cognitive values, but only by
**non-cognitive** or **contextual values**.
Contextual values are moral, personal, social, political and cultural
values such as pleasure, justice and equality, conservation of the
natural environment and diversity. The most notorious cases of
improper uses of such values involve travesties of scientific
reasoning, where the intrusion of contextual values led to an
intolerant and oppressive scientific agenda with devastating epistemic
and social consequences. In the Third Reich, a large part of
contemporary physics, such as the theory of relativity, was condemned
because its inventors were Jewish; in the Soviet Union, biologist
Nikolai Vavilov was sentenced to death (and died in prison) because
his theories of genetic inheritance did not match Marxist-Leninist
ideology. Both states tried to foster a science that was motivated by
political convictions ("Deutsche Physik" in Nazi Germany,
Lysenko's Lamarckian theory of inheritance and denial of
genetics), leading to disastrous epistemic and institutional
effects.



Less spectacular, but arguably more frequent are cases where research
is biased toward the interests of the sponsors, such as tobacco
companies, food manufacturers and large pharmaceutic firms (e.g.,
Resnik 2007; Reiss 2010). This **preference bias**,
defined by Wilholt (2009) as the infringement of conventional
standards of the research community with the aim of arriving at a
particular result, is clearly epistemically harmful. Especially for
sensitive high-stakes issues such as the admission of medical drugs or
the consequences of anthropogenic global warming, it seems desirable
that research scientists assess theories without being influenced by
such considerations. This is the core idea of the



> 
> 
> **Value-Free Ideal (VFI):** Scientists should strive to
> minimize the influence of contextual values on scientific reasoning,
> e.g., in gathering evidence and assessing/accepting scientific
> theories.
> 
> 
> 



According to the VFI, scientific objectivity is characterized by
absence of contextual values and by exclusive commitment to cognitive
values in stages (ii) and (iii) of the scientific process. See Dorato
(2004: 53-54), Ruphy (2006: 190) or Biddle (2013: 125) for
alternative formulations.



For value-freedom to be a reasonable ideal, it must not be a goal
beyond reach and be attainable at least to some degree. This claim is
expressed by the



> 
> 
> **Value-Neutrality Thesis (VNT):** Scientists
> can--at least in principle--gather evidence and
> assess/accept theories without making contextual value judgments.
> 
> 
> 



Unlike the VFI, the VNT is not normative: its subject is whether the
judgments that scientists make are, or could possibly be, free of
contextual values. Similarly, Hugh Lacey (1999) distinguishes three
principal components or aspects of value-free science: impartiality,
neutrality and autonomy. **Impartiality** means that
theories are solely accepted or appraised in virtue of their
contribution to the cognitive values of science, such as truth,
accuracy or explanatory power. This excludes the influence of
contextual values, as stated above. **Neutrality** means
that scientific theories make no value statements about the world:
they are concerned with what there is, not with what there should be.
Finally, scientific **autonomy** means that the
scientific agenda is shaped by the desire to increase scientific
knowledge, and that contextual values have no place in scientific
method.



These three interpretations of value-free science can be combined with
each other, or used individually. All of them, however, are subject to
criticisms that we examine below. Denying the VNT, or the
attainability of Lacey's three criteria for value-free science,
poses a challenge for scientific objectivity: one can either conclude
that the ideal of objectivity should be rejected, or develop a
conception of objectivity that differs from the VFI.


### 3.2 Acceptance of Scientific Hypotheses and Value Neutrality



Lacey's characterization of value-free science and the VNT were
once mainstream positions in philosophy of science. Their widespread
acceptance was closely connected to Reichenbach's famous
distinction between **context of discovery** and
**context of justification**. Reichenbach first made this
distinction with respect to the epistemology of mathematics:



> 
> 
> the objective relation from the given entities to the solution, and
> the subjective way of finding it, are clearly separated for problems
> of a deductive character [...] we must learn to make the same
> distinction for the problem of the inductive relation from facts to
> theories. (Reichenbach 1938: 36-37)
> 
> 
> 



The standard interpretation of this statement marks contextual values,
which may have contributed to the discovery of a theory, as irrelevant
for *justifying* the acceptance of a theory, and for assessing
how evidence bears on theory--the relation that is crucial for
the objectivity of science. Contextual values are restricted to a
matter of individual psychology that may influence the discovery,
development and proliferation of a scientific theory, but not its
epistemic status.



This distinction played a crucial role in post-World War II philosophy
of science. It presupposes, however, a clear-cut distinction between
cognitive values on the one hand and contextual values on the other.
While this may be *prima facie* plausible for disciplines such
as physics, there is an abundance of contextual values in the social
sciences, for instance, in the conceptualization and measurement of a
nation's wealth, or in different ways to measure the inflation
rate (cf. Dupre 2007; Reiss 2008). More generally, three major
lines of criticism can be identified.



First, Helen Longino (1996) has argued that traditional cognitive
values such as consistency, simplicity, breadth of scope and
fruitfulness are not purely cognitive or epistemic after all, and that
their use imports political and social values into contexts of
scientific judgment. According to her, the use of cognitive values in
scientific judgments is not always, not even normally, politically
neutral. She proposes to juxtapose these values with feminist values
such as novelty, ontological heterogeneity, mutuality of interaction,
applicability to human needs and diffusion of power, and argues that
the use of the traditional value instead of its alternative (e.g.,
simplicity instead of ontological heterogeneity) can lead to biases
and adverse research results. Longino's argument here is
different from the one discussed in
 section 3.1.
 It casts the very distinction between cognitive and contextual values
into doubt.



The second argument against the possibility of value-free science is
semantic and attacks the neutrality of scientific theories: fact and
value are frequently entangled because of the use of so-called
"thick" ethical concepts in science (Putnam
2002)--i.e., ethical concepts that have mixed descriptive and
normative content. For example, a description such as "dangerous
technology" involves a value judgment about the technology and
the risks it implies, but it also has a descriptive content: it is
uncertain and hard to predict whether using that technology will
really trigger those risks. If the use of such terms, where facts and
values are inextricably entangled, is inevitable in scientific
reasoning, it is impossible to describe hypotheses and results in a
value-free manner, undermining the value-neutrality thesis.



Indeed, John Dupre has argued that thick ethical terms are
ineliminable from science, at least certain parts of it (Dupre
2007). Dupre's point is essentially that scientific
hypotheses and results concern us because they are relevant to human
interests, and thus they will necessarily be couched in a language
that uses thick ethical terms. While it will often be possible to
translate ethically thick descriptions into neutral ones, the
translation cannot be made without losses, and these losses obtain
precisely because human interests are involved (see
 section 6.2
 for a case study from social science). According to Dupre,
then, many scientific statements are value-free only because their
truth or falsity does not matter to us:



> 
> 
> Whether electrons have a positive or a negative charge and whether
> there is a black hole in the middle of our galaxy are questions of
> absolutely no immediate importance to us. The only human interests
> they touch (and these they may indeed touch deeply) are cognitive
> ones, and so the only values that they implicate are cognitive values.
> (2007: 31)
> 
> 
> 



A third challenge to the VNT, and perhaps the most influential one,
was raised first by Richard Rudner in his influential article
"The Scientist Qua Scientist Makes Value Judgments"
(Rudner 1953). Rudner disputes the core of the VNT and the context of
discovery/justification distinction: the idea that the acceptance of a
scientific theory can in principle be value-free. First, Rudner argues
that



> 
> 
> no analysis of what constitutes the method of science would be
> satisfactory unless it comprised some assertion to the effect that the
> scientist as scientist **accepts or rejects hypotheses**.
> (1953: 2)
> 
> 
> 



This assumption stems from industrial quality control and other
application-oriented research. In such contexts, it is often necessary
to accept or to reject a hypothesis (e.g., the efficacy of a drug) in
order to make effective decisions.



Second, he notes that no scientific hypothesis is ever confirmed
beyond reasonable doubt--some probability of error always
remains. When we accept or reject a hypothesis, there is always a
chance that our decision is mistaken. Hence, our decision is also
"a function of the *importance*, in the typically ethical
sense, of making a mistake in accepting or rejecting a
hypothesis" (1953: 2): we are balancing the seriousness of two
possible errors (erroneous acceptance/rejection of the hypothesis)
against each other. This corresponds to type I and type II error in
statistical inference.



The decision to accept or reject a hypothesis involves a value
judgment (at least implicitly) because scientists have to judge which
of the consequences of an erroneous decision they deem more palatable:
(1) some individuals die of the side effects of a drug erroneously
judged to be safe; or (2) other individuals die of a condition because
they did not have access to a treatment that was erroneously judged to
be unsafe. Hence, ethical judgments and contextual values necessarily
enter the scientist's core activity of accepting and rejecting
hypotheses, and the VNT stands refuted. Closely related arguments can
be found in Churchman (1948) and Braithwaite (1953). Hempel (1965:
91-92) gives a modified account of Rudner's argument by
distinguishing between judgments of *confirmation*, which are
free of contextual values, and judgments of *acceptance*. Since
even strongly confirming evidence cannot fully prove a universal
scientific law, we have to live with a residual "inductive
risk" in inferring that law. Contextual values influence
scientific methods by determining the acceptable amount of inductive
risk (see also Douglas 2000).



But how general are Rudner's objections? Apparently, his result
holds true of applied science, but not necessarily of fundamental
research. For the latter domain, two major lines of rebuttals have
been proposed. First, Richard Jeffrey (1956) notes that lawlike
hypotheses in theoretical science (e.g., the gravitational law in
Newtonian mechanics) are characterized by their general scope and not
confined to a particular application. Obviously, a scientist cannot
fine-tune her decisions to their possible consequences in a wide
variety of different contexts. So she should just refrain from the
essentially pragmatic decision to accept or reject hypotheses. By
restricting scientific reasoning to gathering and interpreting
evidence, possibly supplemented by assessing the probability of a
hypothesis, Jeffrey tries to save the VNT in fundamental scientific
research, and the objectivity of scientific reasoning.



Second, Isaac Levi (1960) observes that scientists commit themselves
to certain standards of inference when they become a member of the
profession. This may, for example, lead to the statistical rejection
of a hypothesis when the observed significance level is smaller than
5%. These community standards may eliminate any room for contextual
ethical judgment on behalf of the scientist: they determine when she
should accept a hypothesis as established. Value judgments may be
implicit in how a scientific community sets standards of inference
(compare
 section 5.1),
 but not in the daily work of an *individual* scientist (cf.
Wilholt 2013).



Both defenses of the VNT focus on the impact of values in theory
choice, either by denying that scientists actually choose theories
(Jeffrey), or by referring to community standards and restricting the
VNT to the individual scientist (Levi). Douglas (2000: 563-565)
points out, however, that the "acceptance" of scientific
theories is only one of several places for values to enter scientific
reasoning, albeit an especially prominent and explicit one. Many
decisions in the process of scientific inquiry may conceal implicit
value judgments: the design of an experiment, the methodology for
conducting it, the characterization of the data, the choice of a
statistical method for processing and analyzing data, the
interpretational process findings, etc. None of these methodological
decisions could be made without consideration of the possible
consequences that could occur. Douglas gives, as a case study, a
series of experiments where carcinogenic effects of dioxin exposure on
rats were probed. Contextual values such as safety and risk aversion
affected the conducted research at various stages: first, in the
classification of pathological samples as benign or cancerous (over
which a lot of expert disagreement occurred), second, in the
extrapolation from the high-dose experimental conditions to the more
realistic low-dose conditions. In both cases, the choice of a
conservative classification or model had to be weighed against the
adverse consequences for society that could result from
underestimating the risks (see also Biddle 2013).



These diagnoses cast a gloomy light on attempts to divide scientific
labor between gathering evidence and determining the degree of
confirmation (value-free) on the one hand and accepting scientific
theories (value-laden) on the other. The entire process of
conceptualizing, gathering and interpreting evidence is so entangled
with contextual values that no neat division, as Jeffrey envisions,
will work outside the narrow realm of statistical inference--and
even there, doubts may be raised
 (see section 4.2).



Philip Kitcher (2011a: 31-40; see also Kitcher 2011b) gives an
alternative argument, based on his idea of "significant
truths". There are simply too many truths that are of no
interest whatsoever, such as the total number of offside positions in
a low-level football competition. Science, then, doesn't aim at
truth *simpliciter* but rather at something more narrow: truth
worth pursuing from the point of view of our cognitive, practical and
social goals. Any truth that is worth pursuing in this sense is what
he calls a "significant truth". Clearly, it is value
judgments that help us decide whether or not any given truth is
significant.



Kitcher goes on to observing that the process of scientific
investigation cannot neatly be divided into a stage in which the
research question is chosen, one in which the evidence is gathered and
one in which a judgment about the question is made on the basis of the
evidence. Rather, the sequence is multiply iterated, and at each
stage, the researcher has to decide whether previous results warrant
pursuit of the current line of research, or whether she should switch
to another avenue. Such choices are laden with contextual values.



Values in science also interact, according to Kitcher, in a
non-trivial way. Assume we endorse predictive accuracy as an important
goal of science. However, there may not be a convincing strategy to
reach this goal in some domain of science, for instance because that
domain is characterized by strong non-linear dependencies. In this
case, predictive accuracy might have to yield to achieving other
values, such as consistency with theories in neighbor domains.
Conversely, changing social goals lead to re-evaluations of scientific
knowledge and research methods.



Science, then, cannot be value-free because no scientist ever works
exclusively in the supposedly value-free zone of assessing and
accepting hypotheses. Evidence is gathered and hypotheses are assessed
and accepted in the light of their potential for application and
fruitful research avenues. Both cognitive and contextual value
judgments guide these choices and are themselves influenced by their
results.


### 3.3 Science, Policy and the Value-Free Ideal



The discussion so far has focused on the VNT, the practical
attainability of the VFI, but little has been said about whether a
value-free science is desirable in the first place. This subsection
discusses this topic with special attention to informing and advising
public policy from a scientific perspective. While the VFI, and many
arguments for and against it, can be applied to science as a whole,
the interface of science and public policy is the place where the
intrusion of values into science is especially salient, and where it
is surrounded by the greatest controversy. In the 2009
"Climategate" affair, leaked emails from climate
scientists raised suspicions that they were pursuing a particular
socio-political agenda that affected their research in an improper
way. Later inquiries and reports absolved them from charges of
misconduct, but the suspicions alone did much to damage the authority
of science in the public arena.



Indeed, many debates at the interface of science and public policy are
characterized by disagreements on propositions that combine a factual
basis with specific goals and values. Take, for instance, the view
that growing transgenic crops carries too much risk in terms of
biosecurity, or addressing global warming by phasing out fossil
energies immediately. The critical question in such debates is whether
there are theses \(T\) such that one side in the debate endorses
\(T\), the other side rejects it, the evidence is shared, and both
sides have good reasons for their respective positions.



According to the VFI, scientists should uncover an epistemic,
value-free basis for resolving such disagreements and restrict the
dissent to the realm of value judgments. Even if the VNT should turn
out to be untenable, and a strict separation to be impossible, the VFI
may have an important function for *guiding* scientific
research and for minimizing the impact of values on an objective
science. In the philosophy of science, one camp of scholars defends
the VFI as a necessary antidote to individual and institutional
interests, such as Hugh Lacey (1999, 2002), Ernan McMullin (1982) and
Sandra Mitchell (2004), while others adopt a critical attitude, such
as Helen Longino (1990, 1996), Philip Kitcher (2011a) and Heather
Douglas (2009). These criticisms we discuss mainly refer to the
desirability or the conceptual (un)clarity of the VFI.



First, it has been argued that the VFI is not desirable at all.
Feminist philosophers (e.g., Harding 1991; Okruhlik 1994; Lloyd 2005)
have argued that science often carries a heavy androcentric values,
for instance in biological theories about sex, gender and rape. The
charge against these values is not so much that they are contextual
rather than cognitive, but that they are unjustified. Moreover, if
scientists did follow the VFI rigidly, policy-makers would pay even
less attention to them, with a detrimental effect on the decisions
they take (Cranor 1993). Given these shortcomings, the VFI has to be
rethought if it is supposed to play a useful role for guiding
scientific research and leading to better policy decisions.
 Section 4.3
 and
 section 5.2
 elaborate on this line of criticism in the context of scientific
community practices, and a science in the service of society.



Second, the autonomy of science often fails in practice due to the
presence of external stakeholders, such as funding agencies and
industry lobbies. To save the epistemic authority of science, Douglas
(2009: 7-8) proposes to detach it from its autonomy by
reformulating the VFI and distinguishing between **direct and
indirect roles of values in science**. Contextual values may
legitimately affect the assessment of evidence by indicating the
appropriate standard of evidence, the representation of complex
processes, the severity of consequences of a decision, the
interpretation of noisy datasets, and so on (see also Winsberg 2012).
This concerns, above all, policy-related disciplines such as climate
science or economics that routinely perform scientific risk analyses
for real-world problems (cf. also Shrader-Frechette 1991). Values
should, however, not be "reasons in themselves", that is,
evidence or defeaters for evidence (direct role, illegitimate) and as
"helping to decide what should count as a *sufficient*
reason for a choice" (indirect role, legitimate). This
prohibition for values to replace or dismiss scientific evidence is
called **detached objectivity** by Douglas, but it is
complemented by various other aspects that relate to a reflective
balancing of various perspectives and the procedural, social aspects
of science (2009: ch. 6).



That said, Douglas' proposal is not very concrete when it comes
to implementation, e.g., regarding the way diverse values should be
balanced. Compromising in the middle cannot be the solution (Weber
1917 [1949]). First, no standpoint is, just in virtue of being in the
middle, evidentially supported vis-a-vis more extreme
positions. Second, these middle positions are also, from a practical
point of view, the least functional when it comes to advising
policy-makers.



Moreover, the distinction between direct and indirect roles of values
in science may not be sufficiently clear-cut to police the legitimate
use of values in science, and to draw the necessary borderlines.
Assume that a scientist considers, for whatever reason, the
consequences of erroneously accepting hypothesis \(H\) undesirable.
Therefore he uses a statistical model whose results are likely to
favor !\(H\) over \(H\). Is this a matter of reasonable
conservativeness? Or doesn't it amount to reasoning to a
foregone conclusion, and to treating values as evidence (cf. Elliott
2011: 320-321)?



The most recent literature on values and evidence in science presents
us with a broad spectrum of opinions. Steele (2012) and Winsberg
(2012) agree that probabilistic assessments of uncertainty involve
contextual value judgments. While Steele defends this point by
analyzing the role of scientists as policy advisors, Winsberg points
to the influence of contextual values in the selection and
representation of physical processes in climate modeling. Betz (2013)
argues, by contrast, that scientists can largely avoid making
contextual value judgments if they carefully express the uncertainty
involved with their evidential judgments, e.g., by using a scale
ranging from purely qualitative evidence (such as expert judgment) to
precise probabilistic assessments. The issue of value judgments at
earlier stages of inquiry is not addressed by this proposal; however,
disentangling evidential judgments and judgments involving contextual
values at the stage of theory assessment may be a good thing in
itself.



Thus, should we or should we not worried about values in scientific
reasoning? While the interplay of values and evidential considerations
need not be pernicious, it is unclear why it *adds* to the
success or the authority of science. How are we going to ensure that
the permissive attitude towards values in setting evidential standards
etc. is not abused? In the absence of a general theory about which
contextual values are beneficial and which are pernicious, the VFI
might as well be as a first-order approximation to a sound,
transparent and objective science.


## 4. Objectivity as Freedom from Personal Biases



This section deals with scientific objectivity as a form of
intersubjectivity--as freedom from personal biases. According to
this view, science is objective to the extent that personal biases are
absent from scientific reasoning, or that they can be eliminated in a
social process. Perhaps all science is necessarily perspectival.
Perhaps we cannot sensibly draw scientific inferences without a host
of background assumptions, which may include assumptions about values.
Perhaps all scientists are biased in some way. But objective
scientific results do not, or so the argument goes, depend on
researchers' personal preferences or experiences--they are
the result of a process where individual biases are gradually filtered
out and replaced by agreed upon evidence. That, among other things, is
what distinguishes science from the arts and other human activities,
and scientific knowledge from a fact-independent social construction
(e.g., Haack 2003).



Paradigmatic ways to achieve objectivity in this sense are measurement
and quantification. What has been measured and quantified has been
verified relative to a standard. The truth, say, that the Eiffel Tower
is 324 meters tall is relative to a standard unit and conventions
about how to use certain instruments, so it is neither aperspectival
nor free from assumptions, but it is independent of the person making
the measurement.



We will begin with a discussion of objectivity, so conceived, in
measurement, discuss the ideal of "mechanical objectivity"
and then investigate to what extent freedom from personal biases can
be implemented in statistical evidence and inductive
inference--arguably the core of scientific reasoning, especially
in quantitatively oriented sciences. Finally, we discuss
Feyerabend's radical criticism of a rational scientific method
that can be mechanically applied, and his defense of the epistemic and
social benefits of personal "bias" and idiosyncrasy.


### 4.1 Measurement and Quantification



Measurement is often thought to epitomize scientific objectivity, most
famously captured in Lord Kelvin's dictum



> 
> 
> when you cannot express it in numbers, your knowledge is of a meagre
> and unsatisfactory kind; it may be the beginning of knowledge, but you
> have scarcely, in your thoughts, advanced to the stage of
> *science*, whatever the matter may be. (Kelvin 1883, 73)
> 
> 
> 



Measurement can certainly achieve some independence of perspective.
Yesterday's weather in Durham UK may have been "really
hot" to the average North Eastern Brit and "very
cold" to the average Mexican, but they'll both accept that
it was 21degC. Clearly, however, measurement does not result in a
"view from nowhere", nor are typical measurement results
free from presuppositions. Measurement instruments interact with the
environment, and so results will always be a product of both the
properties of the environment we aim to measure as well as the
properties of the instrument. Instruments, thus, provide a
perspectival view on the world (cf. Giere 2006).



Moreover, making sense of measurement results requires interpretation.
Consider temperature measurement. Thermometers function by relating an
unobservable quantity, temperature, to an observable quantity,
expansion (or length) of a fluid or gas in a glass tube; that is,
thermometers measure temperature by assuming that length is a function
of temperature: length = \(f\)(temperature). The function \(f\) is not
known *a priori*, and it cannot be tested either (because it
could in principle only be tested using a *veridical*
thermometer, and the veridicality of the thermometer is just what is
at stake here). Making a specific assumption, for instance that \(f\)
is linear, solves that problem by fiat. But this
"solution" does not take us very far because different
thermometric substances (e.g., mercury, air or water) yield different
results for the points intermediate between the two fixed points
0degC and 100degC, and so they can't all expand
linearly.



According to Hasok Chang's account of early thermometry (Chang
2004), the problem was eventually solved by using a "principle
of minimalist overdetermination", the goal of which was to find
a reliable thermometer while making as few substantial assumptions
(e.g., about the form for \(f\)) as possible. It was argued that if a
thermometer was to be reliable, different tokens of the same
thermometer type should agree with each other, and the results of air
thermometers agreed the most. "Minimal" doesn't mean
zero, however, and indeed this procedure makes an important
presupposition (in this case a metaphysical assumption about the
one-valuedness of a physical quantity). Moreover, the procedure
yielded at best a reliable instrument, not necessarily one that was
best at tracking the uniquely real temperature (if there is such a
thing).



What Chang argues about early thermometry is true of measurements more
generally: they are always made against a backdrop of metaphysical
presuppositions, theoretical expectations and other kinds of belief.
Whether or not any given procedure is regarded as adequate depends to
a large extent on the purposes pursued by the individual scientist or
group of scientists making the measurements. Especially in the social
sciences, this often means that measurement procedures are laden with
normative assumptions, i.e., values.



Julian Reiss (2008, 2013) has argued that economic indicators such as
consumer price inflation, gross domestic product and the unemployment
rate are value-laden in this sense. Consumer-price indices, for
instance, assume that if a consumer prefers a bundle \(x\) over an
alternative \(y\), then \(x\) is better for her than \(y\), which is
as ethically charged as it is controversial. National income measures
assume that nations that exchange a larger share of goods and services
on markets are richer than nations where the same goods and services
are provided by the government or within households, which too is
ethically charged and controversial.



While not free of assumptions and values, the goal of many measurement
procedures remains to reduce the influence of personal biases and
idiosyncrasies. The Nixon administration, famously, indexed social
security payments to the consumer-price index in order to eliminate
the dependence of security recipients on the flimsiest of party
politics: to make increases automatic instead of a result of political
negotiations (Nixon 1969). Lorraine Daston and Peter Galison refer to
this as **mechanical objectivity**. They write:



> 
> 
> Finally, we come to the full-fledged establishment of mechanical
> objectivity as the ideal of scientific representation. What we find is
> that the image, as standard bearer of is objectivity is tied to a
> relentless search to replace individual volition and discretion in
> depiction by the invariable routines of mechanical reproduction.
> (Daston and Galison 1992: 98)
> 
> 
> 



Mechanical objectivity reduces the importance of human contributions
to scientific results to a minimum, and therefore enables science to
proceed on a large scale where bonds of trust between individuals can
no longer hold (Daston 1992). Trust in mechanical procedures thus
replaces trust in individual scientists.



In his book *Trust in Numbers*, Theodore Porter pursues this
line of thought in great detail. In particular, on the basis of case
studies involving British actuaries in the mid-nineteenth century, of
French state engineers throughout the century, and of the US Army
Corps of Engineers from 1920 to 1960, he argues for two causal claims.
First, measurement instruments and quantitative procedures originate
in commercial and administrative needs and affect the ways in which
the natural and social sciences are practiced, not the other way
around. The mushrooming of instruments such as chemical balances,
barometers, chronometers was largely a result of social pressures and
the demands of democratic societies. Administering large territories
or controlling diverse people and processes is not always possible on
the basis of personal trust and thus "objective
procedures" (which do not require trust in persons) took the
place of "subjective judgments" (which do). Second, he
argues that quantification is a technology of distrust and weakness,
and not of strength. It is weak administrators who do not have the
social status, political support or professional solidarity to defend
their experts' judgments. They therefore subject decisions to
public scrutiny, which means that they must be made in a publicly
accessible form.



This is the situation in which scientists who work in areas where the
science/policy boundary is fluid find themselves:



> 
> 
> The National Academy of Sciences has accepted the principle that
> scientists should declare their conflicts of interest and financial
> holdings before offering policy advice, or even information to the
> government. And while police inspections of notebooks remain
> exceptional, the personal and financial interests of scientists and
> engineers are often considered material, especially in legal and
> regulatory contexts.
> 
> 
> 
> Strategies of impersonality must be understood partly as defenses
> against such suspicions [...]. Objectivity means knowledge that
> does not depend too much on the particular individuals who author it.
> (Porter 1995: 229)
> 
> 
> 



Measurement and quantification help to reduce the influence of
personal biases and idiosyncrasies and they reduce the need to trust
the scientist or government official, but often at a cost.
Standardizing scientific procedures becomes difficult when their
subject matters are not homogeneous, and few domains outside
fundamental physics are. Attempts to quantify procedures for treatment
and policy decisions that we find in evidence-based practices are
currently transferred to a variety of sciences such as medicine,
nursing, psychology, education and social policy. However, they often
lack a certain degree of responsiveness to the peculiarities of their
subjects and the local conditions to which they are applied (see also
 section 5.3).



Moreover, the measurement and quantification of characteristics of
scientific interest is only half of the story. We also want to
describe relations between the quantities and make inferences using
statistical analysis. Statistics thus helps to quantify further
aspects of scientific work. We will now examine whether or not
statistical analysis can proceed in a way free from personal biases
and idiosyncrasies--for more detail, see the entry on
 philosophy of statistics.


### 4.2 Statistical Evidence



The appraisal of scientific evidence is traditionally regarded as a
domain of scientific reasoning where the ideal of scientific
objectivity has strong normative force, and where it is also
well-entrenched in scientific practice. Episodes such as
Galilei's observations of the Jupiter moons, Lavoisier's
calcination experiments, and Eddington's observation of the 1919
eclipse are found in all philosophy of science textbooks because they
exemplify how evidence can be persuasive and compelling to scientists
with different backgrounds. The crucial question is therefore: can we
identify an "objective" concept of scientific evidence
that is independent of the personal biases of the experimenter and
interpreter?



Inferential statistics--the field that investigates the validity
of inferences from data to theory--tries to answer this question.
It is extremely influential in modern science, pervading experimental
research as well as the assessment and acceptance of our most
fundamental theories. For instance, a statistical argument helped to
establish the recent discovery of the Higgs Boson. We now compare the
main theories of statistical evidence with respect to the objectivity
of the claims they produce. They mainly differ with respect to the
role of an explicitly subjective interpretation of probability.


#### 4.2.1 Bayesian Inference



Bayesian inference quantifies scientific evidence by means of
probabilities that are interpreted as a scientist's subjective
degrees of belief. The Bayesian thus leaves behind Carnap's
(1950) idea that probability is determined by a logical relation
between sentences. For example, the prior degree of belief in
hypothesis \(H\), written \(p(H)\), can in principle take any value in
the interval \([0,1]\). Simultaneously held degrees of belief in
different hypotheses are, however, constrained by the laws of
probability. After learning evidence E, the degree of belief in \(H\)
is changed from its prior probability \(p(H)\) to the conditional
degree of belief \(p(H \mid E)\), commonly called the posterior
probability of \(H\). Both quantities can be related to each other by
means of
 Bayes' Theorem.



These days, the Bayesian approach is extremely influential in
philosophy and rapidly gaining ground across all scientific
disciplines. For quantifying evidence for a hypothesis, Bayesian
statisticians almost uniformly use the **Bayes factor**,
that is, the ratio of prior to posterior odds in favor of a
hypothesis. The Bayes factor in favor of hypothesis \(H\) against its
negation \(\neg\)\(H\) in the light of evidence \(E\) can be written
as

 

\[\tag{3}\label{eqn:BF} BF (E) := \frac{p(H \mid E)}{p(\neg H \mid E)} \cdot \frac{p(\neg H)}{p(H)} = \frac{p(E \mid H)}{p(E \mid \neg H)},\]


or in other words, as the likelihood ratio between \(H\) and
\(\neg\)\(H\). The Bayes factor reduces to the likelihoodist
conception of evidence (Royall 1997) for the case of two competing
point hypotheses. For further discussion of Bayesian measures of
evidence, see Good (1950), Sprenger and Hartmann (2019: ch. 1) and the
entry on
 confirmation and evidential support.



Unsurprisingly, the idea to measure scientific evidence in terms of
subjective probability has met resistance. For example, the
statistician Ronald A. Fisher (1935: 6-7) has argued that
measuring psychological tendencies cannot be relevant for scientific
inquiry and sustain claims to objectivity. Indeed, how should
scientific objectivity square with subjective degree of belief?
Bayesians have responded to this challenge in various ways:


* Howson (2000) and Howson and Urbach (2006) consider the objection
misplaced. In the same way that deductive logic does not judge the
correctness of the premises but just advises you what to infer from
them, **Bayesian inductive logic** provides rational
rules for representing uncertainty and making inductive inferences.
Choosing the premises (e.g., the prior distributions)
"objectively" falls outside the scope of Bayesian
analysis.
* **Convergence or merging-of-opinion theorems** guarantee
that under certain circumstances, agents with very different initial
attitudes who observe the same evidence will obtain similar posterior
degrees of belief in the long run. However, they are asymptotic
results without direct implications for inference with real-life
datasets (see also Earman 1992: ch. 6). In such cases, the choice of
the prior matters, and it may be beset with idiosyncratic bias and
manifest social values.
* Adopting a more modest stance, Sprenger (2018) accepts that Bayesian
inference does not achieve the goal of objectivity in the sense of
intersubjective agreement (concordant objectivity), or being free of
personal values, bias and subjective judgment. However, he argues that
competing schools of inference such as frequentist inference face this
problem to the same degree, perhaps even worse. Moreover, some
features of Bayesian inference (e.g., the transparency about prior
assumptions) fit recent, socially oriented conceptions of objectivity
that we discuss in
 section 5.



A radical Bayesian solution to the problem of personal bias is to
adopt a principle that radically constrains an agent's rational
degrees of belief, such as the **Principle of Maximum
Entropy** (MaxEnt--Jaynes 1968; Williamson 2010).
According to MaxEnt, degrees of belief must be probabilistic and in
sync with empirical constraints, but conditional on these constraints,
they must be equivocal, that is, as middling as possible. This latter
constraint amounts to maximizing the entropy of the probability
distribution in question. The MaxEnt approach eliminates various
sources of subjective bias at the expense of narrowing down the range
of rational degrees of belief. An alternative objective Bayesian
solution consists in so-called **"objective
priors"**: prior probabilities that do not represent an
agent's factual attitudes, but are determined by principles of
symmetry, mathematical convenience or maximizing the influence of the
data on the posterior (e.g., Jeffreys 1939 [1980]; Bernardo 2012).



Thus, Bayesian inference, which analyzes statistical evidence from the
vantage point of rational belief, provides only a partial answer to
securing scientific objectivity from personal idiosyncrasy.


#### 4.2.2 Frequentist Inference



The frequentist conception of evidence is based on the idea of the
**statistical test of a hypothesis**. Under the influence
of the statisticians Jerzy Neyman and Egon Pearson, tests were often
regarded as rational decision procedures that minimize the relative
frequency of wrong decisions in a hypothetical series of repetitions
of a test (hence the name "frequentism"). Rudner's
argument in
 section 3.2
 has pointed out the limits of this conception of hypothesis tests:
the choice of thresholds for acceptance and rejection (i.e., the
acceptable type I and II error rates) may reflect contextual value
judgments and personal bias. Moreover, the losses associated with
erroneously accepting or rejecting that hypothesis depend on the
context of application which may be unbeknownst to the
experimenter.



Alternatively, scientists can restrict themselves to a purely
evidential interpretation of hypothesis tests and leave decisions to
policy-makers and regulatory agencies. The statistician and biologist
R.A. Fisher (1935, 1956) proposed what later became the orthodox
quantification of evidence in frequentist statistics. Suppose a
"null" or default hypothesis \(H\_0\) denotes that an
intervention has zero effect. If the observed data are
"extreme" under \(H\_0\)--i.e., if it was highly
likely to observe a result that agrees better with \(H\_0\)--the
data provide evidence against the null hypothesis and for the efficacy
of the intervention. The epistemological rationale is connected to the
idea of severe testing (Mayo 1996): if the intervention were
ineffective, we would, in all likelihood, have found data that agree
better with the null hypothesis. The strength of evidence against
\(H\_0\) is equal to the **\(p\)-value**: the lower it is,
the stronger evidence \(E\) speaks against the null hypothesis
\(H\_0\).



Unlike Bayes factors, this concept of statistical evidence does not
depend on personal degrees of belief. However, this does not
necessarily mean that \(p\)-values are more objective. First,
\(p\)-values are usually classified as "non-significant"
(\(p > .05\)), "significant" (\(p < .05\)),
"highly significant", and so on. Not only that these
thresholds and labels are largely arbitrary, they also promote
**publication bias**: non-significant findings are often
classified as "failed studies" (i.e., the efficacy of the
intervention could not be shown), rarely published and end up in the
proverbial "file drawer". Much valuable research is
suppressed. Conversely, significant findings may often occur when the
null hypothesis is actually true, especially when researchers have
been "hunting for significance". In fact, researchers have
an incentive to keep their \(p\)-values low: the stronger the
evidence, the more convincing the narrative, the greater the
impact--and the higher the chance for a good publication and
career-relevant rewards. Moving the goalpost by
"p-hacking" outcomes--for example by eliminating
outliers, selective reporting or restricting the analysis to a
subgroup--evidently biases the research results and compromises
the objectivity of experimental research.



In particular, such **questionable research practices
(QRP)** increase the type I error rate, which measures the rate
at which false hypotheses are accepted, substantially over its nominal
5% level and contribute to publication bias (Bakker et al. 2012).
Ioannidis (2005) concludes that "most published research
findings are false"--they are the combined result of a low
base rate of effective causal interventions, the file drawer effect
and the widespread presence of questionable research practices. The
frequentist logic of hypothesis testing aggravates the problem because
it provides a framework where all these biases can easily enter
(Ziliak and McCloskey 2008; Sprenger 2016). These radical conclusions
are also confirmed by empirical findings: in many disciplines
researchers fail to replicate findings by other scientific teams. See
 section 5.1
 for more detail.



Summing up our findings, neither of the two major frameworks of
statistical inference manages to eliminate all sources of personal
bias and idiosyncrasy. The Bayesian considers subjective assumptions
to be an irreducible part of scientific reasoning and sees no harm in
making them explicit. The frequentist conception of evidence based on
\(p\)-values avoids these explicitly subjective elements, but at the
price of a misleading impression of objectivity and frequent abuse in
practice. A defense of frequentist inference should, in our opinion,
stress that the relatively rigid rules for interpreting statistical
evidence facilitate communication and assessment of research results
in the scientific community--something that is harder to achieve
for a Bayesian. We now turn from specific methods for stating and
interpreting evidence to a radical criticism of the idea that there is
a rational scientific method.


### 4.3 Feyerabend: The Tyranny of the Rational Method



In his writings of the 1970s,
 Paul Feyerabend
 launched a profound attack on the rationality and objectivity of
scientific method. His position is exceptional in the philosophical
literature since traditionally, the threat for objective and
successful science is located in contextual rather than epistemic
values. Feyerabend turns this view upside down: it is the
"tyranny" of rational method, and the emphasis on
epistemic rather than contextual values that prevents us from having a
science in the service of society. Moreover, he welcomes a diversity
of different personal, also idiosyncratic perspectives, thus denying
the idea that freedom from personal "bias" is
epistemically and socially beneficial.



The starting point of Feyerabend's criticism of rational method
is the thesis that strict epistemic rules such as those expressed by
the VFI only suppress an open exchange of ideas, extinguish scientific
creativity and prevent a free and truly democratic science. In his
classic "Against Method" (1975: chs. 8-13),
Feyerabend elaborates on this criticism by examining a famous episode
in the history of science. When the Catholic Church objected to
Galilean mechanics, it had the better arguments by the standards of
seventeenth-century science. Their conservatism in their position was
scientifically backed: Galilei's telescopes were unreliable for
celestial observations, and many well-established phenomena (no fixed
star parallax, invariance of laws of motion) could not yet be
explained in the heliocentric system. With hindsight, Galilei managed
to achieve groundbreaking scientific progress just because he
deliberately violated rules of scientific reasoning. Hence
Feyerabend's dictum "Anything goes": no methodology
whatsoever is able to capture the creative and often irrational ways
by which science deepens our understanding of the world. Good
scientific reasoning cannot be captured by rational method, as Carnap,
Hempel and Popper postulated.



The drawbacks of an objective, value-free and method-bound view on
science and scientific method are not only epistemic. Such a view
narrows down our perspective and makes us less free, open-minded,
creative, and ultimately, less human in our thinking (Feyerabend 1975:
154). It is therefore neither possible nor desirable to have an
objective, value-free science (cf. Feyerabend 1978: 78-79). As a
consequence, Feyerabend sees traditional forms of inquiry about our
world (e.g., Chinese medicine) on a par with their Western
competitors. He denounces appeals to "objective" standards
as rhetorical tools for bolstering the epistemic authority of a small
intellectual elite (=Western scientists), and as barely disguised
statements of preference for one's own worldview:



> 
> 
> there is hardly any difference between the members of a
> "primitive" tribe who defend their laws because they are
> the laws of the gods [...] and a rationalist who appeals to
> "objective" standards, except that the former know what
> they are doing while the latter does not. (1978: 82)
> 
> 
> 



In particular, when discussing other traditions, we often project our
own worldview and value judgments into them instead of making an
impartial comparison (1978: 80-83). There is no purely rational
justification for dismissing other perspectives in favor of the
Western scientific worldview--the insistence on our Western
approach may be as justified as insisting on absolute space and time
after the Theory of Relativity.



The Galilei example also illustrates that personal perspective and
idiosyncratic "bias" need not be bad for science.
Feyerabend argues further that scientific research is accountable to
society and should be kept in check by democratic institutions, and
laymen in particular. Their particular perspectives can help to
determine the funding agenda and to set ethical standards for
scientific inquiry, but also be useful for traditionally value-free
tasks such as choosing an appropriate research method and assessing
scientific evidence. Feyerabend's writings on this issue were
much influenced by witnessing the Civil Rights Movement in the U.S.
and the increasing emancipation of minorities, such as Blacks, Asians
and Hispanics.



All this is not meant to say that truth loses its function as a
normative concept, nor that all scientific claims are equally
acceptable. Rather, Feyerabend advocates an **epistemic
pluralism** that accepts diverse approaches to acquiring
knowledge. Rather than defending a narrow and misleading ideal of
objectivity, science should respect the diversity of values and
traditions that drive our inquiries about the world (1978:
106-107). This would put science back into the role it had
during the scientific revolution or the Enlightenment: as a liberating
force that fought intellectual and political oppression by the
sovereign, the nobility or the clergy. Objections to this view are
discussed at the end of
 section 5.2.


## 5. Objectivity as a Feature of Scientific Communities and Their Practices



This section addresses various accounts that regard scientific
objectivity essentially as a function of social practices in science
and the social organization of the scientific community. All these
accounts reject the characterization of scientific objectivity as a
function of correspondence between theories and the world, as a
feature of individual reasoning practices, or as pertaining to
individual studies and experiments (see also Douglas 2011). Instead,
they evaluate the objectivity of a *collective* of studies, as
well as the methods and community practices that structure and guide
scientific research. More precisely, they adopt a meta-analytic
perspective for assessing the reliability of scientific results
(section 5.1), and they construct objectivity from a feminist
perspective: as an open interchange of mutual criticism, or as being
anchored in the "situatedness" of our scientific practices
and the knowledge we gain
 (section 5.2).


### 5.1 Reproducibility and the Meta-Analytic Perspective



The collectivist perspective is especially useful when an entire
discipline enters a stage of crisis: its members become convinced that
a significant proportion of findings are not trustworthy. A
contemporary example of such a situation is the **replication
crisis**, which was briefly mentioned in the previous section
and concerns the *reproducibility* of scientific knowledge
claims in a variety of different fields (most prominently: psychology,
biology, medicine). Large-scale replication projects have noticed that
many findings which we considered as an integral part of scientific
knowledge failed to replicate in settings that were designed to mimic
the original experiment as closely as possible (e.g., Open Science
Collaboration 2015). Successful attempts at replicating an
experimental result have long been argued to provide evidence of
freedom from particular kinds of artefacts and thus the
trustworthiness of the result. Compare the
 entry on experiment in physics.
 Likewise, failure to replicate indicates that either the original
finding, the result of the replication attempt, or both, are
biased--though see John Norton's (ms., ch. 3--see
Other Internet Resources) arguments that the evidential value of
(failed) replications crucially depends on researchers' material
background assumptions.



When replication failures in a discipline are particularly
significant, one may conclude that the published literature lacks
objectivity--at a minimum the discipline fails to inspire trust
that its findings are more than artefacts of the researchers'
efforts. Conversely, when observed effects can be replicated in
follow-up experiments, a kind of objectivity is reached that goes
beyond the ideas of freedom from personal bias, mechanical
objectivity, and subject-independent measurement, discussed in
 section 4.1.



Freese and Peterson (2018) call this idea **statistical
objectivity**. It grounds in the view that even the most
scrupulous and diligent researchers cannot achieve full objectivity
all by themselves. The term "objectivity" instead applies
to a collection or population of studies, with
**meta-analysis** (a formal method for aggregating the
results from ranges of studies) as the "apex of
objectivity" (Freese and Peterson 2018, 304; see also Stegenga
2011, 2018). In particular, aggregating studies from different
researchers may provide evidence of systematic bias and questionable
research practices (QRP) in the published literature. This diagnostic
function of meta-analysis for detecting violations of objectivity is
enhanced by statistical techniques such as the funnel plot and the
\(p\)-curve (Simonsohn et al. 2014).



Apart from this epistemic dimension, research on statistical
objectivity also has an activist dimension: methodologists urge
researchers to make publicly available essential parts of their
research before the data analysis starts, and to make their methods
and data sources more transparent. For example, it is conjectured that
the replicability (and thus objectivity) of science will increase by
making all data available online, by preregistering experiments, and
by using the registered reports model for journal articles (i.e., the
journal decides on publication before data collection on the basis of
the significance of the proposed research as well as the experimental
design). The idea is that transparency about the data set and the
experimental design will make it easier to stage a replication of an
experiment and to assess its methodological quality. Moreover,
publicly committing to a data analysis plan beforehand will lower the
rate of QRPs and of attempts to *accommodate* data to
hypotheses rather than making proper predictions.



All in all, statistical objectivity moves the discussion of
objectivity to the level of population of studies. There, it takes up
and modifies several conceptions of objectivity that we have seen
before: most prominently, freedom of subjective bias, which is
replaced with collective bias and pernicious conventions, and the
subject-independent measurement of a physical quantity, which is
replaced by reproducibility of effects.


### 5.2 Feminist and Standpoint Epistemology



Traditional notions of objectivity as faithfulness to facts or freedom
of contextual values have also been challenged from a feminist
perspective. These critiques can be grouped in three major research
programs: feminist epistemology, feminist standpoint theory and
feminist postmodernism (Crasnow 2013). The program of **feminist
epistemology** explores the impact of sex and gender on the
production of scientific knowledge. More precisely, feminist
epistemology highlights the epistemic risks resulting from the
systematic exclusion of women from the ranks of scientists, and the
neglect of women as objects of study. Prominent case studies are the
neglect of female orgasm in biology, testing medical drugs on male
participants only, focusing on male specimen when studying the social
behavior of primates, and explaining human mating patterns by means of
imaginary neolithic societies (e.g., Hrdy 1977; Lloyd 1993, 2005). See
also the
 entry on feminist philosophy of biology.



Often but not always, feminist epistemologists go beyond pointing out
what they regard as androcentric bias and reject the value-free ideal
altogether--with an eye on the social and moral responsibility of
scientific inquiry. They try to show that a value-laden science can
also meet important criteria for being epistemically reliable and
objective (e.g., Anderson 2004; Kourany 2010). A classical
representative of such efforts is Longino's (1990)
**contextual empiricism**. She reinforces Popper's
insistence that "the objectivity of scientific statements lies
in the fact that they can be inter-subjectively tested" (1934
[2002]: 22), but unlike Popper, she conceives scientific knowledge
essentially as a social product. Thus, our conception of scientific
objectivity must directly engage with the social process that
generates knowledge. Longino assigns a crucial function to social
systems of criticism in securing the epistemic success of science.
Specifically, she develops an epistemology which regards a method of
inquiry as "objective to the degree that it permits
**transformative criticism**" (Longino 1990: 76).
For an epistemic community to achieve transformative criticism, there
must be:


* **avenues for criticism**: criticism is an essential part
of scientific institutions (e.g., peer review);
* **shared standards**: the community must share a set of
cognitive values for assessing theories (more on this in
 section 3.1);
* **uptake of criticism**: criticism must be able to
transform scientific practice in the long run;
* **equality of intellectual authority**: intellectual
authority must be shared equally among qualified practitioners.



Longino's contextual empiricism can be understood as a
development of John Stuart Mill's view that beliefs should never
be suppressed, independently of whether they are true or false. Even
the most implausible beliefs might be true, and even if they are
false, they might contain a grain of truth which is worth preserving
or helps to better articulate true beliefs (Mill 1859 [2003: 72]). The
underlying intuition is supported by recent empirical research on the
epistemic benefits of a diversity of opinions and perspectives (Page
2007). By stressing the social nature of scientific knowledge, and the
importance of criticism (e.g., with respect to potential androcentric
bias and inclusive practice), Longino's account fits into the
broader project of feminist epistemology.



**Standpoint theory** undertakes a more radical attack on
traditional scientific objectivity. This view develops Marxist ideas
to the effect that epistemic position is related to, and a product of,
social position. Feminist standpoint theory builds on these ideas but
focuses on gender, racial and other social relations. Feminist
standpoint theorists and proponents of **"situated
knowledge"** such as Donna Haraway (1988), Sandra Harding
(1991, 2015a, 2015b) and Alison Wylie (2003) deny the internal
coherence of a view from nowhere: all human knowledge is at base
*human* knowledge and therefore necessarily perspectival. But
they argue more than that. Not only is perspectivality the human
condition, it is also a good thing to have. This is because
perspectives, especially the perspectives of underprivileged classes
and groups in society, come along with epistemic benefits. These ideas
are controversial but they draw attention to the possibility that
attempts to rid science of perspectives might not only be futile but
also costly: they prevent scientists from having the epistemic
benefits certain standpoints afford and from developing knowledge
*for* marginalized groups in society. The perspectival stance
can also explain why criteria for objectivity often vary with context:
the relative importance of epistemic virtues is a matter of goals and
interests--in other words, standpoint.



By endorsing a perspectival stance, feminist standpoint theory rejects
classical elements of scientific objectivity such as neutrality and
impartiality (see
 section 3.1
 above). This is a notable difference to feminist epistemology, which
is in principle (though not always in practice) compatible with
traditional views of objectivity. Feminist standpoint theory is also a
political project. For example, Harding (1991, 1993) demands that
scientists, their communities and their practices--in other
words, the ways through which knowledge is gained--be
investigated as rigorously as the object of knowledge itself. This
idea she refers to as **"strong
objectivity"** replaces the "weak" conception
of objectivity in the empiricist tradition: value-freedom,
impartiality, rigorous adherence to methods of testing and inference.
Like Feyerabend, Harding integrates a transformation of epistemic
standards in science into a broader political project of rendering
science more democratic and inclusive. On the other hand, she is
exposed to similar objections (see also Haack 2003). Isn't it
grossly exaggerated to identify class, race and gender as important
factors in the construction of physical theories? Doesn't the
feminist approach--like social constructivist
approaches--lose sight of the particular epistemic qualities of
science? Should non-scientists really have as much authority as
trained scientists? To whom does the condition of equally shared
intellectual authority apply? Nor is it clear--especially in
times of fake news and filter bubbles--whether it is always a
good idea to subject scientific results to democratic approval. There
is no guarantee (arguably there are few good reasons to believe) that
democratized or standpoint-based science leads to more reliable
theories, or better decisions for society as a whole.


## 6. Issues in the Special Sciences



So far everything we discussed was meant to apply across all or at
least most of the sciences. In this section we will look at a number
of specific issues that arise in the social sciences, in economics,
and in evidence-based medicine.


### 6.1 Max Weber and Objectivity in the Social Sciences



There is a long tradition in the philosophy of social science
maintaining that there is a gulf in terms of both goals as well as
methods between the natural and the social sciences. This tradition,
associated with thinkers such as the neo-Kantians Heinrich Rickert and
Wilhelm Windelband, the hermeneuticist Wilhelm Dilthey, the
sociologist-economist Max Weber, and the twentieth-century
hermeneuticists Hans-Georg Gadamer and Michael Oakeshott, holds that
unlike the natural sciences whose aim it is to establish natural laws
and which proceed by experimentation and causal analysis, the social
sciences seek understanding ("*Verstehen*") of
social phenomena, the interpretive examination of the meanings
individuals attribute to their actions (Weber 1904 [1949]; Weber 1917
[1949]; Dilthey 1910 [1986]; Windelband 1915; Rickert 1929; Oakeshott
1933; Gadamer 1960 [1989]). See also the entries on
 hermeneutics
 and
 Max Weber.



Understood this way, social science lacks objectivity in more than one
sense. One of the more important debates concerning objectivity in the
social sciences concerns the role value judgments play and,
importantly, whether value-laden research entails claims about the
desirability of actions. Max Weber held that the social sciences are
necessarily value laden. However, they can achieve some degree of
objectivity by keeping out the social researcher's views about
whether agents' goals are commendable. In a similar vein,
contemporary economics can be said to be value laden because it
predicts and explains social phenomena on the basis of agents'
preferences. Nevertheless, economists are adamant that economists are
not in the business of telling people what they ought to value. Modern
economics is thus said to be objective in the Weberian sense of
**"absence of researchers'
values"**--a conception that we discussed in detail
in
 section 3.



In his widely cited essay "'Objectivity' in Social
Science and Social Policy" (Weber 1904 [1949]), Weber argued
that the idea of an aperspectival social science was meaningless:



> 
> 
> There is no absolutely objective scientific analysis of [...]
> "social phenomena" independent of special and
> "one-sided" viewpoints according to which expressly or
> tacitly, consciously or unconsciously they are selected, analyzed and
> organized for expository purposes. (1904 [1949: 72])
> 
> 
> 
> All knowledge of cultural reality, as may be seen, is always knowledge
> from particular points of view. (1904 [1949:. 81])
> 
> 
> 



The reason for this is twofold. First, social reality is too complex
to admit of full description and explanation. So we have to select.
But, perhaps in contraposition to the natural sciences, we cannot just
select those aspects of the phenomena that fall under universal
natural laws and treat everything else as "unintegrated
residues" (1904 [1949: 73]). This is because, second, in the
social sciences we want to understand social phenomena in their
individuality, that is, in their unique configurations that have
significance for us.



Values solve a selection problem. They tell us what research questions
we ought to address because they inform us about the cultural
importance of social phenomena:



> 
> 
> Only a small portion of existing concrete reality is colored by our
> value-conditioned interest and it alone is significant to us. It is
> significant because it reveals relationships which are important to
> use due to their connection with our values. (1904 [1949: 76])
> 
> 
> 



It is important to note that Weber did not think that social and
natural science were different in kind, as Dilthey and others did.
Social science too examines the causes of phenomena of interest, and
natural science too often seeks to explain natural phenomena in their
individual constellations. The role of causal laws is different in the
two fields, however. Whereas establishing a causal law is often an end
in itself in the natural sciences, in the social sciences laws play an
attenuated and accompanying role as mere means to explain cultural
phenomena in their uniqueness.



Nevertheless, for Weber social science remains objective in at least
two ways. First, once research questions of interest have been
settled, answers about the causes of culturally significant phenomena
do not depend on the idiosyncrasies of an individual researcher:



> 
> 
> But it obviously does not follow from this that research in the
> cultural sciences can only have results which are
> "subjective" in the sense that they are valid for one
> person and not for others. [...] For scientific truth is
> precisely what is valid for all who seek the truth. (Weber 1904 [1949:
> 84], emphasis original)
> 
> 
> 



The claims of social science can therefore be objective in our third
sense
 (see section 4).
 Moreover, by determining that a given phenomenon is "culturally
significant" a researcher reflects on whether or not a practice
is "meaningful" or "important", and not
whether or not it is commendable: "Prostitution is a cultural
phenomenon just as much as religion or money" (1904 [1949: 81]).
An important implication of this view came to the fore in the
so-called "*Werturteilsstreit*" (quarrel concerning
value judgments) of the early 1900s. In this debate, Weber maintained
against the "socialists of the lectern" around Gustav
Schmoller the position that social scientists qua scientists should
not be directly involved in policy debates because it was not the aim
of science to examine the appropriateness of ends. Given a policy
goal, a social scientist could make recommendations about effective
strategies to reach the goal; but social science was to be value-free
in the sense of not taking a stance on the desirability of the goals
themselves. This leads us to our conception of objectivity as freedom
from value judgments.


### 6.2 Contemporary Rational Choice Theory



Contemporary mainstream economists hold a view concerning objectivity
that mirrors Max Weber's (see above). On the one hand, it is
clear that value judgments are at the heart of economic theorizing.
"Preferences" are a key concept of rational choice theory,
the main theory in contemporary mainstream economics. Preferences are
evaluations. If an individual prefers \(A\) to \(B\), she
*values* \(A\) higher than \(B\) (Hausman 2012). Thus, to the
extent that economists predict and explain market behavior in terms of
rational choice theory, they predict and explain market behavior in a
way laden with value judgments.



However, economists are not themselves supposed to take a stance about
whether or not whatever individuals value is also
"objectively" good in a stronger sense:



> 
> 
> [...] that an agent is rational from [rational choice
> theory]'s point of view does not mean that the course of action
> she will choose is objectively optimal. Desires do not have to align
> with any objective measure of "goodness": I may want to
> risk swimming in a crocodile-infested lake; I may desire to smoke or
> drink even though I know it harms me. Optimality is determined by the
> agent's desires, not the converse. (Paternotte 2011:
> 307-8)
> 
> 
> 



In a similar vein, Gul and Pesendorfer write:



> 
> 
> However, standard economics has no therapeutic ambition, i.e., it does
> not try to evaluate or improve the individual's objectives.
> Economics cannot distinguish between choices that maximize happiness,
> choices that reflect a sense of duty, or choices that are the response
> to some impulse. Moreover, standard economics takes no position on the
> question of which of those objectives the agent should pursue. (Gul
> and Pesendorfer 2008: 8)
> 
> 
> 



According to the standard view, all that rational choice theory
demands is that people's preferences are (internally)
consistent; it has no business in telling people what they ought to
prefer, whether their preferences are consistent with external norms
or values. Economics is thus value-laden, but laden with the values of
the agents whose behavior it seeks to predict and explain and not with
the values of those who seek to predict and explain this behavior.



Whether or not social science, and economics in particular, can be
objective in this--Weber's and the contemporary
economists'--sense is controversial. On the one hand, there
are some reasons to believe that rational choice theory (which is at
work not only in economics but also in political science and other
social sciences) cannot be applied to empirical phenomena without
referring to external norms or values (Sen 1993; Reiss 2013).



On the other hand, it is not clear that economists and other social
scientists qua social scientists shouldn't participate in a
debate about social goals. For one thing, trying to do welfare
analysis in the standard Weberian way tends to obscure rather than to
eliminate normative commitments (Putnam and Walsh 2007). Obscuring
value judgments can be detrimental to the social scientist as policy
adviser because it will hamper rather than promote trust in social
science. For another, economists are in a prime position to contribute
to ethical debates, for a variety of reasons, and should therefore
take this responsibility seriously (Atkinson 2001).


### 6.3 Evidence-based Medicine and Social Policy



The same demands calling for "mechanical objectivity" in
the natural sciences and quantification in the social and policy
sciences in the nineteenth century and mid-twentieth century are
responsible for a recent movement in biomedical research, which, even
more recently, have swept to contemporary social science and policy.
Early proponents of so-called "evidence-based medicine"
made their pursuit of a downplay of the "human element" in
medicine plain:



> 
> 
> Evidence-based medicine de-emphasizes intuition, unsystematic clinical
> experience, and pathophysiological rationale as sufficient grounds for
> clinical decision making and stresses the examination of evidence from
> clinical research. (Guyatt et al. 1992: 2420)
> 
> 
> 



To call the new movement "evidence-based" is a misnomer
strictly speaking, as intuition, clinical experience and
pathophysiological rationale can certainly constitute evidence. But
proponents of evidence-based practices have a much narrower concept of
evidence in mind: analyses of the results of randomized controlled
trials (RCTs). This movement is now very strong in biomedical
research, development economics and a number of areas of social
science, especially psychology, education and social policy, and
especially in the English speaking world.



The goal is to replace subjective (biased, error-prone, idiosyncratic)
judgments by mechanically objective methods. But, as in other areas,
attempting to mechanize inquiry can lead to reduced accuracy and
utility of the results.



Causal relations in the social and biomedical sciences hold on account
of highly complex arrangements of factors and conditions. Whether for
instance a substance is toxic depends on details of the metabolic
system of the population ingesting it, and whether an educational
policy is effective on the constellation of factors that affect the
students' learning progress. If an RCT was conducted
successfully, the conclusion about the effectiveness of the treatment
(or toxicity of a substance) under test is certain for the particular
arrangement of factors and conditions of the trial (Cartwright 2007).
But unlike the RCT itself, many of whose aspects can be (relatively)
mechanically implemented, applying the result to a new setting
(recommending a treatment to a patient, for instance) always involves
subjective judgments of the kind proponents of evidence-based
practices seek to avoid--such as judgments about the similarity
of the test to the target or policy population.



On the other hand, RCTs can be regarded as "debiasing
procedure" because they prevent researchers from allocating
treatments to patients according to their personal interests, so that
the healthiest (or smartest or...) subjects get the
researcher's favorite therapy. While unbalanced allocations can
certainly happen by chance, randomization still provides some warrant
that the allocation was not done *on purpose* with a view to
promoting somebody's interests. *A priori*, the
experimental procedure is thus more impartial with respect to the
interests at stake. It has thus been argued that RCTs in medicine,
while no guarantor of the best outcomes, were adopted by the U.S. Food
and Drugs Administration (FDA) to different degrees during the 1960s
and 1970s in order to regain public trust in its decisions about
treatments, which it had lost due to the thalidomide and other
scandals (Teira and Reiss 2013; Teira 2010). It is important to
notice, however, that randomization is at best effective with respect
to one kind of bias, viz. selection bias. Important other epistemic
concerns are not addressed by the procedure but should not be ignored
(Worrall 2002).


## 7. The Unity and Disunity of Scientific Objectivity



In sections 2-5, we have encountered various concepts of
scientific objectivity and their limitations. This prompts the
question of how unified (or disunified) scientific objectivity is as a
concept: Is there something substantive shared by all of these
analyses? Or is objectivity, as Heather Douglas (2004) puts it, an
"irreducibly complex" concept?



Douglas defends **pluralism about scientific
objectivity** and distinguishes three areas of application of
the concept: (1) interaction of humans with the world, (2) individual
reasoning processes, (3) social processes in science. Within each
area, there are various distinct senses which are again irreducible to
each other and do not have a common core meaning. This does not mean
that the senses are unrelated; they share a complex web of
relationships and can also support each other--for example,
eliminating values from reasoning may help to achieve procedural
objectivity. For Douglas, reducing objectivity to a single core
meaning would be a simplification without benefits; instead of a
complex web of relations between different senses of objectivity we
would obtain an impoverished concept out of touch with scientific
practice. Similar arguments and pluralist accounts can be found in
Megill (1994), Janack (2002) and Padovani et al. (2015)--see also
Axtell (2016).



It has been argued, however, that pluralist approaches give up too
quickly on the idea that the different senses of objectivity share one
or several important common elements. As we have seen in section
 4.1
 and
 5.1,
 scientific objectivity and **trust in science** are
closely connected. Scientific objectivity is desirable because to the
extent that science is objective we have reasons trust scientists,
their results and recommendations (cf. Fine 1998: 18). Thus, perhaps
what is unifying among the difference senses of objectivity is that
each sense describes a feature of scientific practice that is able to
inspire trust in science.



Building on this idea, Inkeri Koskinen has recently argued that it is
in fact not trust but reliance that we are after (Koskinen
forthcoming). Trust is something that can be betrayed, but only
individuals can betray whereas objectivity pertains to institutions,
practices, results, etc. We call scientific institutions, practices,
results, etc. objective to the extent that we have reasons to rely on
them. The analysis does not stop here, however. There is a distinct
view about objectivity that is behind Daston and Galison's
historical epistemology of the concept and has been defended by Ian
Hacking: that objectivity is not a--positive--virtue but
rather the absence of this or that vice (Hacking 2015: 26). Speaking
of objectivity in imaging, for instance, Daston and Galison write that
the goal is to



> 
> 
> let the specimen appear without that distortion characteristic of the
> observer's personal tastes, commitments, or ambitions. (Daston
> and Galison 2007: 121)
> 
> 
> 



Koskinen picks up this idea of **objectivity as absence of
vice** and argues that it is specifically the aversion of
*epistemic risks* for which the term is reserved. Epistemic
risks comprise "any risk of epistemic error that arises anywhere
during knowledge practices' (Biddle and Kukla 2017: 218) such as
the risk of having mistaken beliefs, the risk of errors in reasoning
and risks related to operationalization, concept formation, and model
choice. Koskinen argues that only those epistemic risks that relate to
failings of scientists as human beings are relevant to objectivity
(Koskinen forthcoming: 13):



> 
> 
> For instance, when the results of an experiment are incorrect because
> of malfunctioning equipment, we do not worry about
> objectivity--we just say that the results should not be taken
> into account. [...] So it is only when the epistemic risk is related
> to our own failings, and is hard to avert, that we start talking about
> objectivity. Illusions, subjectivity, idiosyncrasies, and collective
> biases are important epistemic risks arising from our imperfections as
> epistemic agents.
> 
> 
> 



Koskinen understands her account as a response to Hacking's
(2015) criticism that we should stop talking about objectivity
altogether. According to Hacking, "objectivity" is an
"elevator" or second-level word, similar to
"true" or "real"--"Instead of
saying that the cat is on the mat, we move up one story and and say
that it is true that the cat is on the mat" (2015: 20). He
recommends to stick to ground-level questions and worry about whether
specific sources of error have been controlled. (A similar elimination
request with respect to the labels "objective" and
"subjective" in statistical inference has been advanced by
Gelman and Hennig (2017).) In focussing on averting specific epistemic
risks, Koskinen's account does precisely that. Koskinen argues
that a unified account of objectivity as averting epistemic risks
takes into account Hacking's negative stance and explains at the
same time important features of the concept--for example, why
objectivity does not imply certainty and why it varies with
context.



The strong point of this account is that none of the threats to a
peculiar analysis puts scientific objectivity at risk. We can (and in
fact, we do) rely on scientific practices that represent the world
from a perspective and where non-epistemic values affect outcomes and
decisions. What is left open by Koskinen's account is the
normative question of what a scientist who cares about her experiments
and inferences being objective should actually do. That is, the
philosophical ideas we have reviewed in this section stay mainly on
the descriptive level and do not give an actual guideline for working
scientists. Connecting the abstract philosophical analysis to
day-to-day work in science remains an open problem.


## 8. Conclusions



So is scientific objectivity desirable? Is it attainable? That, as we
have seen, depends crucially on how the term is understood. We have
looked in detail at four different conceptions of scientific
objectivity: faithfulness to facts, value-freedom, freedom from
personal biases, and features of community practices. In each case,
there are at least some reasons to believe that either science cannot
deliver full objectivity in this sense, or that it would not be a good
thing to try to do so, or both. Does this mean we should give up the
idea of objectivity in science?



We have shown that it is hard to define scientific objectivity in
terms of a view from nowhere, value freedom, or freedom from personal
bias. It is a lot harder to say anything positive about the matter.
Perhaps it is related to a thorough critical attitude concerning
claims and findings, as Popper thought. Perhaps it is the fact that
many voices are heard, equally respected and subjected to accepted
standards, as Longino defends. Perhaps it is something else
altogether, or a combination of several factors discussed in this
article.



However, one should not (as yet) throw out the baby with the
bathwater. Like those who defend a particular explication of
scientific objectivity, the critics struggle to explain what makes
science objective, trustworthy and special. For instance, our
discussion of the value-free ideal (VFI) revealed that alternatives to
the VFI are as least as problematic as the VFI itself, and that the
VFI may, with all its inadequacies, still be a useful heuristic for
fostering scientific integrity and objectivity. Similarly, although
entirely "unbiased" scientific procedures may be
impossible, there are many mechanisms scientists can adopt for
protecting their reasoning against undesirable forms of bias, e.g.,
choosing an appropriate method of statistical inference, being
transparent about different stages of the research process and
avoiding certain questionable research practices.



Whatever it is, it should come as no surprise that finding a positive
characterization of what makes science objective is hard. If we knew
an answer, we would have done no less than solve the problem of
induction (because we would know what procedures or forms of
organization are responsible for the success of science). Work on this
problem is an ongoing project, and so is the quest for understanding
scientific objectivity.