The Nature and Philosophy of Science
Wade A. Tisthammer
Introduction
Scientists are unbiased observers who
use
the scientific method to conclusively
confirm
and conclusively falsify various theories.
These experts have no preconceptions
in gathering
the data and logically derive theories
from
these objective observations. One great
strength
of science is that it’s self-correcting,
because scientists readily abandon
theories
when they are shown to be irrational.
Although
such eminent views of science have
been accepted
by many people, they are almost completely
untrue. Data can neither conclusively
confirm
nor conclusively falsify theories,
there
really is no such thing as the scientific
method, data become somewhat subjective
in
practice, and scientists have displayed
a
surprisingly fierce loyalty to their
theories.
There have been many misconceptions
of what
science is and is not. I’ll discuss
why these
misconstruals are inaccurate later,
but first
I’d like to begin by talking about
some of
the basics of what science is.
Science is a project whose goal is
to obtain
knowledge of the natural world. The
philosophy
of science is a discipline that deals
with
the system of science itself. It examines
science’s structure, components, techniques,
assumptions, limitations, and so forth.
The Basic Structure of Science
To properly understand the contemporary
philosophy
of science, it is necessary to examine
some
basic components of science. The components
of science are data, theories, and
what is
sometimes called shaping principles.[1]
Data are the collections of information
about
physical processes.[2] Sometimes collecting
and finding data to support theories
can
be rather laborious. This is because
the
specific details of data that come
into play
can make science such a tricky business
that
some scientists, when talking to laymen,
sometime leave them out. Also, it is
easy
to fit a theory in with the data if
the data
are vague and overgeneralized. It usually
becomes more difficult to fit the theory
with specific data, especially since
the
details make it more likely for the
theory
to become less plausible. Even so,
data are
important parts of theories and of
science.[3]
Theories come in roughly two forms.
Contrary
to what some might think, a theory
in the
scientific sense does not have anything
to
do with whether or not it is supported
by
the evidence, contradicted by the evidence,
well liked among scientists, and so
forth.[4]
It only has to do with its structure
and
the way it functions. That is, just
because
a theory is a scientific theory does
not
mean that the scientific community
currently
accepts it. There are many theories
that,
though technically scientific, have
been
rejected because the scientific evidence
is strongly against it. Phenomenological
theories are empirical generalizations
of
data. They merely describe the recurring
processes of nature and do not refer
to their
causes or mechanisms. Phenomenological
theories
are also called scientific laws, physical
laws, and natural laws. Newton’s third
law
is one example. It says that every
action
has an equal and opposite reaction.
Explanatory
theories attempt to explain the observations
rather than generalize them. Whereas
laws
are descriptions of empirical regularities,
explanatory theories are conceptual
constructions
to explain why the data exist. For
example,
atomic theory explains why we see certain
observations. The same could be said
with
DNA and relativity. Explanatory theories
are particularly helpful in such cases
where
the entities (like atoms, DNA, and
so forth)
cannot be directly observed.
Shaping principles are non-empirical
factors
and assumptions that form the basis
of science
and go into selecting a “good” theory.
Why
are they necessary? Can’t theories
be selected
solely on the basis of empirical data?
Surprisingly,
the answer is no. Why not? Describing
some
mistaken views of science come in handy
for
explaining the answer.
Mistaken Beliefs of the Scientific
Method
Many students (including me) were brought
up with a somewhat eminent view of
science,
or at least a fairly eminent view of
science
as it should be done. As I have found
however,
the status of science which most of
us were
taught may have been a bit misleading.
Some
ideas of what “the scientific method”
is
have also been erroneous. This is perhaps
because scientists themselves tend
to be
ignorant of the philosophy of science.[5]
Changes have been made in history about
what
science is and how it should be done.
In the early years of science, the
system
of acquiring knowledge was viewed as
completely
objective, rational, and empirical.[6]
This
traditional view of science held that
scientific
theories and laws were to be conclusively
confirmed or conclusively falsified
based
on objective data. This was supposed
to be
done through “the scientific method.”
Apparently
some sort of method was necessary because
humans seemed to have a variety of
tendencies
and feelings that were not very trustworthy,
including biases, feelings, intuitions,
and
so forth. These kinds of things had
to be
prevented from infecting science so
that
knowledge could be reliably obtained.[7]
Rigorous and precise procedure (“the
scientific
method”) was to be followed so that
such
imperfections of humanity would not
hinder
the process of discovering nature.
Baconian inductivism in the early seventeenth
century was at one point considered
to be
the scientific method. The basic idea
at
the time was this: collect numerous
observations
(as much as humanly possible) being
unaffected
by any prior prejudice or theoretical
preconceptions
while gathering the data, inductively
infer
theories from those data (by generalizing
the data into physical laws), and collect
more data to modify or reject the hypothesis
if needed.[8] In many instances, this
concept
seemed to work. One can collect numerous
observations of physical processes
and experiments
to derive natural laws, such the conservation
of mass-energy. Alas, Baconian inductivism
is an inaccurate picture of scientific
method.
When using inductivism to arrive at
natural
laws, certain theoretical preconceptions
are absolutely vital. To generalize
the data
into physical laws, the individual
must assume
that the laws apply for physical processes
not observed. This results in several
assumptions
being held, such as the uniform operation
of nature. Even if we put aside the
fact
that inductive logic is invariably
based
on such postulations, there is another
problem.
Science deals with concepts and explanatory
theories that cannot be directly observed,
including atomic theory and the theory
of
gravity. Many other theories include
unobservable
concepts like forces, fields, and subatomic
particles. There is no known rigorous
inductive
logic that can infer those theories
and concepts
solely from the data they explain.
If inductivism
is the correct scientific method, then
such
theories cannot be legitimate science.
As
if these difficulties weren’t enough,
inductivism
has other major technical problems
that have
led to its demise.[9]
Sir Isaac Newton developed hypothetico-deductivism
in the late 1600s (though the method
was
actually named at a later date).[10]
Essentially,
one starts with a hypothesis
(a hypothesis is basically a provisional
theory) and then deduces what we would
expect
to find in the empirical world as a
result
of that hypothesis, hence the name
hypothetico-deductivism.
Here the idea was to quarantine human
irrationality.[11]
One could make a theory for any or
no reason.
The sources of theories would be irrelevant
in hypothetico-deductivism since the
theories
could be tested against the empirical
world
and be confirmed or refuted that way.
A theory
did not become a good theory by its
origins,
but because of the hypothetico-deductive
method of verification.[12] Inductivism,
recall, could not work because empirical
data cannot be the sole source of a
theory.
Some scientists and philosophers of
science
who rejected inductivism embraced hypothetico-deductivism.
A significant reason is that it allowed
ideas
like atomic theory to be legitimate
science
whereas they would not be in inductivism.
Unfortunately, hypothetico-deductivism
also
has problems. The philosophy that rigorous
proof is necessary for good science
has serious
problems even if we assume that sense
experience,
memory, and testimony are all generally
reliable.[13]
For one thing, we cannot be sure that
we
have examined all the germane data.[14]
There
is always the opportunity for future
observations
to topple even the most established
of theories.[15]
For example, there is always the possibility
that an observation could conflict
with any
known scientific law. This is what
caused
Newtonian mechanics to be cut down
to size.
Rather than being a total account for
the
nature and dynamics of the universe,
Einstein,
Heisenberg, and other physicists demonstrated
that the realm of Newtonian mechanics
is
much more restricted than what was
once thought.
Unrevealed data can also contradict
the predictions
of any explanatory theory as well.
Every
theory has an infinite number of expected
empirical outcomes, and we are incapable
of testing all of those expectations.
So
even though a theory can be confirmed
to
some extent by empirical data, it can
never
be conclusively confirmed. Apart from
this,
hypothetico-deductivism’s method of
verification
has this sort of structure where T
is a theory
and D a set of data that we would expect
if the theory were true:
If T then D. D. Therefore, T.
This is not a logically valid argument.
Indeed,
an argument of this sort of structure
is
called the fallacy of affirming the
conclusion.[16]
Have T = “An invisible unicorn from
Mars
flew into the sky to cause rain,” and
D =
“It is raining.” Logically, the first
premise
must be correct (If T is true, then
D would
be true). Suppose the second premise
is correct.
It is raining. Even so, the conclusion
doesn’t
logically follow. Why doesn’t it work?
Because
there could be other possibilities
for D
other than T. That is, more than one
theory
could exist to explain the data. And
this
is indeed the case. In this example,
it could
simply be natural weather patterns,
not a
flying invisible unicorn from Mars,
that
caused the rain. In science or anywhere
else,
any given body of data (no matter how
large)
will always be agreeable with an unlimited
number of alternative theories. Invariably
there are many theories that explain
the
exact same data, and at least some
of the
theories will contradict each other.
This
fact is sometimes expressed as data
underdetermining
theories, or is simply referred to
as the
underdetermination of theories.[16]
Because
such competing theories are consistent
with
the same set of data, all of these
theories
are empirically identical. This means
that
empirical data by itself cannot exclusively
confirm one theory from among its empirically
indistinguishable competitors. Some
of these
empirically indistinguishable theories
may
be elegantly simple and others may
be outrageously
complex, but multiple alternatives
exist
for any set of data. There are examples
of
this problem in the real world. In
one such
instance, Tyco Brahe and Copernicus
each
had a competing theory of the solar
system.
It can be shown mathematically that
every
bit of data that is predicted by one
theory
would be predicted by the other theory.[17]
We may not always be able to think
of alternative
theories, but this only has to do with
problems
of human imagination in constructing
such
theories, not the logic of the circumstances.
Of course, the underdetermination of
theories
also poses yet another problem for
Baconian
inductivism. (Explanatory theories
cannot
be inferred from data alone if there
are
always numerous alternatives that explain
that same set of data.) As a result
of the
underdetermination of theories and
the risk
of undiscovered, contradictory empirical
evidence, a scientific theory cannot
be conclusively
proven merely through the data. Even
if we
take out the notion of conclusive proof
from
hypothetico-deductivism, it seems that
this
idea of the scientific method dreadfully
oversimplifies how science works. No
rational
scientist would accept the flying invisible
unicorn from mars theory simply because
it
passed the empirical confirmation test
in
the above example, for instance.
Popperian falsification is another
belief
of what the scientific method is. Karl
Popper,
regarded by many as one of the finest[18]
and most influential[19] philosophers
of
science of the twentieth century, realized
the flaws of inductivism and rejected
it.
Popper recognized that one could not
record
everything observed, because that is
simply
not feasible. Some sort of selection
is needed,
and thus observation is always selective.[20]
That being true, Popper believed that
a hypothesis
had to be created first for scientific
investigation
to begin. Otherwise there would be
no other
way to tell which data are germane.[21]
Since
theories must be created first in order
to
decide what observations were relevant,
such
theoretical preconceptions would be
essential
to doing science (contrary to Baconian
inductivism).[22]
This was one of the reasons he believed
inductivism
is unworkable. He also denied the concept
of conclusive proof and instead stressed
the idea that falsifiability is the
necessary
criterion for a theory to be legitimate
science.[23]
In other words, if a theory cannot
be falsified
through some conceivable observation,
then
such a theory is not genuine science.
The
necessity for a scientific theory to
be conclusively
falsifiable is known as the demarcation
criterion.[24]
This idea seemed reasonable enough,
since
scientific theories can make predictions.
Popperian falsification suggested that
if
a prediction does not come true, then
the
scientific theory must be false. Popper’s
idea of the scientific method was for
scientists
to test scientific theories in experiments
where the outcome could potentially
falsify
the theory, especially in experiments
where
the theory would most likely collapse.[25]
Science still had some of its traditional
quality in that it could make definite
progress
by conclusively eliminating theories.
Yet, like inductivism, Popper’s ideas
are
not entirely successful either. (Consequently,
some regard Popper’s contribution to
the
philosophy of science to be overrated.)[26]
Popper was certainly correct that data
are
selective, but they need not have a
theory
to guide the selection (though that
is often
the case). For instance, one can record
data
and apply assumptions to the data to
form
a theory, as is sometimes the case
with scientific
laws. (Note that since assumptions
need to
be accepted for the theory to be created,
this would not be an example of inductivism
without assumptions in action.) The
demarcation
criterion is even more flawed. Surprisingly,
the problem is that it is impossible
to conclusively
falsify theories by empirical data.
One reason
is that theories by themselves are
incapable
of making predictions. Instead, the
empirical
consequences of a theory invariably
rest
on background assumptions (also called
auxiliary
assumptions[27]) from which to derive
predictions
and even to obtain data.[28]
Suppose we have a particle theory that
says
if we process a certain particle in
a particular
way, we will get specified values on
various
measurements.
All theories (the particular electrical,
atomic, particle, etc. models that
are used)
involved in deriving the prediction
are correct;
The specific version of those theories
and
models (from #1) from which the predictions
are derived from are correct (for example,
belief in atoms have been widely accepted
for quite some time now, but the precise
details and models of the exact composition,
components etc. have significantly
varied.);
The prediction derived from those theories
and specific versions of those models
is
mathematically or logically correct;
and
Some other things we’ll skip.
Note that most of the items depend
on scientific
theories. But scientific theories,
remember,
cannot be conclusively proven. The
dependence
on background assumptions to make predictions
is sometimes called the Duhem-Quine
problem.[29]
There are real life examples of this
problem.
To “disprove” the idea that the earth
was
moving, some people noted that birds
did
not get thrown off into the sky whenever
they let go of a tree branch. That
data is
no longer accepted as empirical evidence
that the earth is not moving because
we have
adopted a different background system
of
physics that allows us to make different
predictions. So if a theory’s prediction
does not come true, one can claim that
the
theory is correct and that at least
one of
the auxiliary assumptions is wrong.
Besides using auxiliary assumptions
to make
predictions, such assumptions are necessary
to find out if the predictions come
true.
Suppose that in order to test our particle
theory in the real world we must use
a certain
particle accelerator in a particular
way.
To experimentally test this, we must
adhere
to the following statements:
All of the theories and models (particle,
electronic, engineering) used in what
we
believe happens inside this accelerator
are
correct (including the specifics);
All theories (electronics and so forth)
on
how the detector works are correct
(including
the specifics of the models involved);
Both the detection devices and the
accelerator
are operating are designed;
Both of the above devices are being
used
properly (including the assumption
that the
readings are recorded correctly); and
Some other things we’ll skip again.
Notice that several of the items are
again
dependent on scientific theories, which
cannot
be rigorously proven. Suppose the prediction
does not come true and we observe that,
“this
particle did not have the specified
properties
that it should’ve had.” That observation
would be heavily dependent on theories.
Although
it is possible that our theory could
be wrong,
it is also possible that instead one
or more
of the assumptions listed are wrong.
Often,
the terminology used to describe experimental
results in addition to the measurements
and
instruments used in testing theories
make
up another set of background assumptions.
The dependence on such postulations
for obtaining
data is described as observations being
theory-laden.[30]
In this example, we have to assume
these
kinds of assumptions (including #1,
#2, #3,
and #4 on the list above) to accept
the observation
of what properties the particle produced.
A completely theoretically neutral
language
for recording data is not always possible.[31]
Suppose Bob’s prediction comes true.
There
is still the possibility of the background
assumptions being wrong. Consequently,
theories
can neither be conclusively proven
nor conclusively
falsified by empirical data.
Also, it is possible to salvage a troubled
theory or make arguments against a
well-supported
theory. This can be done because one
can
alter auxiliary assumptions to produce
different
predictions or change the meaning of
theory-laden
observations. For example, suppose
I proposed
the theory that the moon is made of
cheese.
To refute this theory, many people
would
point out that astronauts have gone
up there
and found out that it is more like
a rock
than a huge piece of cheese. I could
counter
that argument by saying something like,
“the
moon with its great age would naturally
accumulate
massive quantities of rocks and other
particles
from space. Under that layer of space
debris,
however, is the cheese.” This type
of argument
that explains away such evidence is
called
an ad hoc hypothesis[32], especially
if the
theory-saving device lacks further
significant
evidence to support itself. Of course,
it
is possible to rationally discard this
absurd
theory, but the point is one cannot
do this
merely by pointing to the data. When
the
right ad hoc hypotheses are made, the
theory
of the moon being made of cheese becomes
empirically identical to the moon being
rock-like.
This sort of thing is not limited to
ridiculous
theories about the moon’s composition.
It’s
possible to modify virtually any theory
so
that it’s consistent with whatever
data that
might come up.
Despite the fact that Karl Popper was
not
completely successful, he did make
some useful
contributions. He pointed out that
data are
selective and subject to human choice
(and
thus demonstrated that data are not
quite
as objective as once thought). He also
showed
the flaws of inductivism and why a
theory
cannot originate exclusively from empirical
data.
So it does seem that, if the only way
to
evaluate theories is in terms of empirical
predictions, science is in trouble.
In testing
theories, scientists use auxiliary
assumptions
for which they have rational reason
for being
true, even though the assumptions and
theories
are not conclusively proven. Yet, given
the
underdetermination of theories, we
can’t
just pick a theory and justify it solely
by the data. We can’t even justify
a particular
theory as probable by the empirical
evidence
since there are an infinite number
of other
theories that can explain the exact
same
set of data. How can science function?
Shaping Principles
It is evident that theories and data
by themselves
are insufficient for science to work,
and
thus other factors are needed for science
to operate. This group of factors in
the
nature of science is that of shaping
principles,
which can be used to select theories
and
form the foundations of science. Many
assumptions
are made in science. One example is
the uniformity
of nature. That is, the belief that
natural
processes operate in a fairly consistent
manner. This shaping principle is the
basis
for the idea of natural laws. For example,
Newton’s laws are said to apply throughout
the universe.[33] This is believed
even though
scientists have not actually tested
the laws
everywhere in the universe. Natural
laws
could not exist in science without
assuming
the uniformity of nature. Other assumptions
made for science to operate include
that
there exists an external objective
reality,
that our senses are generally reliable,
and
so forth.
Another set of shaping principles evaluates
the empirical evidence to select theories.
Because of the underdetermination of
theories,
there is always an infinite number
of competing
theories that can accommodate any given
set
of empirical data. Since these competing
theories are empirically indistinguishable
from each other, if science is to pick
out
a theory from among these numerous
competitors
and claim that it is correct, then
such a
selection must be based on nonempirical
principles
(whether they be philosophical, personal,
societal, or whatever). The law of
parsimony
is one of them. This principle of logic
states
that, if all other aspects are equal,
the
simplest theory is preferred over other
theories
involving additional factors. This
is also
called Ockham’s razor (sometimes spelled
as Occam's razor). The law of parsimony
is
often used because a theory conforming
to
this principle fits the data more easily.
This principle especially applies to
theories
with ad hoc hypotheses. The lower the
number
of ad hoc hypotheses a scientific theory
has, the better. Other principles include
(but are not limited to) empirical
adequacy
(covering the pertinent data in some
suitable
way), self-consistency, fruitfulness
(giving
rise to other understandings and having
stimulated
pioneering investigations and advancements),
and explanatory power.[34] Another
key principle
is how well a theory ties in with other
scientific
theories and concepts that are rational
to
believe. It is only when these kinds
of shaping
principles interact with data can science
then provide rational support for a
theory
over its competitors.
However, there are a few exceptions
to the
idea that there is no conclusive proof
in
science. Logic is the closest we can
get
to rigorous proof and falsification.
For
example, suppose our friend Bob has
this
theory: hairless men have no hair.
By the
rules of logic, Bob’s theory must be
true.
Of course, Bob’s theory is a tautology
(needless
repetition of an idea, in this case
it’s
the repeated concept of hairless men),
and
tautologies are typically not very
helpful.
Nevertheless, there are some instances
where
logic can give conclusive proof that
has
more utility. Because of the logical
precept
ex nihilo nihil fit (Latin for “from
nothing,
nothing is produced”), it is impossible
for
nothingness to cause something. Thus,
anything
that comes into being must have a cause
for
its existence. This fact is very useful
because
it supports a fundamental law of science
called the law of cause-and-effect
(the theory
that no effect can be produced without
something
to cause it). Sadly, not very many
helpful
theories can be thoroughly proved by
logic.
Similarly, logic disproving a scientific
theory is almost never used because
seldom
does a scientist propose a theory that
is
logically impossible. Most of the time
science
relies on other shaping principles
to pick
theories.
It becomes easier to understand these
principles
when they are put into action. In the
“moon
is made of cheese” example, we can
reject
it because of the law of parsimony.
It uses
an ad hoc hypothesis, whereas the theory
of the moon being like a rock does
not. Often
times, of course, more than one shaping
principle
becomes applicable. For example, suppose
Bob’s computer is malfunctioning. One
theory
he has is that an invisible gremlin
has caused
such problems, and another is that
a computer
virus has invaded his machine through
his
modem, computer programming, and some
fairly
complex electronic systems in his computer
as well as on the Internet. The gremlin
theory
is simpler, and thus it would seem
to appeal
to the law of parsimony. Yet the gremlin
theory hardly seems empirically adequate
in this case. This is because other
considerations
need to be taken into account. Another
fact
to consider here is that the computer
virus
theory ties in with electronic concepts
that
are supported by evidence, whereas
the gremlin
theory does not. Because so many shaping
principles are used and because they
can
often conflict with each other, we
should
be careful about justifying how much
the
evidence supports a theory. [35]
Unfortunately, there are still limitations
involved in scientific practice and
shaping
principles do not solve the entire
problem,
even in the basic foundational beliefs
of
science. Take the uniformity of nature,
for
example. We believe nature is consistent
enough so that the experimental data
(such
as from testing physical laws) obtained
from
two years ago on Earth will essentially
be
the same if the experiments were to
be conducted
in identical conditions on Mars next
week.
But there really is no logical principle
to tell us that physical laws will
hold true
in places where we haven’t tested them
(even
if that place is the future).[36] A
similar
sort of problem arises when we choose
between
empirically identical theories. When
using
shaping principles to select a theory,
we
must have some philosophical basis
for believing
that nature’s preferences are similar
to
ours. And for many of these principles
there
is no logical rule to imply their reliability.
For example, in picking out a theory
from
among it’s empirically indistinguishable
competitors (and when all other factors
are
held constant), the notion that reality
favors
simple theories over complex ones is
nevertheless
a philosophical principle. Although
these
indicators of theoretical truth are
necessary
for science to work, they are significantly
indirect, circumstantial, highly fallible,
and are still unable to prove/disprove
theories.[37]
While science may be the best we can
do,
the limitations should still be recognized.
On top of that, there is no known clear-cut
method that tells us to what degree
the evidence
confirms a scientific theory, despite
attempts
at finding one.[38] This becomes problematic
when scientists must decide on what
theory
to accept as the most rational one.
Scientists
intuitively feel how rational scientific
theories are, rather than having a
precise
logical method for such judgments.
These
intuitive feelings result from shaping
principles.
The interactions of shaping principles
in
the minds of scientists are so complex
and
so numerous that we may never come
up with
a rigorously logical system to select
theories.
Most of the shaping principles are
frequently
unspoken and sometimes scientists themselves
do not know they are using them. Although
some shaping principles are based on
logic,
others are not always so sensible and
objective.
Scientists (and regular human beings)
are
also affected by cultural, social,
and personal
beliefs.[39] Indeed, such factors have
been
significant influences in scientific
revolutions.[40]
This is because many activities in
science,
such constructing theories, involve
numerous
aspects of oneself. In the case of
making
theories, the theories themselves are
creative
inventions that come from the minds
of scientists.
Science is a human activity, and what
affects
scientists will have an effect on science.[41]
One may think that having such unscientific
factors affect theory judgments is
bad for
science. That may very well be true,
but
unfortunately there is no known way
to separate
the helpful principles (explanatory
power
etc.) from the unfavorable ones (personal
biases etc.) in the subconscious minds
of
scientists that make these theory judgments.
Because every human being has their
own unique
set of shaping principles, different
scientists
(and regular human beings) can look
at the
exact same set of data and disagree
about
which theory most rationally explains
the
observations. Rather than the traditional
view that science is to be protected
from
biases and other imperfections of people,
it turns out that science is inescapably
infected with humanness.
Tapestry
It would seem that there is a delicate
tapestry
in interpreting the data. It is uncommon
for a theory to be tested in isolation
because
of the Duhem-Quine problem. Because
we often
rely on background assumptions to derive
predictions for a theory, and because
those
background theories depend on other
auxiliary
theories and principles for their empirical
expectations etc., it would seem to
follow
that the collection of theories combined
with their shaping and background principles
thus make up an explanatory matrix,
or conceptual
grid, in which to fit the data.[42]
Modifications
to the explanatory matrix can be made
in
attempts to get a better fit, but because
of the interwoven nature of the tapestry,
often times one cannot supplant aspects
of
the grid without changing things in
some
way elsewhere. So it’s possible that
the
need would arise for an entire conceptual
system to be replaced. Additionally,
the
nature of science can make it difficult,
if not impossible, to empirically test
an
individual theory completely independent
of this matrix. However, it is also
quite
possible for nature to teach us some
things
in carrying out our investigation.
That is,
the interaction between the explanatory
matrix
and the data can be a sort of two-way
process.
As we uncover more data, we can learn
better
ways to shape the grid and how to go
about
it.[43]
Limitations of Science as a Result
of Scientists
Some have pictured the scientist as
a completely
objective individual who is free of
bias
and preconceptions, and who is willing
to
quickly abandon even the most well
accepted
theory if it were shown to be scientifically
inadequate. This belief is not close
to the
truth.[44] The reality is that scientists
are humans, and humans are fallible
beings.
They have weaknesses just like the
rest of
us. For one thing, a bias towards favored
theories is actually built into all
scientific
research.[45] (Recall the necessity
of background
assumptions to make predictions and
test
theories.)
A related imperfection, and to many
a startling
one, is a shaping principle called
tenacity
(also referred to as belief-perseverance
by psychologists).[46] Scientists throughout
history have shown a surprisingly severe
loyalty to their theories, even with
theories
that are in trouble with the evidence.
Furthermore,
this sort of tenacity persists in scientists
for rather long periods of time.[47]
Why
is this the case? The reasons become
clear
when one considers what scientists
do in
their field of work. When people put
enormous
amounts of effort into something over
great
lengths of time, as scientists often
do with
their theories, they have a tendency
to become
attached to it. Scientists in such
cases
have an inclination to want the theory
to
be true and it becomes psychologically
more
difficult for them to reject it as
false,
even if they are presented with strong
evidence
against the theory. The satisfaction
of destroying
a theory one has arduously worked for
can
be small compared to watching the theory
become successful. Furthermore, the
reluctance
to give up long-held beliefs is part
of human
nature, and scientists are not immune
to
it.[48] Not many of us would renounce
the
idea that two plus two equals four
even if
we were presented with a mathematical
proof
disproving that idea.[49] Consequently,
a
scientist whose career and livelihood
are
invested in a scientific theory will
probably
not give it up effortlessly. Needless
to
say, not everyone has been aware of
this,
including scientists.[50] How is it
then
that new theories emerge in science?
Nobel
prize winning physicist Max Planck
has said,
“A new scientific truth does not triumph
by convincing its opponents and making
them
see the light, but rather because its
opponents
eventually die, and a new generation
grows
up that is familiar with it.”[51]
However, tenacity is not necessarily
a bad
thing.[52] Ironically, belief-perseverance
is one of the reasons science has advanced
as far as it has. This is because scientific
theories are not perfect, and the only
way
to make real progress with a theory
is to
be committed to it.[53] Virtually every
scientific
theory has some sort of problems with
the
scientific evidence; which are sometimes
explained away by ad hoc hypotheses,
at times
there is some waiting for the problems
to
be eventually solved, sometimes the
problems
are unnoticed, at times they are simply
ignored,
and from time to time a theory is kept
because
there is no better alternative. If
science
abandoned every theory that had contradictory
evidence, science would barely have
any theories
at all. Furthermore, if a theory’s
problems
are eventually solved, then we have
tenacity
to thank for preventing the premature
abandonment
of the theory. Besides that, consider
this
hypothetical case. Suppose a scientist
who
possesses no tenacity writes a paper
for
a scientific journal and points out
all the
ways a concept or experiment might
be flawed.
Such a paper is likely to be rejected.
Part
of the responsibility of a scientist
is to
provide the most favorable case for
his theories
and leaving the criticism of the theories
to other scientists.[54] Belief-perseverance
helps accomplish this and thus can
work well
when science deals with theories that
only
a few scientific workers really care
about.
When significant tenacity to an accepted
theory is only limited to a single
scientist
or a small group of scientists, the
theory
can easily be weeded out. So some amount
of tecnacity is reasonable, and is
part of
what makes science function. Nevertheless,
tenacity can become a major problem
when
the majority of scientists fervently
accept
a scientific theory that does not have
enough
rational support. Naturally, there
is an
extent where the amount of tenacity
becomes
excessive and it’s time to abandon
the theory
in favor of a different theory that
has more
evidence behind it. Unfortunately,
there
is no clear-cut agreeable procedure
to decide
when such scientific concepts should
be discarded.
Feelings and other shaping principles
play
a part in deciding when that time should
come, and scientists can sometimes
disagree
reasonably on that issue.
Another imperfection is that of observation.
Because scientists are human, we cannot
obtain
completely objective observations even
if
there could be total theoretical neutrality.
One time it was believed (because of
direct
observation) by Thomas Huxley that
he discovered
a being halfway between a living organism
and a dead one. Many other scientists
made
observations that came to support that
view.
Later, however, it was discovered to
be purely
mineral.[55] Over a hundred independent
observations
corroborated Rene Blondlot’s concept
of N-rays,
but later it was discovered that there
were
no such things as N-rays.[56] These
are,
of course, extreme cases, but it does
demonstrate
that data are not totally uncontaminated
by humans. In practice, data are somewhat
subjective. This is because shaping
principles
influence the data we perceive, and
also
because of the tendency for the mind
to unconsciously
fill in patterns based on these notions.
Such human contamination is called
internal
theoretical orientation of data.[57]
As a
result, totally objective data cannot
be
obtained.
Besides honest confusion of data, there
is
also deliberate distortion. Often times
the
scientist who commits the fraud thinks
he
knows the answer.[58] Some people may
have
justified faking the data by thinking
they
were just speeding up the process.
Some examples
include that of Cyril Burt; a psychologist
who forged data on identical twins
to support
the idea that intelligence was inherited.[59]
It is possible this was done because
finding
thirty-three identical twins who were
separated
at birth would be a bit tricky. A more
famous
case would be that of Piltdown man,
an alleged
missing link in human evolution. This
is
also an example of internal theoretical
orientation
of data, because the fraud was an obvious
one[60] and yet persisted for over
forty
years. Of course, these things do not
happen
all the time, but it should be noted
that
scientists are not perfectly moral
beings
either, and sometimes this can have
a debilitating
effect on science.
Religion, Philosophy and Science
The notion that religion and science
have
constantly been at war is not without
foundation.
It is true that there have been some
religious
people who have disagreed with the
scientific
community (e. g. Biblical creationists).
It is also true that many religious
people
once held views contrary to what is
now accepted
(such as the Catholic Church accepting
geocentricism).
However, these sorts of events should
not
be overgeneralized. While many attempts
have
been made to show that religion is
unhealthy
for science (particularly in the 19th
century),
contemporary historians see that work
as
more propaganda than legitimate history.[61]
Even so, some believe that religion
and science
are utterly incompatible.[62] Actually,
that
view is relatively recent. It dates
back
not to Galileo, but to the liberal
theologians
of the Enlightenment.[63] (Incidentally,
Galileo was not actually branded a
heretic,
the sentence he received was for disobeying
orders.)[64] Not every educated person
believes
that science is against religion.[65]
There
are a growing number of people who
believe
otherwise, and that have rational support
for the idea that theology and science
cannot
be totally separated.[66] Many scientists
(including Newton, Faraday, and even
Galileo)
have been deeply religious.[67] To
add to
that, some scientists have actually
implanted
their religion into their scientific
work,
including Newton, Boyle, Maxwell, Pasteur,
and others.[68] Clearly, religion and
science
are not always bitter enemies.
Also, the evidence suggests that religion
(and more specifically the theistic
philosophy
that stemmed from the Christian worldview)
was a significant factor in the birth
of
modern science, at least partly because
it
provided some unique philosophical
principles
that science requires.[69] Why, for
instance,
would a rational investigation of nature
be successful? Because a rationally
orderly
God created the universe. (Nature consistently
operating in mathematical patterns
would
especially be confirmative for this
belief.)
According to the Christian religion
of that
time and area, the universe is orderly,
this
orderly world can be known, and there
is
a motive to discover this order.[70]
Indeed,
many of the founders of modern science
were
Christians trying to demonstrate that
humanity
lived in an orderly universe.[71] Why
should
the investigation of nature be empirical?
Because God could have created an orderly
universe in more than one way.[72]
This sort
of mindset is rather different from
classical
atheism (which was even accepted in
the 16th
century), which holds to the metaphysical
view of a universe dominated by chance
events.
This philosophy hardly implied an orderly
universe.[73]
Conclusion
So what exactly is the scientific method?
Although scientists certainly do something
in their field of work, there really
is no
such thing as the scientific method.[74]
This is true for a number of reasons.
First,
the majority opinion in the scientific
community
is often wrong.[75] Someone not going
along
with what the majority does can produce
something
scientifically useful, and this has
been
done many times. Second, science has
many
specialized fields, and scientists
in those
fields require certain craft skills
unique
in that field to conduct experiments.[76]
Such experiments do not involve precise
rules
that give detailed instructions on
what to
do at each step.[77] What may appear
to be
misconduct to an outsider may actually
be
quite valid scientific practice in
that field.[78]
Furthermore, rapid progress in science
will
be more likely if scientists do not
follow
a single standardized method. [79]
Individual
scientists have numerous ways of making
theories
and evaluating them, which explains
why there
can be disagreements among scientists.
The
different shaping principles that interact
with data can produce different results
with
each scientific worker, including on
how
scientists should approach things.
Sometimes
these disconformities help to produce
useful
scientific revolutions. At times revolutions
in science happen in large part because
these
kinds of shaping principles that are
accepted
by the majority change over time. Great
changes
in shaping principles create another
reason
why there has never been a single scientific
method used by all scientists.[80]
Although
there are some general objectives to
achieve
in science (e. g. finding scientific
theories
that are rationally supported), there
are
a number of ways to go about this,
and not
every scientist shares the same method.
It does seem that science contains
various
imperfections and some serious limitations
on certainty. Many have pointed out
the existence
of technology as a sign that we are
on the
right track. But just because technology
works doesn’t necessarily mean that
our theories
of why it works are correct.[81] Often,
the
reliability of technology depends more
upon
empirical regularities, rather than
explanatory
concepts. For example, candles and
light
bulbs have worked and will continue
to work
even though our theories of why they
work
have changed over time (light as particles,
waves, or some combination of the two;
the
rejection of the phlogiston theory
of heat,
etc.). The underdetermination of theories
applies to explaining the effectiveness
of
technology just like any other data.
Some
have believed that science has been
successful
in acquiring knowledge, yet there really
is no way of verifying this. Data are
incapable
of conclusively proving theories, and
we
can’t exactly read an omniscient “book
of
truth” to see how often our theories
have
been correct. Historically speaking,
almost
every theory in science eventually
becomes
discarded as wrong.[82] Consequently,
there
have been so many false starts in science
that it would be rather incredible
if we
were the ones who are finally on the
right
track.[83] It would be especially amazing
considering that the theories that
we’ve
already discarded have not even been
conclusively
falsified by the data. Even so, this
is not
to say science isn’t worth having around.
On the contrary, science provides significant
benefits for humanity. For one thing,
science
has helped us to alleviate the struggle
to
survive.[84] Whether or not we are
on the
right track, it seems clear that science
is conducive for useful technology.
Various
aspects of science can be used for
the needs
of people, understanding ourselves
and even
our place in the universe.[85] Although
there
is a very real possibility of being
wrong,
we can increase our chances of being
right
through further accumulation of data.
Despite
all its imperfections and limitations,
science
may very well be the best tool we have
for
discovering nature.
NOTES:
[1] Ratzsch, Del The Battle of Beginnings:
Why Neither Side is Winning the Creation-
Evolution Debate talks about such non-empirical
factors on non-empirical factors on
pp. 124-127.
[2] Tyson, Neil D. “The structure of
science.”
[3] “science, philosophy of.” [4] Ratzsch,
Del The Battle of Beginnings: Why Neither
Side is Winning the Creation- Evolution
Debate.
p. 121. [5] Schick, Theodore “The end
of
science?” [6] Ratzsch, Del The Battle
of
Beginnings: Why Neither Side is Winning
the
Creation- Evolution Debate. p. 105.;
Ratzsch,
Del The Philosophy of Science pp. 14-20—see
this source for further discussion.
[7] Ratzsch,
Del The Battle of Beginnings: Why Neither
Side is Winning the Creation- Evolution
Debate.
p. 104. [8] Goodstein “Conduct, Misconduct,
and the Structure of Science.”
[9] Ratzsch, Del The Battle of Beginnings:
Why Neither Side is Winning the Creation-
Evolution Debate. p. 111 [10] Ratzsch,
Del
The Philosophy of Science chapter 2
[11]
“hypothetico-deductive method.” [12]
Ratzsch,
Del The Battle of Beginnings: Why Neither
Side is Winning the Creation- Evolution
Debate
p. 110 [13] Ibid. p. 108; “hypothetico-deductive
method.” [14] I briefly talk about
why we
don't have any hard proof that memory,
testimony,
and sense experience of being reliable
at
http://www.angelfire.com/mn2/tisthammerw/rlgn&phil/skepticism.html.
[15] Schick, Theodore “The end of science?”
[16] Ibid.; Rosen, Kenneth H. Discrete
Mathematics
and its applications p. 172 [16] Ibid.;
Glasner,
David “Karl Popper, critical rationalist.
(modern philosopher).” [17] Wolpert,
Lewis
“Science: The art of the insoluble?”
[18]
“Karl Popper.” [19] Glasner, David
“Karl
Popper, critical rationalist.(modern
philosopher)”
[20] Popper, Karl Conjectures and Refutations:
The Growth of Scientific Knowledge
p. 46
[21] Schick, Theodore “The end of science?”
[22] Woodward, James and David Goodstein
“Conduct, Misconduct, and the Structure
of
Science.” [23] Ibid.; Glasner, David
“Karl
Popper, critical rationalist.(modern
philosopher)”
[24] Schick, Theodore “The end of science?”
[25] Woodward, James and David Goodstein
“Conduct, Misconduct, and the Structure
of
Science.” [26] Wolpert, Lewis “Science,
the
art of the insoluble?”; Wolpert, Lewis
“Hypotheses”
[27] Woodward, James and David Goodstein
“Conduct, Misconduct, and the Structure
of
Science.” [28] Schick, Theodore “The
end
of science?” [29] Woodward, James and
David
Goodstein “Conduct, Misconduct, and
the Structure
of Science.” [30] Woodward, James and
David
Goodstein “Conduct, Misconduct, and
the Structure
of Science.” [31] Ibid. [32] Carroll,
Robert
Todd “Skeptic’s Dictionary: ad hoc
hypothesis.”
[33] Trinklein, Frederick E. “Modern
Physics.”
p. 56 [34] Ratzsch, Del The Battle
of Beginnings:
Why Neither Side is Winning the Creation-
Evolution Debate. pp. 131,132 [35]
Carroll,
Robert Todd “Skeptic’s Dictionary:
Occam’s
razor.” — Regarding an apparently irrational
theory conforming to simplicity, he
merely
dismisses the theory as “simpleminded”
without
giving further reasoning to why it
should
be rejected.
[36] Miller, David “Skepticism and
Relativism.”
[37] Ratzsch, Del The Battle of Beginnings:
Why Neither Side is Winning the Creation-
Evolution Debate. p. 191 [38] Ratzsch,
Del
The Battle of Beginnings: Why Neither
Side
is Winning the Creation- Evolution
Debate.
p. 110 [39] National Academy of Science
1996,
p. 201; Ratzsch, Del The Battle of
Beginnings:
Why Neither Side is Winning the Creation-
Evolution Debate. p. 126 [40] Harre,
Rom
Obituary: Professor Thomas S. Kuhn
[41] “Science”
The Columbia Encyclopedia [42] Ratzsch,
Del
The Battle of Beginnings: Why Neither
Side
is Winning the Creation- Evolution
Debate.
pp. 127-136 [43] Ibid. pp. 130 [44]
Goodstein
“Conduct, Misconduct, and the Structure
of
Science.”; Goodstein, David “What do
we mean
when we use the term ‘science fraud’?”
—
The author calls the mistaken belief
“the
Myth of the Noble Scientist.”
[45] Goodstein, David “What do we mean
when
we use the term ‘science fraud’?” [46]
Woodward,
James and David Goodstein “Conduct,
Misconduct,
and the Structure of Science.” [47]
Ratzsch,
Del The Battle of Beginnings: Why Neither
Side is Winning the Creation- Evolution
Debate.
pp. 116, 128-129, 174 [48] Woodward,
James
and David Goodstein “Conduct, Misconduct,
and the Structure of Science.” [49]
I know
I wouldn’t. [50] Schafersman, Steven
D. “An
Introduction to Science.” — Here the
author
is an example.
[51] Dembski, William “Disbelieving
Darwin—And
Feeling No Shame!”; Milton, Richard
“Alternative
Science.” [52] Woodward, James and
David
Goodstein “Conduct, Misconduct, and
the Structure
of Science.” — The authors suggest
that tenacity
may be necessary for scientific developments
to be successful.
[53] Dembski, William “Disbelieving
Darwin—And
Feeling No Shame!” [54] Woodward, James
and
David Goodstein “Conduct, Misconduct,
and
the Structure of Science.” [55] Shapin,
Steven
“History of Science and Its Sociological
Reconstructions.” p. 160; Gould, Stephen
Jay “Bathybius Meets Eozoon.” p. 18
[56]
Klotz, Irving “The N-Ray Affair.” p.
168ff
[57] Ratzsch, Del The Battle of Beginnings:
Why Neither Side is Winning the Creation-Evolution
Debate p. 124 [58] Goodstein, David
“What
Do We Mean When We Use the Term ‘Science
Fraud?’” [59] Ibid. [60] Gould, Stephen
Jay
“Smith Woodward’s Folly.” [61] Kathloff,
Mark “God and Creation: A Historical
Look
at Encounters Between Christianity
and Science.”
In Bauman pp. 5-30 [62] Davidson, Aaron
“Science
as a Belief System.” [63] Wertheim,
Margaret
“Science & Religion: Blurring the
Boundaries.”
[64] Gingerich, Owen “How Galileo Changed
the Rules of Science.” [65] Witham,
Larry
“Creation-evolution debate takes on
a less-shrill
tone.” — Niles Eldredge, a curator
at the
American Museum of Natural History
holds
this view.
[66] Wertheim, Margaret “Science &
Religion:
Blurring the Boundaries.” [67] Wolpert,
Lewis
“Hypotheses.” [68] Ratzsch, Del The
Battle
of Beginnings: Why Neither Side is
Winning
the Creation-Evolution Debate. p. 166
[69]
Helweg, Otto J. “SCIENTIFIC FACTS:
Compatible
with Christian Faith?” [70] Ibid. [71]
Ibid.
[72] Bede “Christianity and the rise
of modern
science” [73] Helweg, Otto J. “SCIENTIFIC
FACTS: Compatible with Christian Faith?”
[74] Woodward, James and David Goodstein
“Conduct, Misconduct, and the Structure
of
Science.”; Bridgman, Percy W. “On Scientific
Method” [75] Ibid. [76] American Association
for the Advancement of Science 1993,
p. 7;
Ibid. [77] Woodward, James and David
Goodstein
“Conduct, Misconduct, and the Structure
of
Science.” [78] Ibid. [79] Ibid. [80]
Ratzsch,
Del The Battle of Beginnings: Why Neither
Side is Winning the Creation-Evolution
Debate.
p. 127 [81] Wipond, Rob “The World
is Round
(and other mythologies of modern science).
(Exploring the Foundations of Humanism)”
[82] Ratzsch, Del “Recapitulations”;
Ratzsch,
Del The Battle of Beginnings: Why Neither
Side is Winning the Creation-Evolution
Debate.
p. 165 [83] Ratzsch, Del The Battle
of Beginnings:
Why Neither Side is Winning the Creation-Evolution
Debate. p. 132 [84] Reines, Frederick
“Who
Needs Science?” [85] Ibid.
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