To my regret, there is little recent literature cited,
because there is little written on statistics from a
philosophical and non-technical point of view. There also is an
almost complete absence of formulae, which I do not regret at
all; this absence is because simulation in general, and
resampling in particular, make formulae unnecessary.
The book's goal is large: To present the basic elements of
statistical inference without absurdities or inconsistencies, and
to resolve on-going controversies about fundamental conceptions.
The reader may well disagree with the basic approach, that of the
operational definition rather than a structure built on first
principles, and the reader may also feel that I skirt the key
issues rather than overcome them. But such disagreements are a
matter of taste, whereas it should be a matter of ascertainable
fact whether the book succeeds in making its main statements
clear and mutually consistent, within the book's chosen basic
approach.
OUT AND MAYBE USE LATER
The present volume presents the philosophical ideas at a high
level of abstraction for the consideration of professional
statisticians and philosophers; also unlike a text, it does not
contain a survey of resampling techniques. If anyone buys the
book and feels that s/he has not received full money's worth
because some of the material is not new to them, please write me
for a refund.
The writing style in this book is a simple one, born of a
desire to make reading as easy as possible. Simplicity has a
drawback, however; there is a psychological need for writing to
be somewhat difficult, which partly accounts for the effective-
ness of including formulaic mathematics in one's writing. (T. S.
Eliot wrote somewhere that a poem must be easy enough to under-
stand, but hard enough not to be understandable immediately.) I
hope that simplicity of style does not mislead the reader into
thinking that the ideas are not deep. Indeed, as I urge through-
out the book, the fundamental ideas in statistical inference are
as deep as any in the intellectual enterprise (excluding those
that are apparently deep only because they are fundamentally
nonsensical). And I hope that the reader recognizes the novelty
in the ideas presented here despite the simplicity of treatment.
I say this in part because I want credit for these ideas, but
even more so because I want the reader to take them seriously.
Only the fundamental and controversial issues in statistics
are covered here. Methods such as regression (whose essence is
not probabilistic) and path analysis are hardly mentioned here
(though see Chapter 00 on the role of regression. Readers
interested in how I treat these subjects may consult Simon and
Burstein (1985).
page 1 statphil sintro Chapter 1 4-1-6900
PART I: GENERAL PHILOSOPHY OF STATISTICAL INFERENCE
CHAPTER 0: INTRODUCTION
Statistical inference is a set of devices designed to help
us comprehend and deal with the world in which we live. This
book'S primary aim is to clarify and resolve the confusions at
the conceptual base of statistics. Its secondary aim is to
provide a theoretical rationale for the use of resampling (Monte
Carlo) methods in statistics, and to systematize its practice,
illustrate the power of the "new statistics" in clarifying the
theory and bettering the practice of statistical inference.
Statistical inference seems mysterious to the uninitiated.
And situations where knowledge is uncertain seem different in
nature from situations where knowledge is clearcut. But in fact
there is no clearcut distinction between situations of certainty
and uncertainty. Consider the analogy of a two-horse race. In
most cases our unaided visual faculties can easily determine the
winner; that is, we can usually determine that the probability a
given horse won is either zero or one. But some races are so
close that our eyes alone are not sufficient to determine which
horse won, and observers differ in their judgments; these are
situations in which the probability that a given horse won is
somewhere between zero and one. In such cases we resort to
photography or electronic determination to resolve the "photo
finish".
Similarly, we call upon statistical inference to help us
resolve those situations in science, business, medicine, sports,
and other knowledge-creating and knowledge-using arenas in which
the data are sufficiently unclear that our unaided intuition does
not enable us to agree which data constitute the "winner", or we
need to asses the reliability of our best estimate. These are the
situations of uncertainty wherein our naked faculties do not
suffice. These uncertain cases are only a small proportion of
all the situations we encounter, but they can be important. And
cases requiring statistical inference are becoming more prominent
with each decade, it would seem, if only because of the
increasing availability of quantititative evidence.
Viewing statistical inference in this way should reduce its
mystery, which should be welcome to the layperson. Such
demystification may not be considered desirable by some
technicians, however, because (they say) this viewpoint -
together with the availability of resampling methods - may lead
laypersons to take probabilistic statistics into their own hands
and hence to lead them into error as they try to do it all
themselves.
It is often said that statistics is applied probability
theory. That is about as true as that engineering is applied
calculus, financial and national accounting are applied
arithmetic, and art criticism and political science are applied
logic. Computing probabilities is the smallest part of statisti-
cal inference, and the least difficult part intellectually - even
though the development of the formulaic mathematics of probabili-
ty and statistics has been a wonderfully challenging and valuable
feat during the past four centuries. The analysis of the context
of a statistics problem, the proper posing of the question, and
the interpretation of the results of the probabilistic
calculations are much more difficult and fraught with error than
are the probability calculations alone. And unlike the
probabilistic calculations, the other aspects of statistical
inference cannot be reduced to logical tests of correctness.
Rather, inference requires judgment and art, though this is
difficult for some scholars to accept.
I am not a professional mathematician or academic
philosopher. Rather, I write as a person who has been doing
empirical statistical work for 35 years, mainly in the field of
economics and demography but also in psychology, sociology,
business, and library science. I also write as someone who has
since the late 1960s been working with, pondering, and teaching
the resampling (Monte Carlo) approach to calculating
probabilities in statistical inference (including the bootstrap)
as a handmaiden to empirical research (see Simon, 1969).
This book shares a basic outlook with my previous books
about thinking and decision-making, and it overlaps with them.
The others are: Basic Research Methods in Social Science (New
York: Random House, 1969, third edition, with Paul Burstein],
1985), which is about the scientific research process; Applied
Managerial Economics (Englewood Cliffs: Prentice-Hall, 1975), a
text on business and other decision-making; Resampling Statistics
(Boston: Wadsworth, 1993), a text in the resampling method; and
The Science and Art of Thinking Well, which encompasses a wide
variety of ways of using one's mind to the advantage of oneself
and of others. My Easy Resampling Answers to Fifty Challenging
Problems in Probability may also be of interest to readers of
this book.
This book is not a compendium of resampling methods. For a a
wide variety of illustrative methods, one may consult Simon
(1969; 1975/1993/1996), Noreen (1989), Manly (1991), and Efron
and Tibshirani (1993).
SHAKINESS AND CONTROVERSY AT THE FOUNDATIONS OF STATISTICS
There is much controversy about the fundamentals of statis-
tics. This book aims to show that most (perhaps even all) the
controversial issues can be settled by recognizing that all the
contending points of view are appropriate in some domains and for
certain problems, but not elsewhere. And using the philosophical
tool of the operational definition<1>, the apparent
incompatibilities of the various points of view about statistics
can be resolved, and they can be integrated into a consistent
theoretical framework.
As eminent Bayesian statistician Leonard Savage noted, the
bases of any science are often its least solid elements. "[T]he
foundations are the most controversial parts of many, if not all,
sciences." And this is especially true of inference. "As for
statistics...[its foundations are] as controversial a subject as
one could name." (1972, p. 1).
As an illustration of these controversies, one of the
greatest modern statisticians, Ronald Fisher, refers to the work
of two other great moderns as follows:
... It is to be feared, therefore, that the principles
of Neyman and Pearson's "Theory of Testing Hypotheses"
are liable to mislead those who follow them into much
wasted effort and disappointment (1973, p. 92).
In turn, another great probabilist has this to say about a
key part of Fisher's work:
I do not understand the many beautiful words used by
Fisher and his followers in support of the likelihood
theory. The main argument...does not mean anything to
me (von Mises, 1981, p. 158)
There is sharp disagreement between Bayesians and non-
Bayesians. For example, a Bayesian textbook writer has this to
say about non-Bayesian writing:
When I first learned a little statistics, I felt
confused, and others I spoke to confessed that they had
similar feelings. Not because the mathematics was
difficult -- most of that was a lot easier than pure
mathematics -- but because I found it difficult to
follow the logic by which inferences were arrived at
from data...the books I looked at were not answering
the questions that would naturally occur to a beginner,
and that instead they answered some rather recondite
questions which no-one was likely to want to ask...
But attempts such as those that Lehmann describes to
put everything on a firm foundation raised even more
questions. (Lee, 1989. p. vii)
An inter-disciplinary team (Gigerenzer et. al., 1989)
asserted:
The idea spread by the textbooks that "statistics" is
an uncontroversial, objective instrument for inductive
inference is, of course, an illusion. Statistical
theories diverge not simply in the third decimal place,
but in the very questions they ask. (p. 228)
Yet despite the fundamental controversies, most textbooks
discuss inference as if there is consensus on the subject within
the profession. Gigerenzer et. al. (1989) studied these texts in
depth and concluded:
... all of these unresolved controversial issues, con-
ceptual ambiguities, and personal insults have been
more or less completely suppressed from the
textbooks...
Although the debate [about significance testing]
continues among statisticians, it was silently resolved
in the "cookbooks" written in the 1940s to the 1960s,
largely by non-statisticians, to teach students in the
social sciences the "rules of statistics." Fisher's
theory of significance testing, which was historically
first, was merged with concepts from the Neyman-Pearson
theory and taught as "statistics" per se. We call this
compromise the "hybrid theory" of statistical
inference...(pp. 106-107)
<2>
But the standard pastiche that is routinely presented - that
which Gigerenzer et. al. call the "hybrid" theory - is simply an
illusion. This patchwork would be rejected by all the major
intellectual figures whose elements are mixed into it.
[I]t goes without saying that neither Fisher nor Neyman
and Pearson would have looked with favor on this off-
spring of their forced marriage (Gigerenzer et. al.,
pp. 196-7)
Because this book finds that the controversies in the field
arise mainly because different schools of thought focus upon
different sorts of problems, the book systematically employs the
various procedures and their logics for the particular circum-
stances for which they were originally designed and fit best.
But the book goes further. It shows, I believe, that the
different methods are all theoretically compatible with each
other. This is quite different from what Gigerenzer et. al. call
"ecumenism". That is, the presentation here is not combination
of differing intellectual cultures living in peace with each
other, but rather an intellectual synthesis.
Illusory compromise among basic points of view, with its
suppression of discussion of foundational ideas, has had grave
effects on understanding and practice.
As a consequence, scientific researchers in many fields
learned to apply statistical tests in a quasi-
mechanical way, without giving adequate attention to
what questions these numerical procedures really
answer. (Gigerenzer et. al., 1989, pp. 105, 106)
This book aspires to resolve the fundamental controversies
by dissolving the basic confusions. If one hews closely to an
operational-definition view of probability, the apparent
contradictions and the grounds of controversy disappear (except
with respect to the conventional concept of confidence
intervals). And with respect to each particular case (or type of
case) that we seek to address, one can lay down a reasonable and
perfectly understandable approach. The approach offered here may
not be "justifiable" in terms of one or another axiomatic
foundation, but it seeks to be pragmatically sound in the sense
that it will lead to more reasonable decisions than other ways of
looking at the matter. The main tools used in this integration
are a) the concept of the operational definition, which obviates
all arguments about the appropriate definition of "probability,"
and also goes a long way toward eliminating conflicts about the
proper place of the Bayesian, Fisherian, and Neyman-Pearson
points of view; and b) the technique of resampling (Monte Carlo
simulation) to remove difficulties connected with the estimation
of probabilities once the problem has been delineated. <3>
THE INHERENT DIFFICULTY OF STATISTICAL INFERENCE
A successful statistical inference is as difficult a feat of
the intellect as one commonly meets, in my judgment. This is not
because of mathematical difficulties. Rather, it is due to the
long chain of reasoning connecting the original question with a
sound conclusion; indeed, the mathematical operations involved in
estimating probabilities once the problem has been correctly
specified can be straightforward, especially when one estimates
with the experimental resampling method rather than with
formulaic sample-space methods.
Indeed, perhaps the greatest benefit of the resampling
approach is that it clears away the mathematical difficulties so
that the difficult philosophical and procedural issues can be
seen clearly, and hence may be tackled head on. Ironically,
however, this characteristic of reducing the computational
difficulties has also been a drawback of the resampling method;
by making the necessary mathematical operations so simple as to
be accessible to any clear-thinking layperson, resampling has
made the assistance of professional statisticians seem less
necessary, which naturally educes resistance by the
traditionalists in that profession.
To restate the point: The great challenge presented by the
ideas of statistics does not stem from the need for a large body
of prerequisite knowledge, or for mathematical sophistication and
inclination. The challenge stems, rather, from the inherent
difficulty of making sense of a complicated situation. Indeed, if
any particular situation is not hard to understand, statistical
inference is not needed. The difficulty lies in there being a
very long sequence of decisions that must be made correctly about
such matters as the nature of the correct hypothetical
population, the correct sampling procedure, and so on. This
essential difficulty will be seen more clearly in Chapters 00
where canonical procedures for confidence intervals and
hypothesis tests are set forth.
The book assumes that every statistical problem contains at
its core a problem in probability estimation. The simplest sort
of problem is to state the (absolute) probability that a given
model produces results like the set of data in the sample of
observations. A second sort of problem, which builds upon the
simplest problem, is to state the relative probabilities that two
or more models produce results like the observed sample. Once
the question has been correctly formulated in a technical form
such as this, the actual estimation of probabilities is quite
simple.
STATISTICS AND ACADEMIC PHILOSOPHY
There is a long philosophical tradition of studying how to
distinguish truth from falsity, both in logic and in scientific
hypotheses. Until this century discussion mostly was limited to
yes-no distinctions, rather than the sort of graded assessment of
truth and falsity that is more consistent with the notion of
probability. And it turned out that as soon as one moves toward
a more probabilistic treatment of truth and falsity, the subject
becomes much more complex, and much harder to treat in a
technical fashion.
The result has been that academic philosophy has come very
late to dealing with statistics; Kaplan (1964) is a shining
exception. <4> And as a working scientist who only later
addressed the philosophy of science, Michael Polanyi properly
viewed philosophy as the handmaiden of science. (See especially
his Personal Knowledge, 1962, a sensible and practical work.)
The present book is in especial accord with Polanyi's writings
that there is inevitably some non-objective aspect - what Polanyi
calls a personal element , and what I usually call judgment - to
all scientific statements.<5>Probability itself was largely left
to the mathematicians, and statistics to practicing scientists.
And when philosophers have made forays into the field, they have
usually focused on technical issues which have led them to
neglect the crucial non-technical issues.<6>
Introduction of probabilistic considerations greatly
complicates matters. Consider for example Popper's
falsificationist notion; one cannot prove a theory but one can
falsify it with a single counterexample. Yet in the practical
world of science or craftsmanship or enterprise, a single
counterexample experiment or datum almost never is sufficient to
kill a theory, because one can never be sure that the experimetn
or datum is completely reliable or relevant. On the other hand,
it is beyond doubt that the increasing accumulation of confirming
evidence increases the propensity of scientists and decision-
makers to rely upon a theory in subsequent actions. Everyone who
has participated in discussions of theory together with data must
recognize how it will be forever impossible to produce formal
criteria that can bring together different opinions on what is
reasonable to conclude on the matter. It is equally clear that
the formal procedures of statistics can help clarify such
discussions and often reduce and/or sharpen the differences
opinion.
The point is that if philosophers are to be helpful in the
study of knowledge-getting in a modern world, they will have to
temper their value for formal technical analyses, and enter into
the messy and less-than-objective give and take of everyday
scientific discourse.
It is an important fact that most of the useful
philosophical treatments of science in the Twentieth Century have
come from persons who began as working scientists and later
turned to philosophy either part-time or full time; this includes
such persons as Michael Polanyi (1962; 1969), John Ziman (1968),
and before them Einstein, Bohr, Bridgman, Plank, and Heisenberg,
to name a few of that extraordinary generation of physicists in
the first half of the century. Morris Raphael Cohen and F. S. C.
Northrop were exceptions; both treated probability and scientific
inference in a reasonable and practical fashion<7>[fn-s1]. And
Abraham Kaplan (1964) deserves favorable notice here, too.
NOVELTY, SIMPLICITY, AND CREDIT
Though it is difficult for any author to view his or her own
work with any objectivity, these are the main elements in the
book that I believe are new:
1. Though many philosophers and statisticians have
discussed continuity in connection with forecasting, the
systematic analysis of the role of continuity and sameness -
described in Chapter 2 as the key concept in inference - is a
novel treatment of this fundamental concept, I believe, .
2. The chapter on causality (first published in Simon, 1969
and 1970) seems to be the first explicit statement of the first
major advance in the subject since David Hume's seminal treat-
ment. The analysis presented here, based on an operational-
definition approach to the concept, follows pathbreaking
pragmatic decision-connected illustrations of this advance in
epidemiology (in connection with the 1964 Surgeon General's
Report on Smoking and Health), in the study of juvenile
delinquency (Hirschi and Selvin, 1964), and probably elsewhere,
but none seem to have tackled the concept of causality in its
broad generality. This analysis fits hand-in-glove with the
other ideas in Part II of the book.
3. The analysis of resampling as a device that does not
require the calculations of the size of the sample space and
parts of it, described in Chapters 00, seems not to have been
suggested before, to my knowledge.
4. The broad idea of using Monte Carlo simulation to handle
most everyday problems in statistics is the main novelty for
statistical practice that is offered in the book, and the most
radical. Dwass in 1957, and Chung and Fraser in 1958, had
published the stochastic version of Fisher's exact test, Barnard
in 1963 published a Monte Carlo test for runs, and in 1969 I
published what has come to be called the bootstrap. But in my
view the key innovation is the broader suggestion: considering
resampling as the tool of first resort to handle the estimation
of probabilities in everyday statistics problems. My early
publications of this idea (1969; 1976 with Atkinson and Shevokas)
did not succeed in convincing the profession. But the time for
these ideas to be adopted may finally to have come in the 1990s,
after the work of Efron and others has formalized the
mathematical ideas and hence made them acceptable and exciting to
mathematical statisticians.
5. An approach to assessing reliability is offered that has
much in common with the conventional concept of confidence
intervals, but that involves none of the obscurity in
interpretation of the conventional concept.
6. It is difficult to believe that the idea is novel, but I
have not yet come across in the literature the statement that at
the core of every statistics problem is a question about the
behavior of a probability model, and the likelihood that that
model will produce the observed sample. (The only exceptions are
[a] a Neyman-Pearson sort of setup where the question instead
asks about the relative likelihoods of two distributions
producing the observed sample, and [b] a Fisherian framework
where one asks about which distribution among many would be the
most likely to produce the observed sample.) All the rest of
statistics consists of framing the probabilistic question and
interpreting the results.
As noted earlier, this book has considerable overlap both
with my 1969 book on research methods (third edition, 1985, with
Paul Burstein), and with my 1975/1993 text on resampling
statistics (and even more so with the second edition to come,
which will draw heavily on the present volume). For this overlap
I make no apology; these volumes are different vehicles intended
to (among other jobs) present a common set of ideas in a variety
of contexts<8>[fn]. This book is part of a lifetime evolution of
thought on these ideas, and I hope that this practice therefore
seems appropriate.
IN CONCLUSION
The discipline of statistics has long been in a state of
controversy about its most fundamental ideas. This book aims to
integrate the fundamental ideas in such fashion as to reduce the
controversy.
I hope the book advances a bit the philosophy and practice
of getting knowledge by making it coherent in both senses -
making the argument hang together, and making it
understandable.
FINAL NOTE
The spirit of this book is exploration of data. There are
no conclusive answers now, nor can we expect there to be, even
though many of us desperately want such answers just as Bertram
Russell did:
I wanted certainty in the kind of way in which people
want religious faith. I thought that certainty is more
likely to be found in mathematics than elsewhere.
(Russell, 1963, p. 54)
But there are no firm first principles on which we can
confidently base our assumptions, our procedures, or our
conclusions. There is no ultimate intellectual justification for
scientific work or for the decisions we make. In the following
extraordinary passage David Hume summed up this inevitable
ambiguity in our view of nature and ourselves, which is in the
spirit of the duality of Nils Bohr's concept of quantum physics:
This sceptical doubt, both with respect to reason
and the senses, is a malady, which can never be radi-
cally cur'd, but must return upon us every moment,
however we may chace it away, and sometimes may seem
entirely free from it. 'Tis impossible upon any system
to defend either our understanding or senses; and we
but expose them farther when we endeavour to justify
them in that manner. As the sceptical doubt arises
naturally from a profound and intense reflection on
those subjects, it always encreases, the farther we
carry our reflections, whether in opposition or con-
formity to it. Carelessness and in-attention alone can
afford us any remedy. For this reason I rely entirely
upon them (1777/1949, pp. 254-5.);
So this book is about doing the best that we can, and I hope
that it helps us move in that direction. We cannot know
perfectly, as Hume tells us, and as Godel and Bohr and Heisenberg
and Hayek agree. We do not even know what we are able to know
and what we are not able to know. But we need not and should not
be dismayed by our inability to satisfy the longing for surety
and for unity. As a species we have already been spectacularly
successful, and we have in our hands the intellectual tools to
learn more and more and hence live better and better. Need we
long for more?
P. S. The reader may wish to begin with Chapter III-1 if
entirely unfamiliar with the resampling method.
page 1 statphil sintro Chapter 1 4-1-6900
ENDNOTES
**ENDNOTES**
<1>: Among statisticians, W. Edwards Deming has been
perhaps the most forceful advocate of operational definitions;
see his 1986 book, Chapter 9. For a general discussion of the
operational (or "working") definition in social science, see
Simon and Burstein (3rd edition, 1985). Chapter 00 [scausali]
contains further discussion of operational definitions.
As I see it, the operational definition is a way to close
the system so that one can begin to analyse it analogous to the
way that one closes a system by assumption when one wishes to
analyse it with respect to the conservation of energy. When it
is and is not appropriate to close a system is a decision that
requires scientific wisdom.
<2>: Gigerenzer et. al. expand on this thesis:
The creation of the hybrid can be understood on
three levels -- mathematical statisticians, textbook
writers, and experimenters. On the first level, there
was a tendency to resolve the controversial issues
separating the three major schools by distinguishing
between theory and application, and by saying that
practical-minded people need not be bothered by these
mainly theoretical issues (noted in Hogben, 1957). To
users of statistics, this seemed perfectly acceptable,
since often the same formulae were used and the same
numerical results obtained. The great differences in
conceptual interpretation were overlooked in the plug-
in-and-crank-through use of statistical rules.
But, on the second level, writers of textbooks for
education, psychology, sociology, and so on, commenced
peace negotiations and created a hybrid theory, to
which shelves and shelves in research libraries now pay
tribute. The hybrid theory combines concepts from the
Fisherian and the Neyman-Pearson framework. It is
presented anonymously as statistical method, while
unresolved controversial issues and alternative
approaches to scientific inference are completely
ignored. Key concepts from the Neyman-Pearson theory
such as power are introduced along with Fisher's
significance testing, without mentioning that both
parties viewed these ideas as irreconcilable. For
instance, checking (without random sampling) thirty
books on statistics for psychology, education, and
sociology that were readily available, we found that
the names of Neyman and E. S. Pearson were not even
mentioned in twenty-five of them, although some of
their ideas were presented. None even hinted at the
existence of controversy, much less spelled out the
issues in dispute. The crucial concepts were not
identified with their creators -- which is very unusual
in fields like psychology, where textbooks list
competing theories and the researchers who proposed
them for almost every phenomenon discussed. Statistics
is treated as abstract truth, the monolithic logic of
inductive inference. (pp. 106, 107)***)
As an apparently non-controversial body of
statistical knowledge, the hybrid theory has survived
all attacks since its inception in the 1940s. If only
for practical reasons, it has easily defeated ecumenism
(Box, 1986), in which one applies the different
approaches to the same data, acknowledging that the
different approaches are conceptually unlike. It has
survived attacks from proponents of the Neyman-Pearson
school, and the Bayesians (Edwards, Lindman, and
Savage, 1963), and Popperians (Meehl, 1978). Its
dominance permits the suppression of the hard
questions. What, if any, is the relation between
statistical significance and substantial importance
within the scientific discipline? To what aspects of
the scientific enterprise do the ideas of Fisher, and
of Neyman and Pearson, appeal, and how can these be
combined? Are the experimental designs developed by
statisticians in agriculture and biology really a good
model for all experimentation in the social sciences?
What is most remarkable is the confidence within
each social-science discipline that the standards of
scientific demonstration have now been objectively and
universally defined. In fact, the standardization of
statistical methods becomes much less complete if one
looks across disciplines. In econometrics, to take the
most striking contrast, experiment is comparatively
rare, and the standard statistical tool is regression
analysis. It has often been applied by economists with
a lack of imagination that matches the psychologists'
use of hypothesis testing (McCloskey, 1985). Graduate
students within the social and biological sciences have
routinely been taught to view their statistical tools
as canonical, given by logic and mathematics. The
methods of statistical inference could be seen by
practitioners uncomfortable with higher mathematics as
someone else's concern, the province of statistical
specialists. (pp. 109, 110)
<3>:This integration is likely to be unwelcome not just
because it it is a disagreement with all of the
separate schools of thought, but also because
contending schools of thought tend to like the
existence of controversy, and find its continuation
pleasant and profitable. That this is so was one of
the less pleasant discoveries of what is now a long
period of time doing scholarly work.
<4>: An outstanding example of malpractice among
philosophers is the much-respected Richard Rorty, who
managed to write an entire chapter on social science
research without referring to a single actual or even
hypothetical study (1982, Chapter 11). [see letters on
Rorty to Michael, perhaps others] Another example is
an entire small book entitled Philosophy of Social
Science by Richard Rudner, wherein the only social
scientists mentioned are sociologist Max Weber (who did
not do quantitative work), economist Joan Robinson (who
probably never touched a piece of data in her life and
whose entire economics is now seen as wrongheaded), and
three famous anthropologists; no research study or
procedure was analysed in any way, and there is much
talk of axiomatic systems and sentences such as "The
hounds hunted the vixens" (p. 16). I suppose it should
not be surprising that philosophers can write so much
nonsense about the research process when they confine
themselves to such examples as the existence of
unicorns, the surety of a human's mortality, and
whether all Martians are green. (Indeed, academic
philosophers do not aim at being useful, by their own
accounts; rather, their discipline is an art form, they
often say.)
<5>: A thoughtful research methods text may actually be
the best instruction in the philosophy of research, though
implicit rather than explicit. (When I first worked on my
own text, I felt as if I was engaged in a bootlegging
operation in epistemology.)
<6>: Consider by analogy the running of a restaurant and
the cooking of the food. The chef's job is quite
technical, and can be specified with recipes and other
procedures; this is analogous to the pre-probilistic
technical study of logic, and to the approach that
philosophers have brought to the study of statistics
generally. But the tasks of organizing a restaurant, such
as choosing a style of food and a decor, and tasks such as
attracting and pleasing a clientele, are much less
technical yet at least as important - and the talents and
skills are usually in shorter supply - than cooking the
food.
Under some conditions, the running of a food facility is a
technical matter; an army field kitchen is such an example
that can be specified in specific objective terms. But to
attempt to run civilian restaurants the same way must
fail; no army field kitchen can thrive under conditions of
consumer choice. Similarly, the context of statistics -
choosing problems, designing studies, interpreting the
results - cannot be specified formally and handled
technically. And this the philosophers have left alone.
<7>:Cohen referred to Leibniz, Cournot, and Peirce as
"honorable exceptions to the practice of philosophers of
giving "scant attention" to the idea of probability (1956,
p. 113).
<8>:I doubt that anyone wants to know which passages have
been published before, and hence I will not distract the
reader by putting them in quotation marks or footnoting
them (though I do note the chapters that have previously
been published as articles).
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