Cruel to Be Kind: A Neopragmatist Approach to Teaching

Cruel to Be Kind: A Neopragmatist
Approach to Teaching Statistics for
Public Administration Students
David Oliver Kasdan
Incheon National University
ABSTRACT
Many public administration students harbor doubts about their ability to learn statistics. Adoption
of a tenet of neopragmatism can realign statistics with students’ cognitive interests and frame it as
a method to advance social progress away from cruelty. This approach is rooted in John Dewey’s
fusion of educational philosophy with scientific method and Richard Rorty’s postmodern upgrade
of classical pragmatism. Neopragmatism recognizes that there are linguistic and contextual
challenges to social science research, and that statistics is “translating” what happens around us into
a language based on the math logic that is actually common to many of our social phenomena. This
eases students’ arithmophobia so they can see the greater challenge as analyzing governance issues
to take advantage of the explanatory powers of statistics. Students then focus on figuring out the
words, rather than the numbers, that are necessary to improve administrative decisions and reduce
cruelty in the world.
KEYWORDS
Neopragmatism, arithmophobia, statistics, public-wellbeing
INTRODUCTION: STATISTICS IN THE PUBLIC
ADMINISTRATION CURRICULUM
The first day of a statistics class is pivotal. Public
administration students—some of whom may
have chosen their field as much to avoid certain
subject areas as to actively pursue an academic
interest—await complex equations with severe
trepidation. Imparting the relevance of statistics
to public administration and bracing students
for its technical dimensions is a challenge for
instructors (Smith & Martinez-Moyano, 2012).
Arithmophobia can block students’ cognitive
pathways, and a case of “quantitative paralysis”
may set in when stu­dents are confronted with
the first mathe­
ma­
tical problems they have
encountered in several years (Adenay & Carey,
JPAE 21 (3), 435–448
2011). Not every institution has a sufficient
math pro­ficiency re­quirement to ensure student
success in a statistics course, but certain public
admini­
stration curricula do have a statistics
course requirement that proves to be a persistent
obstacle to student success.
The teaching of statistics must contend with
contemporary contexts. The information age is
escorted by misinformation, with a widening
gap between comprehension and cognition
when it comes to processing social data (Silver,
2012; Tishkovskaya & Lancaster, 2012). The
“pure” statistics taught for mathematics and the
natural sciences does not suffer the many
variations in presentation seen in the social
Journal of Public Affairs Education435
D. O. Kasdan
sciences (Payne & Williams, 2011). Even
within one political science department, an
instructor with a public administration specialty
will concentrate on and frame certain concepts
differently than an instructor coming from the
comparative politics perspective. Although val­
ues of the mean and standard deviation are
calculated the same regardless of discipline,
sampling methods and p-values hold different
weight in disparate research fields (Gal, 2002).
An ideal curriculum would require a general
statistics course to be paired with a research
design class tailored to the students’ majors, but
degree loads and departmental requirements
do not allow for such extravagance. With a
requirement of more than 30 classes for the
bachelor’s degree, public administration stu­
dents have neither the leisure nor the inclination
to pursue statistics over multiple courses. The
minimal math skills necessary are already offputting to many students; asking them to en­
dure pure statistics for a term before seeing how
it actually applies to their chosen field would be
too much.
Pedagogical theory, framed by the conflict of
institutional themes of education as a consumer
product (e.g., online degree programs) versus
the traditional notion of the academic com­
munity (e.g., liberal arts colleges), has divided
learning approaches. Do students learn what
they are able to learn or what they are receptive
to learning? Are some students wired for the
abstract while others need practical examples?
The trend seems to be that successful teaching
depends on engaging students’ cognitive in­
terests, which includes those things students
think they will be most likely to use later
(Zieffler et al., 2008). This controversy is larger
than the discussion at hand, but it is worth
noting that these views loosely correspond to
the foundational logic behind statistics itself:
deduction and inference. Some students are
better at working from the population to the
sample, while other students naturally reason
from the sample to the population.
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Journal of Public Affairs Education
It is possible to conceive of the idea of an
epistemic community for statistics in public
administration education. That is, these stu­
dents’ shared context affects their understanding
of statistics (Adenay & Carey, 2011; Garfield,
1995; Tishkovskaya & Lancaster, 2012). That
context necessarily includes the means of
communication, the extent of application, and
the weight that a statistical study holds in the
field. For example, students expect to use sur­
veys and census data to inform decisions, as well
as to be educated consumers of public admini­
stration research that presents findings relevant
to their eventual practices (Gal, 2002). At the
street level, there are endemic misconceptions
about some statistical concepts (Zieffler et al.,
2008). For example, polls often portray the
margin of error as the known interval around a
mean value, rather than as the estimate’s samp­
ling error (and thus a poll favoring one candi­
date over another at 55% to 45% with a 5%
margin of error is thought to be a dead heat).
Making students in public administration aware
of their epistemic com­munity helps to stand­ard­
ize statistical opera­tions while still recognizing
that those opera­tions have an appropriate time
and place in research according to the disci­
pline’s objectives.
After several terms teaching statistics to under­
graduates in the public administration, political
science, international relations, and history maj­ors,
I adopted an approach that helps to alleviate
students’ fears by channeling their con­cerns into
a reframing of the utility of statistics. For those
who agree with Lindley’s (2000, p. 294) philo­
sophical position that “statistics is essentially
the study of uncertainty and that the statis­tic­
ian’s role is to assist workers in other fields…
who encounter uncertainty in their work,” then
there is reason to customize statistics for those
students who firmly believe themselves to be
“workers in other fields.” Indeed, public ad­
ministration turns to statistics to quell uncer­
tainty, and it is the instructor’s responsibility to
suggest when statistics can as­sist understanding
of problems in “other fields.”
A Neopragmatist Approach to Teaching Statistics
Neopragmatism is a philosophy that confronts
uncertainty in the determination of utility.
Util­ity is an indicator of social progress, defined
as the alleviation of cruelty (Elshtain, 2003;
Rorty, 1989, 1991, 1999; Shklar, 1984). From
this vantage, statistics becomes a way to calc­
ulate utility as the likelihood that a course of
action may alleviate conditions of cruelty—
argu­ably a foundational precept of public ad­
min­istration. Introducing neopragmatism into a
statistics course leads to homework and test
problems that focus on issues such as inequity
and the protection of democratic values. In my
experience, this approach, paired with a sub­
stantial emphasis on working realistic examples
in class to reinforce the pragmatist value of em­
pirical experience, has yielded gains in student
performance as well as better course evalua­tions from the students themselves. Stu­dents
appre­ciate the outcome-based perspective, which
rele­gates the mechanics of statistics to the realm
of available methodologies useful in some in­
stances (Garfield, 1995), rather than a deon­to­
logical necessity for understandi­ng phenomena.
This paper briefly outlines a philosophical
perspective on statistics in social science before
explaining the relevant aspects of neopragma­
tism in detailed terms. Next comes a description
of melding neopragmatism with statistics,
fol­lowed by a discussion of how this strategy
serves the teaching of statistics to public admin­
istra­tion students. Several statistical concepts
will be “neopragmatized” to illustrate the trans­
form­ation of a math problem into a governance
problem, and thus position the numerate calc­
ulations into mere operational processes that
assist in the answering of a greater concern. Giv­
en that students have ready access to calculators,
spreadsheet programs, and other means of work­
ing the formulae, the teaching objective can now
focus on how observations relate to cond­itions
of cruelty and how the analysis can serve public
administration to make the world a better place.
The objective of this paper is to introduce an
alternative approach to teaching statistics—an
approach that accounts for students’ interests
and competencies while adequately applying the
methodological lessons to the practical contexts
of public administration. This approach takes a
philosophical perspective by integrat­ing a core
idea of neopragmatism into the coursework.
That idea—the inverse relationship between
cruelty and social progress—has been tested in
the classroom with encouraging results, as
described at the end of the paper.
STATISTICS IN SOCIAL SCIENCE: A BRIEF
PHILOSOPHICAL CONSIDERATION
The goal of statistics is to explain as much of a
phenomenon as possible while also reducing
the level of error as much as possible. Good
statistics purport to increase the ratio of explan­
ation to error up to some level of con­fidence
that is necessarily shy of certainty. The error
term—always present, never under­
stood—
eliminates big “T” truth from our vocabulary,
but the concept somehow has remained in the
ontology of statistics despite its history of prag­
matic guesswork (Pearson, 1990). Statistical
conclusions rely on a plethora of qualifying
terms—likelihood, probability, uncertainty,
margin of error, and so on—that, when used
appropriately and consciously, can make the
application of statistics more acceptable to the
social sciences. Within the academy, Lindley
(2000) and commentators give ample credi­bi­
lity to the continuing philosophical debate over
statistics, while popular discourse takes issue
with its use and abuse as well (Silver, 2012).
Assigning a number to a phenomenon helps to
legitimize and deal with it, especially if the
phenomenon concerns the weirdness of human
behavior. At the beginning of the 20th century,
soon after “Student” codified the values of in­
ference for small samples of beer (Pearson, 1990),
social scientists adopted statistics as a new tool
for making societal advances. The prag­matists
took note as well, for the basis of utility in their
philosophy was the ability to apply lessons
learned from their experiences to future
practice. Their lessons were often of the trialJournal of Public Affairs Education437
D. O. Kasdan
and-error variety, and the expediency of stat­
istical inference for making broad assess­ments
of utility fit their mission of social pro­gress.
Of course, the social sciences ran up against
behaviorist and humanist schools when logical
positivism crept over from the hard sciences,
making demands of rationality that conflicted
with experience. The role of the counterfactual
in social sciences became especially profound,
as Popper demanded that falsification be a
criterion of the empirical method against the
inertia of conservatism (1959, p. 57). This is
seen in how the null hypothesis is framed: it is
the condition of the status quo. A scientific
revolution (Kuhn, 1996) in the social sciences
quietly shifted the idea of progress from an
orientation that held the promise of enlighten­
ment by accessing the big “T” truth toward the
much more realistic objective of absolving
researchers of any epistemological limitations
that only enlightened the discipline to human
errors. Kuhn (1996) proposed that development
is truly pushed from the dis­satisfaction with
current explanations. Thus, the counterfactual
is needed to highlight the place that the field is
developing away from; falsifiability ensures that
possibilities are not limited to past negative
experiences, but rather the potential for exper­
iences beyond those circumscribed by the con­
text of the hypothesis set are created. The goal
should not be to seek final answers so much as
new questions.
This lineage has left statistics in the social
sciences with an unstable sense of identity.
Abelson (1995) draws on his years of teaching
statistics for psychology students to outline
what can and cannot be done with its methods.
His prescription for using statistics concludes
with a Kuhnian prophecy: “Each new genera­
tion of research workers in the social sciences,
therefore, is exposed to a more sophisticated
scientific culture than the previous cohort”
(Abelson, 1995, p. 198). Those cohorts are
taking into account contemporary consider­
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Journal of Public Affairs Education
ations from critics of scientific claims, such as
the interpretivists’ warning that “methodsdriven research narrows the range of questions
that the social sciences can usefully entertain
and explore,” and call for “sensitivity to
contextually specific meanings” (Yanow &
Schwartz-Shea, 2006, p. 382).
NEOPRAGMATISM: THEORETICAL AND
OPERATIONAL DEFINITIONS
The story that leads to a neopragmatist ap­
proach goes back to the common history of
statistics and pragmatism at the end of the
19th century. While Karl Pearson, William
Sealy Gossett, and other early statisticians were
working out the formulae and tables for
consistent inference, William James, Charles
Peirce, and John Dewey were trying to figure
out a philosophy to synthesize experience, truth,
and utility. Dewey (1916) was adamant that
education be grounded in shared experience—
what might be called consensus-based empir­
icism—meaning that the classroom lesson had
to center on developing an epistemic com­mun­
ity to understand what is useful in our lives.
Dewey’s concept of education emphasizes prac­
tice as the object of empirical analysis, which
translates to lessons that show concepts through
real-world examples and a communal solidarity
as to those concepts’ usefulness when seen in
different contexts. Both statistics and classical
pragmatism appreciate the value of inquiry,
trial and error, and the need to qualify the
instrumentality of scientific methods in the
pursuit of knowledge.
Neopragmatism is based on regular old empir­
icism: experience and observation matter, but
how we use them necessitates a postmodern
update. Richard Rorty is the most prominent
of neopragmatism’s champions; his core idea is
to reject notions of big “T” truths that we can
someday access through advances in science
(Rorty, 1979). There are no ethereal foundations
to knowledge, just as there is no objective
vantage to contemplate them; we just have
A Neopragmatist Approach to Teaching Statistics
what we can talk about to get us along.
Determining the utility of any bit of knowledge
is an exercise in building consensus rather than
an advancement in our grasp of reality.
Neopragmatism shifts focus from experience to
language through the “linguistic turn” (Hilde­
brand, 2003) that makes the communication
of empirical data into a contested task (Swartz,
1997). The means of representing experience is
context specific; one person’s description of an
experience may differ widely from another’s,
even if they both witnessed the same event. For
Rorty (1991), there is no meaningful distinction
between the experience and the language
because the former is nothing without being
represented by the latter. Since the social
sciences often observe phenomena that must be
described in words rather than measured by an
interval scale, it is reasonable to hinge eval­ua­
tions of utility to reaching agreement on the
language of the phenomena.
Moving from the high philosophical theory to
social science proper, Rorty (1989, pp. 189–198)
builds off Shklar’s (1984) proposition that
cruelty is the worst thing that we can do, and
goes so far as to propose that there is a level of
cruelty that eventually, under the protections of
near universal solidarity, becomes a functional
(big “T”) Truth. Rorty’s argument is convincing
enough to admit that there is some credence to
the idea of a “final vocabulary” when it comes
to cruelty. For instance, although the Nazi
atrocities were widely decried, there were some
sadistic SS officers who did not consider their
acts to be cruel (e.g., killing a Jewish person was
cruel to that person, but was offset by the
benefit to Hitler’s regime and, for stout
believers, the eventual fortunes of the world).
Yet there is surely an unimaginably horrendous
level of cruelty that would force those very
officers to cry for mercy.
This possibility of a transcendent cruelty then
serves as the closest thing we have to an ob­
jective vantage from which we can gauge all
else. For practical social science considerations,
all human activity can then be fixed at some
relative distance from the “universally cruel”
cruelty. Thus cruelty is operationalized as the
antecedent of any proposition that, for general
purposes of utility, is constructed to test one
specific social context against another. The
reference point for the test is the “true” cruelty
that allows us to pass judgment, opening the
door for methods of inquiry (such as statistics)
that rely on relativism between disparate contexts.
Neopragmatism can become complicated quite
quickly, of course, as the intricacies of post­
modern, deconstructivist, antiessentialist lang­
u­
age games riddle the social sciences with
uncertainties. If neopragmatism and its notions
of cruelty and progress take issue with the hard
sciences’ claims to big “T” truth (Rorty, 1991),
then how does this theory fare in the wispier
study of human behaviors? One aspect to keep
in mind is that cruelty is not always manifest as
the übercruelty that even a Nazi fears. Cruelties
may be slight, but the point is that they may
extend eventually beyond local contexts and
grow their influence toward others. No one
wants to wait until another Holocaust is happe­
ning to address a cruelty. The key for neoprag­
matism is balancing limited experiences with a
kind of antifoundational sensitivity as empirical
background for action. This means that claims
of cruelty are neither absolute nor completely rel­
ative; there is no standard of cruelty for reference.
When it comes to the value of science, neo­
pragmatism’s antiessentialism implies that the
natural “hard” sciences are no more valid than
the social sciences; neither school has privileged
access to reality nor a better record of accuracy
when it comes to correspondence with truths
(Rorty, 1979). Baert (2005, p. 141) adds that
“from a neo-pragmatic angle, ontological asser­
tions can never suffice, as methodological op­
tions are at least partly dependent on what is to
be achieved.” The best that either school of
science has to offer is the ability to identify a
set of contextual practices that have helped us
Journal of Public Affairs Education439
D. O. Kasdan
lessen cruelty and advance social progress, as
enveloped in our experience as language. In
other words, empiricism and scientific meth­ods are communications about conditions of
cruelty, not the recording and processing of
perceptions with an instrument (Swartz, 1997).
Nonetheless, neopragmatism does value evi­
dence and methods, whether they are presented
by a doctor in a white lab coat or an academic
sporting tweed and paisley. A semblance of
reliability can be achieved with some nugget of
science that produces the same outcome as we
experience and talk about it, but reliability does
not equal validity. At best, reliability lends
support to notions of internal validity, but
repeating scientific experiments only shows the
ability to cope with a particular situation. The
knowledge achieved with reliability is that the
closed system and its proprietary logic work in
their present context. This serves to bolster
internal validity, but that validity has an
expiration date when contexts change to such a
degree that a paradigm shift is in order (Kuhn,
1996). The neopragmatist is interested in that
kind of knowledge insofar as it can be gener­
alized for the good right now.
Social scientists can take this opportunity to
feel good about their fields. The natural sciences
do help us get by in material ways, but all of
that is secondary to the grander human experi­
ment conducted in society, as Dewey (1916)
and Rorty (1999) see it. This is where external
validity is assessed: Does the result of an analy­
tical method contribute to human welfare? The
correspondence sought through scien­tific study
is grasping reality as it is experienced.
If there is doubt as to the preeminence of social
progress as the ultimate outcome of natural
sciences, then consider that the actual cement
that undergirds a classroom where social science
ideas are theorized is a product of chemistry.
Social science is enabled by natural science, but
knowing the elasticity projections for poured
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Journal of Public Affairs Education
con­crete does not directly solve problems of
equit­
able health policy or forge a service
agreement between neighboring governments.
To drive the point home, consider that the
studies of astrophysics are done in the broad
interests of advancing humankind, not for the
sake of another alphanumeric mark on a map
that is fully contained and explained within the
closed system of instrument-enhanced human
visual perception.
More often the social sciences are confronted
with violations of civil rights, which neo­prag­
matism views as cruelty by means of the margin­
al­ization of a group that does not buy into some
predominant idea of objective truth (Abellanosa,
2010, p. 102). This usually occurs when a seg­
ment is excluded from participating in consensus
building (demo­cracy). For ex­ample, the Jim
Crow laws of the South were based on protecting
a foundational ontology full of big “T” truths—
blacks were less than whites—that enabled
marg­inalization. Whites felt that giving equal
rights to blacks was cruel; it would upset the
social order and be a cruelty to the white man’s
natural superiority. In this case, social progress
was obviously the reduction of the greater cruelty
of violations against black peoples’ civil rights.
THE NEOPRAGMATIST APPROACH:
HOW IS STATISTICS CRUEL?
The next step is taking the neopragmatist
perspective to figure out how statistics might
actually inhibit social progress and thus itself be
a cruel practice. The broad-form cruelty of
statistics is the assumption that its conclusions
are logically sound indicators of the way things
really are. Neopragmatism exposes the fallacy
that truth is correspondent to reality if the logic
behind the truth claims is held to be objectively
indisputable. Since much of statistics works
from the math logic that exemplifies Cartesian
a priori knowledge and is the basis of so much
Western thought, the neopragmatist perspective
takes issue on behalf of any phenomena that is
not of this traditionalist mold.
A Neopragmatist Approach to Teaching Statistics
This is not to say that math logic is useless—
experience shows that it serves human purposes
in many ways—but the blind application of
math logic to all aspects of human lives is
troublesome, especially when used in the
context of the oddities of human behavior. The
logic that denies us the ability to conceive of a
triangle whose interior angles do not sum to
180 degrees, or demands that the product of an
odd number and an even number be an odd
number, has little to offer in the way of
understanding transgender political sensitivities1
or public safety policy compliance rates.
The following subsections divide perspectives
on the cruelty of statistics into internal and
external understandings. In this context, cruelty
is not meant to evoke images of torture or
distress, but rather the inhibiting of social
progress for students and of the objectives of
public administration. More generally, the idea
is that the neopragmatist approach can alleviate
some of the instances where the use of statistics
may in fact produce outcomes that conflict
with the intentions of the social sciences.
Internal: “Statistics Itself Is Cruel”
The internal understanding holds that statistics
may produce cruel outcomes, intentional or
not, by virtue of its epistemological nature.
This understanding includes the notion that
statistics is a cruel tool, insofar as it quantifies
and generalizes things that may not really be
countable or broadly applied. For example,
statistics may be used incorrectly to impart
influence, as told by the quip attributed to
Benjamin Disraeli: “There are three kinds of
lies: lies, damned lies, and statistics.” Forcing
an observation into a value-scheme in order to
make probability assertions can also be cruel.
Consider the measurement of pain—a wholly
subjective human experience—on a Likert scale,
where the ordinal values are grossly insuf­ficient
to reliably convey the myriad character­istics of
pain (Ariely, 2009, pp. xiii–xvii). This would be
statistics as the agent of cruelty, caus­ing some
suffering to the world because it is forcibly
employed in an inappropriate context.
At first blush, arithmophobic students may
identify with this understanding because they
think that having to learn the theory and meth­
ods of statistics is a cruel practice on the part of
their institution. They may condemn statistics
as abusive because it appears incommensurable
with their cognitive interests (Zieffler et al., 2008).
After all, statistics depends on rigid rules that the
social sciences eschew, in contradistinc­tion to
the laws of natural science that frame the episte­
mologies of physics, chemistry, and the like.
Furthermore, the statistics instructor may be
seen as cruel because she is forcing a system
into students’ lives that they expect never to use
again. Add in that the abuse of statistics is
rampant in social science—another quip that
students appreciate is attributed to Mark Twain:
“Statistics never lie, but liars use statistics”—
and conveying the qualified utility of statistical
methods becomes even more difficult.
The neopragmatist approach, as a creature of
postmodern deconstructionism, avers that a
binary view of the world is insufficient; the
languages we use are built on the logic of
dichotomies that do not do justice to our
experiences in the world. Statistical probability
is an obvious culprit of this offense. Yes, a coin
flip necessarily turns up heads or tails and an
event either happens or does not happen. The
Type I and Type II errors are also dichotomous
(to themselves), as is the dependent variable in
a logistic regression. But the outcomes of these
dichotomies are not the end of the story: Do we
have to accept a two-sided coin as the decision
maker? Can we imagine room for compromise
in the world?
Mind, most statistics taught in public admin­is­
tration curricula are parametric; the distribu­tions
are assumed to be normal, Gaussian, and have
a nice curve of probability. (Some spe­cial­ized
and higher-level statistics courses may intro­
duce Bayesian probability, but students are not
Journal of Public Affairs Education441
D. O. Kasdan
normally exposed to this.) Since this is not
always the case for a distribution of a population,
we are making an assumption about the world
that may not hold up. It is a convenient
assumption that can make for difficulties in the
future when the model is not commensurate
with observations.
A relevant example of the potentially cruel
outcomes from the use of statistics played out
in my past teaching context in Michigan. Many
students in the Detroit metro area come from
families with auto industry backgrounds and
are now looking at careers in government—these
are two job sectors that have traditionally offered
workers defined-benefit retirement plans. Both
sectors have seen their pension funds dwindle
far below their needs in recent years, however,
causing a popular backlash against definedbenefit plans because they are now unsustain­able.
These plans depend on actuarial projections
that have proven inadequate at accounting for
the errors extant with such applications. The
context of the retired American worker has
changed—due to longer life expectancies and a
sustained recessionary period—and the statistics
used by the pension managers are underspecified
because the needs are too dynamic for quanti­
tative analysis alone.
The cruelty here is that the experienced results
do not resemble the projections from the
numbers; the projected likelihoods have fallen
short, and the output-outcome discrepancies
will mean tangible reductions in my students’
parents’ retirement quality of life, and perhaps
even these students’ own ability to pay for their
college education.
A more theoretical but nonetheless poignant
instance of the cruelty in statistics may be
found in the hypothesis test. It is cruel to
students because it is counterintuitive; as
Abelson (1995, p. 9) states, “A null hypothesis
test is a ritualized exercise of devil’s advocacy.”
(Indeed, “rejecting the null hypothesis” is one
of the few times a double negative is institu­
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tionalized.) Consider this arrangement as akin
to the justice system, albeit with the twist that a
defendant is presumed guilty until proven
innocent. The status quo condition in the null
hypothesis is the default that would be more
naturally positioned as the lesser option for an
administrative application. It is a cruel evaluation
process, cruel because it puts the burden of proof
on imperfect forecasts of a decision’s chance for
success—thus it stifles social progress when the
null hypothesis is the condition of cruelty that
is known and suffered right now. Better the devil
that we know than the devil we do not, unless
accompanied by a qualified p-value! This boils
down to the conservative versus progressive
debate that frustrates the student who hopes to
improve society.
External: Statistics Determines
“What Is Cruel?”
The external understanding is more amenable
to classroom instruction: in this approach, stat­
istics can be used to assess cruelty by assign­ing
indicators of cruelty to phenomena and then
looking for patterns of predictability. If a
phenomenon can be plausibly observed by a
quantified measure of cruelty, then statistics is
the principal of cruelty because it serves to pro­
vide an understanding of the cruel pheno­men­
on. For example, public health studies often
include a variable for the infant mortality rate
into their calculus as a proxy for many types of
cruel conditions (or at the least, conditions that
do not foster social progress).
Although the previous section argued that the
hypothesis test is structurally cruel because it
puts the burden of proof on the wrong side of
social progress, that same fault works to the
neopragmatist’s advantage in determining what
is cruel. The hypothesis test—as a discursive
measure to gain consensus among those who
are trying to lessen cruelty—is a clear way to
make decisions in the face of uncertain contexts.
Neopragmatism holds that we should avoid
cru­
elty without dictating a more concrete
objec­tive to move toward. “Run away!” as the
A Neopragmatist Approach to Teaching Statistics
conclusion of a situational analysis is often a
useful recommendation.
The goal of lessening cruelty in the world pins
down human experiences of the past while
leaving the future open for assessment. If there
were five instances of beatings yesterday, then
one might hope for less than five today. There
is no replacement or exchange going on with this
hope, but rather the meager desire to have less
of something that is not desired. Mind, this ap­
proach avoids a dichotomous mind-set, which
neopragmatism discredits for forcing a rightor-wrong view of the world. The opposite of
five beatings is not five kisses, but entertaining
the alternative of four beatings or fewer is most
definitely a better outcome. The neopragmatist
perspective can only say what is preferred in
contrast to what has been experienced.
Neopragmatism has a grasp of the null that is as
firm as the consensus behind its recognition. That
is, all can agree that there were five unpleasant
beatings yesterday. An alternative to the null is
simply defined as being the “not null” con­di­
tion—less cruelty is not the opposite of cru­elty—
and thus opens up options for social pro­gress.
Quibbling over this difference may seem like so
many language games, yet that is exactly what
is experienced of the world once an actor tries
to do anything beyond just being in the world
(Rorty, 1979, 1999). Those who embark on
the study of phenomena that are contingent on
words for their measurement, as is usually the
case in the social sciences, are playing language
games that cannot be justifiably summarized by
a t-test for significance.
Other components of statistics may be neo­
prag­matized for public administration students.
For example, randomness is a concession to the
practical limits of the “language” of statistical
methods, to represent how weird and unpre­
dictable phenomena really are. Random is the
side door to the error term; incorporating ran­
dom­ness into statistics is implicit agreement
that the method does not access big “T” truth.
Similarly, a neopragmatist perspective recon­
ciles its distrust of the truth-reality corres­pon­
dence construct with statistical inference by
positioning the p-value as the possibility that
randomness overcame the methodology being
used, within the acceptable allowance for things
not being what they appear. Lindley (2000, p.
295) makes a special note that “the definition
of randomness is subjective; it depends on you.
What is random for one person may not be
random for another.” For example, when con­
sidering what most polling services would call a
random sample of respondents for a pol­itical
survey, students know that the selection pool
is inherently nonrandom. Ameri­cans have con­
flicted feelings about political privacy, and only
certain types—often those whom we con­ven­
tion­ally think of as outliers on the political spec­
trum—are wont to disclose their voting choices.
THE CLASSROOM PRACTICE
OF NEOPRAGMATISM
Fitzpatrick (2000) and Smith and MartinezMoyano (2012) suggested some neopragmatist
values in their prescriptions for effective ped­
agogy: using real examples, keeping the utility
of the practice in mind, and minding that the
interpretation of results is paramount. What
can be added, to fill out the neopragmatist
approach, is realizing that all of these things are
contestable and subject to democratic discourse.
That is not to say that my classroom is a forum
for entertaining alternative mathematics, but
rather that the answers to the questions do not
end with “fail to reject the null.” The mayor
does not care what the p-value is. The U.S.
Census, despite its name and intentions, does
not do an actual head count of every person in
the country. A bad purchasing decision affects
every citizen’s welfare, even for something as
inconsequential as a pressure gauge at a nuclear
reactor. These and myriad other factoids spur­
ring off the intersection of statistics and public
administration illustrate that the outputs of a
study need to be closely related to the outcomes
Journal of Public Affairs Education443
D. O. Kasdan
and then put into the context of the admini­
strative objectives.
My teaching of statistics for social science stu­
dents retains the formal trappings of the trad­­
itional approach, such as stating the alpha and
insisting on a clear diagram of the confi­dence
interval on a normal distribution curve. Yet
these must be supplemented with not only an
interpretation of the statistical answers, but also
an extension of the conclusion into its potential
effect on administrative practice and, ultimately,
social welfare. I continually remind students
that statistics will not reveal big “T” truths or
illuminate metaphysical certainties, but it can
demonstrate the utility of some things within
qualified circumstances.
My classroom experience teaching in the
“traditional” method—that is to say, without
explicitly introducing and emphasizing the
neopragmatist consideration of cruelty—felt dis­
connected. Students are always concerned with
their grades, but earlier, non-neopragmatist
iterations of the course found students to be
more fixated on their answers being correct as
determined by the answers on the key. Student
responses to interviews and their open-ended
comments on course evaluations expressed con­
cerns with their technical aptitude for statistics.
By contrast, in later sections of the class taught
with an explicit neopragmatist approach, some
students still struggled with the mechanics of
statistics, but there was a palpable difference in
that their frustrations were directed not so
much at the complexities of calculating the
standard error as at the fact that the difficulties
of statistics could lead to cruelties. These stu­
dents’ concern was for the appropriate applica­
tion and interpretation of statistics, a long-term
orientation that is much more satisfying than
just mastering the operations to pass a test.
This shift in understanding was illustrated by
the popularity of an assignment that I intro­
duced under the neopragmatist approach to the
class: to write a research proposal. I positioned
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Journal of Public Affairs Education
the assignment as a “grade-saver” insofar as it
was a sizable portion of the course grade that
called on the students to apply the theoretical
properties of statistics to an everyday problem
without concern for doing the actual number
crunching. In essence, I asked students to make
a coherent argument to analyze a current issue
of public administration using statistics, paying
special attention to how the quantitative
research design would fit the context with
explicit consideration of the social progress im­
plications of its potential conclusions. In other
words, they had to propose the right tool for
the job and give a thorough explanation of how
it could improve the world.
In the context of public administration, this
goes toward what Carl Friedrich (1940) called
“publicity,” meaning the bureaucrat’s respons­
ibility to provide clear justification for decisions
and action. A result of this assignment (as an
exercise in neopragmatism) was the interesting
variables and models that students proposed as
they sought ways to measure and reduce cruelty.
For instance, one student operationalized a mea­
sure of “destitution” to indicate how sus­cept­ible
immigrants would be to gang influences in
metropolitan Detroit. Another student outlined
a survey to capture the experienced effects of a
neighborhood stabilization program on longterm residents in the neighborhood; a far cry
from relying on median home values, tax assess­
ments, and the proportion of owner-occupied
properties to evaluate the equity of such public
policies on distressed urban areas.
Statistics is a technique that must be justified for
each use, regardless of whether the problem is a
relatively straightforward calculation for person­
nel allocation or a murky quandary over sewer
routing. An example of a classroom exercise
that I liked to use would be to ask students to
assess the effectiveness of police shifts (i.e., five
8-hour shifts or four 10-hour shifts). As this
kind of administrative action is happening in
almost every state, there are other studies and
data available. But the issue is loaded with
A Neopragmatist Approach to Teaching Statistics
context-specific ramifications for a government
as well as citizens that need to be considered
when trying to design a model to capture
dimensions of equity, social progress, and
potential cruelties. These considerations could
take form in police force absenteeism, increases
in harassment complaints, or even unexpected
wear on equipment (“Like coffeemakers!” one
student joked). All of these aspects speak to
broader concerns than the cost savings or other
administrative indicators that most often drive
such decisions. The neopragmatist approach
serves to ensure that these things are given due
consideration; the question of police shift
length is multifaceted and open to the debate
that is incumbent on public administration
to entertain.
Another example of this approach is a lesson,
always effective for students, about the
implications of using the median or the mean
to understand income inequality. News
coverage and policy action taken with “the
average income” in mind is fraught with
problems, as the outliers are either marginalized
(median) or overindulged (mean). To make the
discussion in class especially prescient, I use the
university’s published salary data for faculty
and staff to illustrate what it means when there
are labor negotiations over a cost-of-living
increase or a proposed increase in tuition.
Suffice it to say, students often gain awareness
of a previously unknown cruelty when they see
the salary difference between a professor and a
basketball coach. The neopragmatist angle to
such a lesson is that summary statistics and
inferences based on such can be horri­
bly misleading if not outright wrong. The
construction of a hypothesis around the issue,
such as, “Faculty salaries are fairly competitive
for university employees,” shows how statistics
can contribute to—but not conclude—issues
in the real world.
The most simple neopragmatist advice for
teaching statistics to public administration
students is to enforce the objective through an
appendage. That is to say, every question,
problem, and answer should include the phrase
“as it reduces cruelty in the world.” Before
signing off on a community needs assessment
survey, contextualize the questions as they
would inform a course to improve social pro­
gress. After analyzing the racial demo­graphics
of a city to determine the effectiveness of an
urban renewal campaign, ensure that the
rejection of the null hypothesis is accompanied
by the indication of confidence as well as a
meaningful statement of how that campaign
has affected the level of inequality for citizens.
Students begin to anticipate and appreciate this
predication as a sort of neopragmatist condi­
tioning that keeps statistics within the realm of
their academic and practical interests. In the
spirit of the philosophy’s antifoundationalist
attitude, the teaching approach is to provide
tools (i.e., the formulae of statistics) and then
open discourse to allow consensus by the
students as to the utility of those tools for a
variety of administrative contexts.
CONCLUSION…AS IT REDUCES
CRUELTY IN THE WORLD
The statistics teacher has long struggled to
apply lessons to students’ experiences (Smith &
Martinez-Moyano, 2012; Zieffler et al., 2008);
this struggle is exacerbated when the class is the
sole quantitative study course in the public
administration curriculum. In essence, there is
an extenuating obstacle in what public
administration students consider to be their
cognitive interests and their need to pass a
required course that, on the surface, might not
directly inform those interests. For many
students, numeracy ended with high school
algebra and home economics. For many more,
statistics is some sort of scientific witchcraft
that is better left to specialists. Part of the
challenge of teaching statistics is to engage the
students in the subject’s pertinence to many
endeavors, as well as to convey statistics’ access­
ibility to even the most arithmophobic learner.
As the neopragmatist approach demonstrates,
a method’s validity and utility is a shared
Journal of Public Affairs Education445
D. O. Kasdan
determination (Rorty, 1991); without a
democratic discourse about statistics, it will
remain a tool for a particular subset of intellects
who will command the definition of cruelty
within their own narrow interests. Neoprag­ma­
tism is an approach to research design and
justification in public administration, rather
than a full-fledged call for a scientific revolution
to re-create quantitative analysis in postmodern
terms. Whereas traditional science puts validity
first and foremost as its objective, a neoprag­ma­
tist approach promotes the alleviation of cruelty
as the ultimate goal and subjugates validity as
an ancillary, context-dependent, and discur­sive­ly
determined outcome. This positioning helps
students see the data of public policy problems
as a potentiality for informing useful outcomes.
Neopragmatism’s ironic epistemology—that we
are all in on the joke but still act as if our lang­
uage has something to do with the world when
all it really has to do with is communication
about our language—lightens the mood of
quant­itative analysis. If students treat statistics
as a language puzzle rather than a positivist chore
battered by a never-ending list of eponymous
tests, then they can more easily accept it in their
cognitive framework. Dewey (1916) makes a
concerted effort to discuss how “interest and
discipline” hinge on students’ connectedness to
the subject, which is the instructor’s respons­
ibility to ensure. If challenged with a classroom
of recalcitrant public administration students
and the equation for the correlation coefficient,
such an instructor would point out that “the
problem of instruction is thus that of finding
material which will engage a person in specific
activities having an aim or purpose of moment
or interest to him, and dealing with things not
as gymnastic appliances but as conditions for
the attainment of ends” (Dewey, 1916, p. 155).
Teaching statistics for social science students
can be a successful endeavor from Dewey’s
perspective by bridging the divide, unifying “an
independent mind on one side and an inde­p­en­dent world of objects and facts on the other”
(1912, p. 162).
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Journal of Public Affairs Education
For students, it is a far leap from deriving the
standard error of the sampling mean to know­
ing that one can get an “irregular” group of
respondents on any particular day that might
warp one’s explanation of a phenomenon. Stu­
dents sometimes feel as though statistics is
something wholly different from their disci­
pline, rather than accepting it as a tool within
their field’s pursuit of understanding. Abelson
notes, “For many students, statistics is an
island, separated from other aspects of the
research enterprise. Statistics is viewed as an un­
pleasant obligation, to be dismissed as rapid­ly
as possible so that they can get on with the rest
of their lives” (1995, p. xii). In other words,
he holds that teaching is the illumination of
how thinking and doing are connected,
foreshadowing our contemporary pedagogical
buzzword: engagement.
This paper offers an approach that extends the
findings of previous studies about the difficulties
of teaching statistics in both the general
(Adenay & Carey, 2011; Garfield, 1995; Payne
& Williams, 2011; Tishkovskaya & Lancaster,
2012; Zieffler et al., 2008) and specific
(Fitzpatrick, 2000; Smith & Martinez-Moyano,
2012) public administration contexts. The
findings from using a neopragmatist approach
are anecdotal: I tried it in three iterations of an
introductory statistics course at a single
institution. Yet there has been positive feedback,
if not a measurable difference in outcomes
(analyzing grades is inconclusive and
inappropriate). Responses to open-ended
questions on the course evaluations for sections
that emphasized a neopragmatist approach
were encouraging: students acknowledged that
framing statistics as a means to assess cruelty
was helpful to their understanding, as well as
recognized that the idea of statistics as just a
tool to make help make administrative decisions
was conducive to their success in the course.2
Fixing the objective as “less cruelty” leads to
utility and operational truths being assessed by
their assistance in getting humankind closer to
that goal. If quantitative empirical analysis can
advance social progress to a state of less cruelty,
then it is useful.
A Neopragmatist Approach to Teaching Statistics
acknowledgement
This research was supported by the Korean
National Research Fund through Incheon
National University.
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1 A real example of this drove the point home:
During my first term teaching the graduate course
in quantitative methods, I intended to use some
basic demographic data about the students in
the class to introduce the concept of a dummy
variable (0 = male, 1 = female). One of my students
was transgender.
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2 I taught three sections of the undergraduate in­
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taught in the “traditional” format. The “sample” was
from a single institution from 2012–2013 with 58
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would be inappropriate, given the issue of academ­
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ABOUT THE AUTHOR
is assistant professor of
public administration at Incheon National Uni­
versity in South Korea. His research interests
include neopragmatism, behavioral economics,
disaster management, and social justice.
David Oliver Kasdan
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