WORKING DRAFT
March 1, 1999
DO NOT QUOTE OR CIRCULATE
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Iddo Gal
University of Haifa, Israel
One
key declared goal of education programs at all levels is preparing learners to
become more informed citizens and workers who can effectively function in an
information-laden society. Towards that end, instruction in literacy and
mathematics is usually provided, in part because adults will need to interact
daily with quantitative situations, including many where quantitative
information is embedded in text. This paper focuses on one critical but often
neglected aspect of mathematics (numeracy) education, that of developing
statistical literacy.
The
term "statistical literacy" does not have an agreed-upon meaning. In
public discourse, when “literacy” is combined with any term referring to a
specific knowledge domain (e.g., “computer literacy”) it conjures up an image
of the minimal subset of "basic
skills" expected of all learners
or citizens, as opposed to a more advanced set of skills and knowledge that
only some people may achieve. In this sense, statistical literacy may be
understood by some to denote a minimal knowledge of basic statistical concepts,
tools, and procedures, possibly including some interpretive skills.
In
this chapter “statistical literacy” describe people's ability to interpret and
critically evaluate statistical information and data-based arguments appearing
in diverse media channels (e.g., newspaper articles, TV and radio news and
programs, publications of political groups, advertisements) and to their
ability to discuss their opinions regarding such statistical information. This
conception is related to a “minimal skills” conception, yet emphasizes
sense-making and communicative capacities, more than formal statistical
knowledge, assuming that most adults are consumers
rather than producers of statistical information.
Statistical literacy is
needed if adults are to be fully aware of trends and phenomena of social and
personal importance, such as regarding crime, population growth, spread of
diseases, industrial production, educational achievement, employment, and so
forth, or to enable informed participation in public debate or action regarding
national or community issues (Wallman, 1993). Similar needs arise in many
workplaces, given increasing demands for quality and employee self-management
(Carnevale, Gainer, & Meltzer, 1990; Packer, 1997).
Despite the importance of
statistical literacy and its role in adults’ general numeracy (Gal, 1997),
almost no in-depth discussions regarding statistical literacy and the
educational processes needed to develop it have been published. This chapter
aims to contribute to a needed dialogue among practitioners, educational planners,
and researchers in order to promote statistical literacy. The chapter is
organized in three parts. First, key contexts were statistical literacy is
needed and can be developed are described. Next, key components of statistical
literacy are outlined. Finally, some instructional dilemmas and implications
for teaching and research are discussed.
A
discussion of statistical literacy should first consider the contexts in which
such “literacy” may both develop and be called for. We start by discussing the
classroom or teaching context, since students should learn and know something
about statistical and probabilistic concepts and procedures as a prerequisite
for making sense of statistical messages.
Introducing statistical ideas
Various
sources exist that discuss goals and approaches to instruction in introductory
statistics at different levels of instruction (e.g., Friel, Russell &
Mokros, 1990; Moore, 1992; Gal & Garfield, 1997; Lajoie, 1998). Such
sources usually make quite similar recommendations, though the range of topics
to be covered and the sequencing of topics and activities may vary depending on
student, teacher, and context factors. An approach often suggested for almost
any level of instruction is that teachers spend some time on basic concepts and
procedures, and also take students on at least one complete cycle of a
statistical investigation, where students engage all these phases:
·
Formulate
a question or hypothesis of interest to the students
·
Plan
the study (e.g., overall approach, sampling, how to measure target variables)
·
Collect
data and organize it
·
Display,
explore, and analyze data
·
Interpret
findings (in light of the question)
·
Discuss
conclusions and implications
The expectation is that by
interweaving focused instruction with actual experiences in all phases of a
statistical inquiry, students will gradually understand key goals, decisions,
and dilemmas associated with designing a data-based study, become familiar with
issues involved in making sense of data, and realize that statistical findings
can be used to answer many questions and to support or reject hypotheses,
depending on the quality and credibility of available information.
To be sure, the chosen
learning process must also develop learners’ awareness of the “big ideas” that
underlie all statistical thinking, such as: there is variation in the world and
it has to be estimated in various ways by statistical methods; there is often a
need to study samples instead of populations and to infer from samples to
populations; errors are likely in measurement and inference, and there is a
need to estimate errors; there is a need to reduce large amounts of raw data by
noting trends and main features (see Moore, 1990; Gal & Garfield, 1997).
The specific nature of interpretive
skills will depend on the context(s) in which they are needed. Gal (1998)
distinguishes two such contexts:
Reporting contexts emerge when learners are
“data producers” and take part in all phases of a data-based study as described
above. They have to interpret their own data/results in order to prepare a
report, and discuss with others their findings, conclusions, implications, etc.
Listening (reading) contexts emerge
when learners are “data consumers” and are encountered either (a) in the
classroom, where learners have to listen to (or read) a report from other
learners, or (b) in or outside the classroom, where people have to make sense
of and possibly react to messages that contain statistical elements.
Listening contexts,
especially those outside the classroom, are our main concern here when we speak
about statistical literacy. In such contexts, people do not engage in generating any data or in making any computations.
Their familiarity with the data-generation process (e.g., study design,
sampling plan, questionnaires, etc), or with the procedures used to analyze the
data, depend on the details and clarity of the information given by the message
producer. Yet, they do have to comprehend the meaning of messages they are
presented with, and be both interested and able to critically examine the
reasonableness of such messages or claims, or reflect about different
implications of findings being reported.
To
illustrate some of the many ways in which statistical concepts and findings and
statistics-related issues appear in the media, consider the following excerpts.
These are taken from newspapers, which provide a prime example for a
listening/reading context where statistical content is embedded in text.
“…The
study found that women of average weight in the U.S. had a 50 per cent higher
chance of heart attack than did women weighing 15 per cent below average.”
(Watson, 1997, p. 109; from Hobart
Mercury, New Zealand, February 10, 1995).
“Judges
count out Census sampling:…at issue is far more than the accuracy of sampling
in the Census held every 10 years: Billions of dollars in federal funds are
allocated on the basis of how many people live in each state and city, and
shifts in population can lead to the redrawing of House districts. A boost in
the count of minorities would normally help Democrats.” (Philadelphia Inquirer, August 25,
1998).
“Poll
backs limits on drinking by teens: The survey of more than 7000 adults… which
has a margin of error of 2 percentage points, found that…More than half favored
restrictions on alcohol advertising…More than 60% would ban TV ads for beer and
wine.” (USA
Today, October 5, 1998)
“The
human race held this year many more sexual intercourses than last year; the
world average was 112 per person this year, compared to 109 last year. This,
according to a comprehensive survey initiated and funded, for the second year,
by Durex, a manufacturer of prophylactics. The survey was held in 14 countries
that according to experts represent all the world citizens…” (Yediot Aharonot, Israel, October 28,
1997).
The demands in listening
contexts may differ from those in reporting contexts with regard to several
related facets. (The reader is advised to apply points made below to the
excerpts above).
1. Literacy and background knowledge demands of the messages involved: In the classroom, adult students will most likely use language
that is quite simple, due to their still developing linguistic and statistical
skills. In the real world, listeners will have to make sense of message of
different degrees of complexity, created by journalists, officials, or others
with diverse (and possibly advanced) linguistic and numeracy skills. Space
limitations may make messages terse, choppy, or lack essential information.
2. Range of statistical topics involved: Messages in the media or other real-world contexts will cover a
much broader range of statistical or probabilistic concepts, findings, and
problems, compared to what learners usually bring up on their own in the
classroom. For example, the design of controlled experiments, sampling
techniques, correlations, trends over time, or risk assessment, are only some
of the subjects that beginning learners of statistics are not likely to choose as a classroom project.
3. Degree of familiarity with sources for variation and measurement error: In listening contexts people are less aware of flaws in doing
research or problems with collecting credible data when the given study is
about an unfamiliar topic.
4. Degree of need for critical evaluation of the source of a message: When an adult has to
critically think about the validity of a message presented by a source such as
a journalist, a politician, or an advertiser, there may be a greater need to
suspect the message producer is trying to advance his or her agenda, than in
classroom reporting.
Despite these differences,
in both reporting and listening context the actors (learners) will be involved
in the interpretation, creation, communication, or defense of opinions (for example, about the
implications of a graph, the adequacy of a certain sample, or the validity of
an argument). The development of students' ability to generate sensible and
justifiable opinions should thus become a target area for instruction (Gal,
1998).
3. Building blocks of
interpretive processes
What
knowledge bases and other enabling processes should be in place so that people
can come up with reasonable and if needed critical interpretations of messages
encountered in statistical tasks? It is argued here that adults’ ability to
effectively manage interpretive statistical tasks involve both a cognitive component (comprised of three
elements: statistical knowledge bases, literacy skills, and a list of critical
questions) as well as a dispositional
component. (Gal, 1997, introduces the idea of management of quantitative
situations). These four building blocks are described below separately, but are
interdependent in operation and development.
Statistical knowledge bases
A prerequisite
for comprehension and interpretation of statistical messages is that students
posses knowledge of basic statistical and probabilistic concepts and
procedures. Obviously, what constitutes “basic” will depend on the students’
level and context of learning. It is important to note that almost all authors
who are concerned about the ability of adults or of school graduates to
function in a statistics-rich society do not
discuss what knowledge is needed to be statistically literate per se, but usually argue that all
school (or college) graduates should master a wide range of statistical topics,
assuming this will ensure learners’ ability to engage both in interpretive as
well as in other statistical tasks as adults.
A recent
example can be found in a comprehensive chapter by Scheaffer, Watkins, &
Landwehr (1998), titled, “What every high-school graduate should know about
statistics.” Based on their extensive prior work in the area of teaching
statistics, and on reviewing various curriculum frameworks, these authors
describe numerous areas as essential to include in a study of statistical
topics towards a high-school degree:
·
Number
sense
·
Understanding
variables
·
Interpreting
tables and graphs
·
Aspects
of planning a survey or experiment, including what constitutes a good sample,
methods of data collection and questionnaire design, etc.
·
Data
analysis processes, such as detecting patterns in univariate or two-way
frequency data, summarizing key features with summary statistics, etc.
·
Relationships
between probability and statistics (e.g., in determining characteristics of
random samples, background for significance testing)
·
Inferential
reasoning (including confidence intervals, testing hypotheses, etc.)
Clearly,
few if any adult education programs cover such an agenda, due to time
constraints but also given teachers’ lack of relevant knowledge and training.
Watson (1997) is one of the very few sources that refer, albeit briefly, to the
basic knowledge and skills needed specifically for statistical literacy. She suggests
that these include percentage, median, mean, specific probabilities, odds,
graphing, measures of spread, and exploratory data analysis.
It is
proposed here that six different but related mathematical and statistical
knowledge bases are needed for statistical literacy. These were identified on
the basis of reviewing (a) writing by mathematics and statistics educators
(examples are Moore, 1990; and chapters in Steen, 1997; Gal & Garfield,
1997; Lajoie, 1998), and (b) sources discussing mathematics and statistics in
the news (such as, Huff, 1954; Hooke, 1983; Crossen, 1994; Paulus, 1995;
Kolata, 1998). Given space limitations, these knowledge bases are described in
broad strokes only; they are presented to suggest what kinds of “things” adults
overall need to know and develop in a lifelong perspective, rather than
determine what students are supposed to formally study at any specific
educational stage, given that adult learners may bring with them diverse prior
knowledge acquired both formally and informally, and given that people engage
in multiple learning episodes throughout their lives. Also, these knowledge
bases are described in absolute terms but have to be interpreted in light of
people’s specific life contexts, keeping in mind that people’s numeracy (Gal,
1997) is not a fixed entity but a relative and dynamic set of skills and
interrelated processes.
1. Possess mathematical foundations:
Adults should posses not only number sense (pertaining both to numbers
and probabilities), but also knowledge of proportionality concepts, especially
of percent, which is a key concept used when reporting statistical results in
the media. Familiarity with principles of reading graphs and tables and ability
to integrate information from their different parts. Advanced arithmetical
knowledge and multi-step problem-solving are needed mainly for learning
data-analysis techniques, but are somewhat less important for functioning in
listening contexts per se. That said, broader mathematical knowledge is overall
essential, since statistical arguments may appear together with other
mathematical issues that are not statistical in nature (Paulus, 1995).
2. Know why data is needed and where it comes from: Understand key “big ideas” behind statistical investigations in general,
and have some technical knowledge of approaches to the design of different
studies (survey, experiment, Census, exploratory), and the logic behind these,
including some sampling and measurement methods and dilemmas. Understand the
influence of sampling processes and sample size or composition on
representativeness and ability to generalize or infer to a population.
3. Know how data are processed and analyzed: Know that the same data can be analyzed or displayed in multiple
ways (e.g., by using different measures of central tendency, different graphs,
etc.), and that different methods may lead to similar results but may also
yield a different and at times conflicting view of the phenomena under
investigation. Be aware that error are unavoidable but that they can be
controlled at various stages of a research and be estimated statistically.
4. Know how statistical conclusions are reached: Be aware of the need to base claims or conclusions on credible
empirical evidence. Know that observed differences, associations, or trends may
exist but may not necessarily be large or stable enough to be meaningful or
important. Realize that results of studies may fluctuate over episodes of data
collection, but that such issues should diminish when studies are well-designed.
Know that there are ways to determine the significance of a difference between
groups, or of an association between variables, e.g., by comparing summary
statistics.
5. Understand basic notions of probability and risk: Understand basic notions of probability and chance, and be
familiar with the many ways in which they may be reported, such as percents,
odds, or verbal estimates (see Wallsten, Fillenbaum, & Cox, 1986).
Understand that statements of chance may come from diverse sources, both formal
and subjective, with different degrees of credibility. Realize that judgments
of chance may fluctuate when additional data is available.
6. Know about typical flaws in executing studies, analyzing data, or
interpreting results: Be familiar with key “things
that can go wrong” during the lifecycle of a study. Be aware of typical errors
that researchers, public officials, and others make when reaching conclusions
or conveying findings them to the public, such as confusing correlation with
causality, making small differences loom large, ignoring significance of
observed differences, etc.
This list, offered here in
outline form, can be modified or expanded, depending on the level of the
student and on the desired sophistication of statistical literacy. For
practical reasons, this list is restricted here to domains that can reasonably
be addressed (and in turn assessed) as part of ongoing educational processes.
That said, we must keep in mind that numerous other knowledge elements that
contribute to statistical literacy can be described but are beyond the scope of
this paper. Some involve more literacy-related issues, such as having a sense
for what good journalistic writing looks like (e.g., objective writing,
presentation of two-sided arguments; provision of background information to
orient readers to the context of a story, etc). Other involve broader cognitive
and metacognitive capacities that can support statistically literate behavior,
such as having a propensity for logical reasoning, curiosity, and open-minded
thinking.
Literacy skills
The development of
statistical literacy requires that people can comprehend text and the meaning
and implications of the statistical information in it, in the context of the
topic to which the reported statistical information pertains (Watson, 1997).
The statistical portion of a message may sometimes not be large; comprehension
of surrounding or knowledge of background information will be needed to enable
a reader or listener to place the statistical part in a wider context. In
addition, learners should also be able to present, orally or in writing, clear
and well-articulated opinions, i.e., that contain enough information about the
reasoning or evidence on which they are based so as to enable another listener
to judge their reasonableness. Thus, statistical literacy and general literacy
are intertwined.
Curriculum frameworks in
mathematics now emphasize the need to develop all learners’ communication
skills (e.g., NCTM, 1989; Curry, Schmidt, and Waldron, 1996). Such calls are
also being echoed in statistics education circles (e.g., Samsa, & Oddone,
1994). However, as several authors have pointed out (e.g., Laborde, 1990),
learning mathematical topics requires various types and degrees of interaction
with learners’ literacy skills. Applied to learning statistics, such
interaction may involve, for instance:
·
learning
new words or word clusters (e.g., median, standard deviation, statistically
significant, margin of error) that have only a mathematical usage and meaning
or where the cluster has a meaning different from those of its components.
·
realizing
that meanings of mathematical, statistical, or probabilistic terms used in a
classroom context (e.g., random, table, association, average, sample,
representative, error) may be different or more precise than those used in
everyday speech.
·
being
aware that some quantitative information might be conveyed by terms or embedded
in displays even if no numbers or formal statistical terms are used, e.g.,
information about degrees of uncertainty, trends or changes over time, and
more.
·
Realizing
the need to apply a range of reading comprehension strategies to extract
meaning from diverse texts which touch on statistical or probabilistic issues,
from tersely-written mathematics or statistics textbooks, to journalistic
texts, etc.
Such and
related demands may affect learning, comprehension, and resulting real-world
performance, not only of adults who are bilingual or otherwise have a weak
mastery of English (Cocking, & Mestre, 1988), but also of people who have a
relatively good command of the language. The results pertaining to Document
Literacy, one of three facets of literacy assessed by the recent International
Adult Literacy Survey (IALS), are interesting in this regard (Organisation for Economic Co-operation and Development
& Human Resources Development Canada, 1997). The IALS employed
functional, realistic tasks to assess literacy in large samples of adults in
several industrialized countries, such as the U.S., U.K., Canada, Sweden, Germany
and others. Literacy was viewed as the ability to understand and employ printed
and written information in daily activities, in order to function in society,
to achieve one’s goals, and to develop one’s knowledge and potential. Document Literacy was defined as the knowledge and skills
required to locate and use information contained in various formats, including
job applications, payroll forms, transportation schedules, maps, tables, and
graphics.
Statistical literacy as described in this paper is related to
Document Literacy (Kirsch
and Mosenthal, 1990).The IALS Document Literacy scale included numerous
statistics-related tasks, for instance making sense of graphs such as those
that routinely appear in newspapers, interpreting statements with percents, or
integrating information across graphs and tables. Such tasks require that
adults not only locate specific information in given texts or displays, but
cycle through various parts of diverse texts or displays, make inferences, or
apply other reading comprehension strategies, quite often in the presence of
irrelevant or distracting information. IALS results indicated that, roughly
speaking, between one-third and two-thirds of all adults in most of the
countries studied had difficulty with many Document Literacy tasks. While
details of the IALS results are too complex to be described here, they do serve
to demonstrate the many interdependencies between literacy and statistical
knowledge and highlight the complexity of the notion of statistical literacy.
Critical questions
We would like
adults to not only be able to comprehend graphical displays or statements and
texts with embedded statistical terms or data-based claims (i.e., simply
understand what is being said or shown), but also have "in their head"
a list of critical questions which relate to things to worry about regarding
that which is being communicated or displayed to them. Such a critical list
should be a direct outgrowth of adults’ possession of the statistical knowledge
bases outlined above, at least in a rudimentary form. When faced with an
interpretive statistical task, we imagine people running through this mental
list and asking for each item, "Is this question relevant for the
situation/ message/ task I face right now?" Examples for possible
questions include (but are not limited to):
1. Where did the data (on which this statement is based) come from?
What kind of study was it? Is this kind of study reasonable in this context?
2. Was a sample used? How was it sampled? Is the sample large
enough? Did the sample include people/things which are representative of the
population? Overall, could this sample reasonably lead to valid inferences
about the target population?
3. How reliable or accurate were the measures used to generate the
reported data?
4. What is the shape of the underlying distribution of raw data (on
which this summary statistic is based)? Does it matter how it is shaped?
5. Are the reported statistics appropriate for this kind of data,
e.g., was an average used to summarize ordinal data; is a mode a reasonable
summary? Could outliers cause a summary statistic to misrepresent the true
picture?
6. Is a given graph drawn appropriately, or does it distort trends
in the data?
7. How was this probabilistic statement calculated, and are there
enough credible data to justify such an estimate of likelihood?
8. Overall, are the claims made here sensible? Are they supported by
the data? (e.g., confusing correlation with causation)
9. Should additional information or procedures be made available to
enable me to evaluate the sensibility of these arguments? Is something missing?
10. Are there alternative interpretations for
the meaning of the findings, different explanations for what caused them, or
additional or different implications?
Answers people
generate to such and related questions can support the process of evaluating
statistical messages and lead to the creation of more informed opinions. This
list can of course be modified and expanded, depending on the level and functional
needs of the learners and on the context and goals of instruction. For example,
it can expand beyond basic statistical issues to cover statements of
probability and risk, statistical significance, or more advanced or
job-specific statistical topics, such as those related to statistical process
control or quality management processes.
It is not enough that
students have access to relevant knowledge bases or are aware of critical
questions they could apply to a
message at hand. To actually act in a statistically literate way, certain
dispositions and beliefs need to be in place. First, and most importantly,
learners should develop a propensity to spontaneously invoke, without external cues, the list of worry
questions, and invest the mental effort needed to ask penetrating questions and
try to answer them. Otherwise, they might learn to accept without questioning
objectionable arguments, and may develop an incorrect world view.
Further, learners should
uphold the belief that there may be alternative interpretations or implications
to any finding which is based on statistical processes (e.g., a sample may be
non-representative, an intervening variable affected the results of the study).
They should feel comfortable with the role of being a critical evaluator of
statistical claims, and accept that it is legitimate to have doubts and
concerns about any aspect of the study or the interpretation of its results,
and to raise questions about statistical information being communicated to
them, even if they have not learned much formal statistics.
While we want students to
develop a critical stance, we also want them to develop an appreciation for the
power of statistical processes, and accept that the use of systematic
data-gathering procedures, be they surveys, controlled experiments, or
exploratory studies, often leads to conclusions that are better than those
obtained by relying on anecdotal data or subjective experiences.
Statistical literacy is part
of both people's numeracy and literacy (Gal, 1999). A
recent white paper of the European Commission argued that in a society in which
the individual will have to understand complex situations and vast quantities
of varied information, “there is a risk of a rift appearing between those who
are able to interpret, those who can only use, and those who can do neither”
(European Commission, 1996, p. 8). Yet, even though statistical literacy
is an important area and included in the rhetoric of public officials and
educators at all levels, especially those involved in numeracy education, it is
hardly represented in existing textbooks or training materials for adult
educators, and gets little attention in standard assessments.
The question is then—how
should students' interpretive skills and statistical literacy be developed?
Many teachers most likely will argue that learning to "do" statistics
promotes achievement of statistical literacy. Many adult educators, however,
visit only briefly the topic of statistics. They may teach their students, for
instance, about bar graphs, how to calculate an average, or what a median
means. This is important as a first step towards statistical literacy, but
unfortunately, such topics are all too often taught in an isolated manner,
without explicit connection to the way the underlying concepts appear in
adults’ everyday lives. Fragmented teaching is not likely to contribute much to
students' understanding of statistical concepts (Shaughnessy, 1992), or to
their ability to make sense of statistical messages.
Students'
statistical and mathematical knowledge bases, as described above, are a
necessary component of statistical literacy, yet some of them are not at the
heart of existing curricula in statistics. It is argued here that teachers must
therefor also work directly towards
statistical literacy, and emphasize broader aspects of research design and
origins of data, interpretation issues, critical questions, and supporting
dispositions and reasoning processes as described earlier. The development of
statistical literacy requires work on broader interpretive questions that put
into action concepts as well as critical perspectives learned before, in the
context of authentic tasks. Texts of relevance to learners’ lives can be found
in local newspapers or TV broadcasts, leaflets distributed by political
candidates, advertisements, health education brochures from medical
organizations, etc. Many resources for statistics education can be used to
obtain examples for media-related classroom projects and ideas for class
discussions, from the seminal Quantitative Literacy series (e.g., Landwehr,
Swift, & Watkins, 1987), to the newspaper excerpts and accompanying
discussion questions distributed regularly on the internet by the Chance Course
(at: www.dartmouth.edu/~chance).
To support the development
of adult learners’ statistical vocabulary and communication skills, including
those needed for modern workplaces, better integration of numeracy-related and
literacy activities is required. Many suggestions made towards developing
communication aspects in mathematical classes can be adapted, such as having
students keep journals of their work, prepare oral or written reports from
statistical projects, make short presentations, or design their own math
stories (Hicks and Wadlington, 1999).
Motivational barriers are
likely to be an issue, as statistical work often ends with findings whose
interpretation, meaning and quality are a matter of opinion. In contrast, much
of school-type mathematics is often viewed by many adult learners, and
sometimes by their teachers, as involving procedural learning and solutions
that can be categorized as right or wrong. Students as well as teachers should
realize that the "rules of the game" are different when it comes to
thinking about statistical issues, and that they should take an active role in
forming opinions and in explaining the reasoning behind them (Gal, 1998).
Extension
of numeracy education to include an emphasis on statistics in general and on
development of statistical literacy in particular may be also hampered by
teachers' lack of knowledge, or concern about need to devote more time to
topics that appear more central. However, work on statistical topics offers
unique opportunities to enhance quantitative reasoning and important
communication skills that mathematics educators have been struggling for years
to advance, but with limited success. At a time when statistical knowledge, in
both formal and informal forms, is increasingly being considered essential for
effective citizenship and as a part of required workforce preparation, adult
education systems should open a dialogue and seek strategies that can support
the development of statistical literacy and thus help fulfill the promise of
informed citizenship for all.
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