Qualitative
Literacy Series
E. Van Harken
MED 600
11/9/15
"As the economy adapts to information-age needs, workers in
every sector ... must learn to interpret intelligently computercontrolled processes. Most jobs require analytical rather than
merely mechanical skills, so most students need more
mathematical power in school as preparation for routine jobs.
Similarly the extensive use of graphical, financial, and
statistical data in daily newspapers and in public policy
discussions compels a higher standard of quantitative literacy
for effective participation in a democratic society. Citizens
who cannot properly interpret quantitative data are, in this
day and age, functionally illiterate.“
(Reshaping School Mathematics, Mathematical Sciences
Education Board, 1990)
Origins
 Joint
Committee with members from
the American Statistical Association (ASA)
 the National Council of Teachers of Mathematics
(NCTM)
formed the Qualitative Literacy Project

 Goal
was to introduce the most relevant and
essential topics in statistics into the elementary and
secondary curriculum
 funded in part by the National Science Foundation
Activities of the QLP
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
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Held regional conferences for teachers beginning in
1988
Statisticians and teachers collaborated to write and field
test curriculum materials in data exploration and
probability that were both relevant to the time and
accessible to students
The approach was to use real and interesting data and
simulate real events to show how statistics can be
practical
Used a lot of graphing and taught practices that were
currently in vogue for real statisticians
Major teaching methods were hands-on activities, group
discussions, projects, and writing reports.
Provided a starting point for the Standards on statistics
to be written
The Qualitative Literacy Series
 Grades
7-12
 Introduction to data
analysis
 Students learn how to
make various graphs and
analyze plots in order to
describe data, recognize
patterns, and make
conjectures




Grades 6-9
Develops a working
knowledge of
probability
Emphasis is on
experiments and
estimation
Explore the difference
between empirical and
theoretical probability
The Art and
Techniques of
Simulation
 Grades
7-12
 Meant to follow
Exploring Probability
 Simulations
used to
demonstrate real-life
applications of
probability and statistics
 Developed analytical
reasoning
Exploring Surveys
and Information
from Samples

Grades 10-12
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
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
somewhat geared towards
above average students
Most advanced topics in the
series
Ideally follows the other three
units
Teaches statistics foundational
to understanding sample
surveys and how to think
critically (be skeptical of) polls
and surveys that bombard the
daily experience
CC Standards Addressed


6. SP.5 Summarize numerical data sets in relation to their
context, such as by: a. Reporting the number of
observations.
7.SP Use random sampling to draw inferences about a
population.
. Understand that statistics can be used to gain information
about a population by examining a sample of the population;
generalizations about a population from a sample are valid
only if the sample is representative of that population.
Understand that random sampling tends to produce
representative samples and support valid inferences.
2. Use data from a random sample to draw inferences about
a population with an unknown characteristic of interest.
Generate multiple samples (or simulated samples) of the
same size to gauge the variation in estimates or predictions.
For example, estimate the mean word length in a book by
randomly sampling words from the book; predict the winner of
a school election based on randomly sampled survey data.
Gauge how far off the estimate or prediction might be.
 CC
Algebra gets pretty heavy into statistics, residuals, and
correlation coefficients
 Rich, generative problems might make it less of a schlep
 These materials provide beautiful ideas for contextualized
examples

might need updating to make them relevant and interesting to
2015
 Steal
away!
CC Standards Addressed
 S-ID
Interpret linear models
8. Compute (using technology) and interpret
the correlation coefficient of a linear fit.
 8.SP Investigate patterns of association in
bivariate data.
1. Construct and interpret scatter plots for
bivariate measurement data to investigate
patterns of association between two
quantities. Describe patterns such as
clustering, outliers, positive or negative
association, linear association, and nonlinear
association.
CC Standards Addressed

7.SP Use random sampling to draw inferences about a
population.
1. Understand that statistics can be used to gain information
about a population by examining a sample of the
population; generalizations about a population from a
sample are valid only if the sample is representative of that
population. Understand that random sampling tends to
produce representative samples and support valid
inferences.
2. Use data from a random sample to draw inferences
about a population with an unknown characteristic of
interest. Generate multiple samples (or simulated samples)
of the same size to gauge the variation in estimates or
predictions. For example, estimate the mean word length in
a book by randomly sampling words from the book; predict
the winner of a school election based on randomly sampled
survey data. Gauge how far off the estimate or prediction
might be.
References
 Scheaffer,
Richard L. The ASA-NCTM
Quantitative Literacy Project : An Overview.
Gainesville: 1990. Print.
 The teacher materials of the QLS