"Statistical literacy is the ability to
read and interpret summary statistics in the everyday media: in graphs,
tables, statements, surveys and studies. Statistical literacy is
needed by data consumers – students in non-quantitative majors: majors with
no quantitative requirement such as political science, history, English,
primary education, communications, music, art and philosophy. About 40% of
all US college students graduating in 2003 had non-quantitative majors."
By Milo Schield in "Assessing Statistical Literacy: Take CARE" Ch 11 in
Assessment Methods in Statistical Education, pp. 133-152.
Wiley 2010 Schield excerpts
Short introduction to Statistical Literacy.
For more on confounding, see Standardizing.
Dennis Haack wrote the first statistical literacy textbook in 1979.
29% of US Freshman completed stats in high
school (15% took AP Stats), so 14% took non-AP Stats.
2012 Am. Freshman
UK Parliament Briefing paper on Statistical Literacy
StatLit.org website had more than 26,000
downloads in May, 2013: the highest number in our ten-year history.
Statistical literacy: "the ability to
read and interpret statistics, and think critically about arguments that use
statistics as evidence"
Development Dictionary (move slider to "s") [link broken/missing in
Statistical literacy: "understanding
the basic language of statistics (e.g., knowing what statistical terms and
symbols mean and being able to read statistical graphs), and understanding
some fundamental ideas of statistics."
GAISE College Report
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MOST IMPORTANT StatEd JOURNAL
ARTICLE SINCE 2002
If you read just one article,
Challenging the state of the art in post-introductory statistics
by Tintle, Chance, Cobb, Rossman, Roy, Swanson and VanderStoep.
"By introducing confounding as 'one of the two major themes in
statistical analysis' this paper is arguably the most important
paper in statistical education since 2002 when Howard Wainer
publicized 'The BK-Plot: Making Simpsons' Paradox Clear to the
Masses'. The Wainer and Tintle papers mark a new
beginning of statistics education for the 21st century." Milo Schield,
NEWEST 2013 ARTICLES POSTED TO
09 Statistics Education:
Steadfast or Stubborn? Schield Audio
Know your variables: critical element of statistical analysis: Miller
09 Responsible Stats ... to
Shape Public Opinion
A Nelson 6up
Components of Numeracy Infusion Course ... Wilder ASA 2013
President's Message: Statistical Literacy ... by R. Carver 2012
07 David Moore Page at
StatLit.org with numerous publications.
Hawkins Response to New Pedagogy... by D. Moore ISR, 1997
Literacy of OB-GYN Residents,
Anderson et al, JGME 2013
NEWEST 2013 ARTICLES POSTED TO
Paired Confidence Intervals using Excel by Schield
A Startup Guide by
05 Effects of Course on Statistical Literacy
by Rose Martinez-Dawson
Analog Scales: T Knapp
N versus (N-1)
Re-visited, To pool
or not to pool by Knapp
Yourselves (and Public) Know the Truth: Cohn Significance '99
Literacy Serves Police Officers by
reproducibility: Begley's Six Rules by Bruce Booth 9/2012
03 Test for Randomness:
Applied to Stock Data. Strandberg & Iglewicz
TOP 8 DOWNLOADS: FEBRUARY 2014
Top Eight Paper Downloads (#
Quantitative Scholarship: From Literacy to Mastery. U. TX-SA
Graphs in USA Today. Milo Schield 2006 ASA
Presenting Confounding Graphically Using Standardization
451 Responsible Stats ... to
Shape Public Opinion
A Nelson 6up
Literacy: A New Mission for Data Producers Schield 2011
Developing a Test of
Normality in the Classroom by Jernigan 2012
323 Check Distributional Assumption: “Benford’s Law”
W. Goodman 2013
306 Framework for Interpreting
Tables & Graphs Kemp & Kissane 2010
Top non-Paper Downloads (# downloads)630 www.StatLit.org/pdf/create-lognormal-excel2013-demo-6up.pdf
StatLit 2009 textbook: Chapter 4 Schield
POPULAR STATLIT AUTHORS IN 2013
ACADEMIC STATLIT AUTHORS IN 2013
MOST POPULAR STATLIT PAPERS in 2013
Graphs in USA Today. Milo Schield 2006 ASA Proceedings.
Statistical Literacy: Uses & Abuses of Numbers by Andrew Nelson
Presenting Confounding Graphically Using Standardization
by Milo Schield. 2006 STATS magazine.
Literacy: A New Mission for Data Producers by Milo Schield. 2011
Univ. Texas San Antonio: Quantitative Scholarship - Final Draft
Press release 2009
Statistics for Political Science
Majors. Gary Klass 2004 ASA
OTHER RECOMMENDED INTRO BOOKS
Victor Cohn (1989),
News and Numbers
How To Lie with Statistics
Edward Tufte (1995),
presenting a general background or overview.
Thirteen articles involving the W. M. Keck Statistical Literacy Project:
Sense of Statistics by Nigel Hawkes and Leonor Sierra. Section
1: If a statistic is the answer, what was the question?
Section 2: Common pitfalls. Section 3: How sure are we?
Section 4: Percentages and risk; knowing the absolute and relative
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2014 GENERAL INTEREST NEWS
DSJIE (Decision Science Journal of
Innovative Education) is soliciting submissions for two special issues.
Rethinking Undergraduate Business Education: In the Classroom and
submission deadline June 1, 2014). Educational Innovation and
Reform in the Decision Sciences Using Multidisciplinary and
Collaborative Practices (Call:
submission deadline August 1, 2014).
Willful Ignorance: The Mismeasure of Uncertainty by Herbert Weisberg
2014 Feb 28.
Improving [NZ] journalists’ statistical literacy via a new unit standard
Misconceptions of science and religion
found in new study
• Nearly 60 percent of evangelical Protestants and 38 percent of all
surveyed believe “scientists should be open to considering miracles in
their theories or explanations.” • 27 percent of Americans feel that
science and religion are in conflict. • Of those who feel science and
religion are in conflict, 52 percent sided with religion. • 48 percent
of evangelicals believe that science and religion can work in
collaboration. • 22 percent of scientists think most religious people
are hostile to science. •Nearly 20 percent of the general population
think religious people are hostile to science. • Nearly 22 percent of
the general population think scientists are hostile to religion. •
Nearly 36 percent of scientists have no doubt about God’s existence.
2014 Feb 16.
Teaching Big Data:
Experiences, Lessons Learned, and Future Directions by by Betsy Page
Sigman, William Garr, Robert Pongsajapan, Marie Selvanadin, Kristin
Bolling, Greg Marsh, Georgetown University. DSI p 9-15.
The Texas Board of Education adopted two new math courses on Thursday that
are designed to cover much of the same material offered in algebra II,
which will no longer be required for high school students under the
Legislature's academic curriculum overhaul.
The board voted ... to create two high-level math courses that could be
alternatives: statistics and algebraic reasoning. Both will be developed
by local schools under the guidance of the Texas Education Agency, and
are designed to cover many of the same concepts covered in algebra II.
2014: Jan 31.
Texas drops Algebra II requirement; offers statistics and algebraic
reasoning as alternatives.
Jan 27 issue, Woman's World magazine cover:
"Breakthrough new study: This lifesaving diet lowers yours obesity
risk 300%." Page 19: "A new study found folks who eat
vegan are 300% less likely to ever become obese. That's triple
protection against obesity for life!"
IMS UPCOMING PROFESSIONAL EVENTS
2014: May 19-23.
sessions should address one of the three themes: (1)
Teaching from Big Data: What are some of the issues
and challenges when it comes to using big data for teaching and
learning purposes? How can a focus on big data change the way we
teach statistics? How can we teach data analytic methods that
draw insights from massive data? (2) The Impact
of the Common Core: How can we better prepare, at the
college level, future teachers of statistics at all levels
(K-16)? How must teachers be prepared to deal with the Common
Core State Standards? Further, how should the teaching of
statistics at the college level change in light of changes in
the K-12 statistics curriculum? (3) Bridging the
Disciplines: How can we enhance the centrality of
statistics across the disciplines? What can we learn from and
take from other disciplines in order to create a more positive
learning experience for our students? How can we connect with
other disciplines and forge relationships with these disciplines
that will be mutually beneficial? How might we create valuable
learning experiences for students that will prepare them to work
in multidisciplinary teams?
1) Selected talks on Teaching from Big Data: (All times are
Eastern Std. Time)
Mon 11:00 Keynote "Preparing K-12 Teachers to Navigate
the Data Stream" with Christine Franklin, University of Georgia
Mon 15:30 "Big Data, Data Science and Next Steps for
the Undergraduate Curriculum" with Nicholas J. Horton,
Tues 11:30 "How Introductory Applied Statistics Course
Instructors Can Introduce Big Data through Four of its Vs".
John McKenzie, Babson College
Tues 2:30 Two Big Ideas for Teaching “Big Data” Analytics
by Milo Schield.
Wed 10:00 "Statistics for the 21st Century: Are we
Teaching the Right Course?" with Richard Deveaux, Williams;
& Daniel J. Kaplan, Macalester
Wed 11:00 Keynote "Fundamentally
Changing Maths Education for the New Era of Data Science"
with Conrad Wolfram, The Wolfram Group
Wed 14:15 "Panel on Big Data"
Tues 2:30: Two Big Ideas for Teaching “Big Data” Analytics by Milo Schield.
DESCRIPTION: Confounding and coincidence are two statistical
influences that dominate when dealing with “big data.” These
ideas are being taught in Augsburg’s “Statistical Literacy for
Managers” course using Excel. Participants will access
PowerPoint slides that demonstrate the influence of confounding
on OLS regression, on logistic regression and on other
multivariate models using Excel. Participants will demonstrate
how coincidences are expected in "Big Data" using Excel.
Engagement: Participants will be encouraged to (1) form
generalizations on the influence of confounding and coincidence
as the number of data records increases, (2) discuss the
importance of teaching these ideas in introductory statistics,
and (3) discuss the ease or difficulty in teaching these ideas
using Excel. Take-away: Participants should have a better
idea of what might be taught when dealing with big data in
2014: July 7-11.
2014 IMS Annual Meeting Sydney, Australia
2014: July 13-18.
Flagstaff, AZ. Theme:
“Sustainability in Statistics Education”
Jul 13 Sun: 6 PM
Opening Ceremony. 7 PM Opening reception.
Jul 14 Mon: 10:55 7B Statistical literacy
requirements for teachers
Improving statistical literacy without a statistics class,
13:45 7D Best practices in developing statistical literacy
16:15 7F Factors that affect statistical literacy II;
10A Developing statistical literacy amongst in-service
teachers through a collaborative project by
Steve MacFeely More ways to Heaven than one: improving
statistical literacy in Ireland by
1C Outreach efforts to enhance statistical education
and statistical literacy in Hungary by Peter Kovacs
Jul 15 Tue: 9:00 David Spiegelhalter (Cambridge)
What can we learn
from real-world communication of risk and uncertainty?
10:55 7C Assessment of Statistical Literacy
15:45 7E Factors that
affect statistical literacy I
Jul 16 Wed:
10:55 8I Research on Risk Literacy
Jul 17 Thu 10:55 6B Conditional
probability and statistics literacy by
12:30 ISLP Project -- Open Meeting
7A Statistical literacy beyond the
classroom [Schield, Lesser & Petty]
Jul 18 Fri:
13:45 1D Building capacity for developing statistical
literacy in a developing country by Temesgen Zewotir
4F Opening up the data world wider and faster
9D E-learning, E-teaching and E-assessment in fully
online, blended and open virtual web-based courses
Topic 7: Statistical
literacy in the wider society (Robert C delMas, Sebastian
Kuntze, Michiko Watanabe). "A perennial ICOTS theme is our
special responsibility to develop sustainable initiatives which
enable citizens to lead and extend debates, in the media and
elsewhere, on issues of inequality, crime, effects of smoking, use
of alcohol, and support for societal preferences. This democratic
imperative leads us to questions such as: How can we encourage
people to want to engage in statistical learning? How can we
contribute to subject-specific learning of relevant statistical
knowledge? How do we enrich our understanding of statistical
literacy and methods by which it can be attained and sustained?
These invited sessions seek to explore and enrich a variety of
effective practices and interventions."
Topic 7 Sections: 7A (Carl Lee) Statistical literacy
beyond the classroom; 7B (Sebastian Kuntze) Statistical literacy requirements for
teachers; 7C (Rosemary Callingham) Assessment of statistical literacy; 7D
(Iddo Gal) Best practices in
developing statistical literacy; 7E (Einav Aizikovitsh-Udi and David
Clarke) Factors that affect statistical literacy.
Session 7: Topics, Titles and Abstracts
beyond the classroom. Milo Schield 7A1:
Odyssey: a journey
to lifelong statistical literacy. Larry Lesser 7A2:
Teaching Statistics for
Critical Engagement Beyond Classroom Walls.
Nicola Ward Petty 7A3:
Taking statistical literacy to the masses
with YouTube, blogging, Facebook and Twitter
Statistical Literacy Requirements for Teachers:
7B1 Statistical Literacy Requirements for Teachers by
7B2 Improving statistical knowledge
for teaching of variability through professional development by
Helena Wessels (South Africa).
7B3 Teachers’ views
related to goals of the statistics classroom – from global to
content-specific by Sebastian Kuntze (Germany).
Section 7C: Assessment of statistical literacy
Rosemary Callingham (Australia). 7C1
literacy - relating assessment to the real world by Penelope
Bidgood (United Kingdom). 7C2
Validity of the LOCUS Assessments
by Catherine Case (United States).
7C3 Assessing teachers’
statistical literacy by Helen Chick, Roger Wander and Robyn
Section 7D: Developing statistical literacy: Case studies and
lessons learned. 7D1
Students’ beliefs about the
benefit of statistical knowledge when perceiving information
through daily media by Alexandra Sturm and Andreas Eichler
(Germany). 7D2 Changing the course: from boring numeracy
to inspiring literacy by Kimmo Vehkalahti (Finland). 7D3
numeracy infusion course for higher education (NICHE):
strategies for effective quantitative reasoning (QR) instruction
by Esther Isabelle Wilder, Elin Waring, Frank Wang and Dene
Hurley (US). 7D4
Implementing a QL core
competency requirement in the College of Arts and Science at
Miami University by John Bailer (US).
Section 7E: Factors that Affect Statistical Literacy.
7E1 Critical thinking as an impact factor on statistical
literacy – theoretical frameworks and results from an interview
study by Einav Aizikovitsh-Udi (Israel) and Sebastian Kuntze
A multilevel perspective on factors influencing
students’ statistical literacy by Ute Sproesser, Sebastian
Kuntze and Joachim Engel (Germany).
Session 7F: Factors that
affect statistical literacy II Monday 16:15 Session
organizer and Chair: Einav Aizikovitsh-Udi 7F1 Laura
Reconceptualizing statistical literacy: Developing an assessment
for the modern introductory statistics course. 7F2
statistical literacy through supplemental instruction.
7F3 Antonio Orta
Interpreting variation of data in risk-context by middle school
Section 8I: Research on Risk Literacy.
8I1: Getting alternative representations for risk into the
school syllabus by David Spiegelhalter (UK). 8I3
literacy: first steps in primary school by Christoph Till
Comparing fast and frugal trees and
Bayesian networks for risk assessment by Kathryn Laskey (United
Selected Contributed papers:
* C109* After statistics reform: should we still teach
significance testing? by Tony Hak (The Netherlands)
* C122 Students understanding of confidence intervals:
implications for teaching by Robyn Reaburn (Australia)
* C134 It is time to include data management in
introductory statistics by Robert H. Carver (United States).
* C196 The status of reform in statistics education: A
focus on the introductory course by Rossi Hassad (United
* C203* A shiny new opportunity for big data in statistics
education by Karsten Tait Maurer (United States)
* C204* Sustaining communication of the value of statistics
in the humanities by Nicole Mee-Hyaang Jinn (United States)
* C206 A graphical illustration of binomial distributions
by Kang Sup Lee and Dong Joo Yang (Korea)
* C212 An evaluation of the statistical methods used by
business researchers in South African publications by Gary D
Sharp (South Africa)
* C216 Teachers’ confidence in teaching statistical ideas
by Rosemary Callingham and Jane M. Watson
* C226* How I learned to stop worrying and just teach MBA
5800 (with apologies to Dr. Strangelove) by Alan S Chesen
* C235* Improving Statistical Literacy through
Supplemental Instruction Alexandra Kapatou (United States)
* C238 Teaching hypothesis testing: a necessary challenge
by Wendy J. Post and Marijtje A.J. Van (The Netherlands).
* C242 Developing conceptual understanding: the role of
interactive dynamic technology by Gail Burrill (United States).
Reconceptualizing statistical literacy: Developing an assessment
for the modern introductory statistics course by Laura Ziegler
* C281 On the (un)sustainability of the criticism of the
coefficient of determination by Janez Stare (Slovenia)
* C283 Against all odds: Inside statistics by Marsha Davis
* P02 A first course in data mining: sustaining statistical
education in the modern business curriculum. Deborah Gougeon
* P10 The “lady” has a name:
teaching history of statistics using Salsburg with corrections.
Kirk Anderson and Phyllis Curtiss
* P21 Assessing students’ statistical reasoning: Dana
Kirin and Jennifer Noll
* P23 Development of
statistical literacy in undergraduate students Diana Keosayian
* P26 Evidence-based
policy making and statistical education. Kyung Ae Park
* P31 Epidemic: a computer based training environment for
informal inferential reasoning. Joachim Engel and Tim Erickson
* P34 Using actionable intelligence to enhance student
success in introductory statistics courses. Brenda Gunderson and
* P36 High school teachers’ conditional probability
content knowledge Adam Molnar
* P37 Attention to details:
does it facilitate or impede learning. Bill Rybolt
* P43 Keeping the momentum:
sustaining interest in statistics lectures for non-statisticians
Angie Wade Eirini Koutoumanou and Vicki Aldridge * P48 Exploring statistical literacy in Northeast China
* P45 Towards a more conceptual way of understanding and
implementing inferential rules. Johanna Hardin and Thalia
* P50 On statistics
education innovation from statistics development perspectives.
Yu Zhu Meng Wang
2014: Aug 2-7.
2014 Boston, MA.
Data Analytics in Business and Education. Topic-contributed
panel. Sponsor: Section on Statistical Education. Session
210126; Abstract 311733. Organizer: Milo Schield. Chair:
Robert Carver. Abstract: Data analytics is a rapidly changing field
and a driving force in business, in schools of business and in
statistics education. Panelists will provide background on their
connection with data analytics in business and address three
questions: (1) What statistical problems are you dealing with in
analyzing data? (2) What statistical tools or techniques are you
using or planning to use? (3) What underlying statistical ideas
should statistical educators be teaching budding data analysts to
help them better analyze "big data"?
2014: Nov 22-25.
DSI Annual Meeting Tampa, FL.
Submission Deadlines. May
1, 2014 - Full Paper Submissions and All Competition Entry
Submissions. May 15, 2014 - Abstract Submissions, Panel,
and Workshop Proposals.