Judea Pearl


Authors Popular StatLit News Authors-Academic Statistical Literacy Numeracy Statistical Reasoning

Judea Pearl (above) sponsors ASA Causality in Statistical Education Award. The committee is pleased to announce that a gift from Microsoft Research will enable the prize to double in 2014. A $10,000 prize or two $5,000 prizes will awarded this year. For additional information about the award, see the Amstat News articles at http://magazine.amstat.org/blog/2012/11/01/pearl/ and http://magazine.amstat.org/blog/2013/08/01/causality-in-stat-edu/. Nominations and questions should be sent to the ASA office at educinfo@amstat.org. The nomination deadline is April 15, 2014. Visit http://www.amstat.org/education/causalityprize/ for nomination information.   More on Judea Pearl

"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"  United Nations Development Dictionary (move slider to "s") [link broken/missing in 2012]

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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Yearly highlights of grants, new books, conference papers (ICOTS, ISI, JSM, JMM), and events involving statistical literacy. 
Milo Schield, Editor www.StatLit.org


Newest StatLit.org web pages: 


If you read just one article, read 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, StatLit Editor



09  Statistics Education: Steadfast or Stubborn? Schield Audio 1up 6up

09  Know your variables: critical element of statistical analysis: Miller

09  Responsible Stats ... to Shape Public Opinion by A Nelson 6up 2012

09  Key Components of Numeracy Infusion Course ...  Wilder ASA 2013

08  ASA President's Message: Statistical Literacy ... by R. Carver 2012

07  David Moore Page at StatLit.org with numerous publications.

07  Anne Hawkins Response to New Pedagogy... by D. Moore ISR, 1997

06  Statistical Literacy of OB-GYN Residents, Anderson et al, JGME 2013


05  Display Paired Confidence Intervals using Excel by Schield  1up   6up

05  Odysseys2Sense: A Startup Guide by Schield  6up  Audio text

05  Effects of Course on Statistical Literacy by Rose Martinez-Dawson

05  Causality, Change, Dichotimization, Likert  & Analog Scales: T Knapp

05  Bias, N versus (N-1) Re-visited, To pool or not to pool by Knapp

05  Help Yourselves (and Public) Know the Truth: Cohn Significance '99

04  Statistical Literacy Serves Police Officers by Irina Soderstrom

03  Scientific reproducibility: Begley's Six Rules by Bruce Booth  9/2012

03  Test for Randomness: Applied to Stock Data. Strandberg & Iglewicz


Top Eight Paper Downloads (# downloads)
2005 Quantitative Scholarship: From Literacy to Mastery. U. TX-SA
1021 Percentage Graphs in USA Today. Milo Schield 2006 ASA
 625 Presenting Confounding Graphically Using Standardization Schield 06
 451 Responsible Stats ... to Shape Public Opinion by A Nelson 6up 2011
 439 Statistical Literacy: A New Mission for Data Producers Schield 2011
 278 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
474 www.StatLit.org/pdf/ztest-function-excel-2008-6up.pdf
327 www.StatLit.org/pdf/ttest-command-excel-2013-1up.pdf
238 www.StatLit.org/pdf/ttest-function-excel-2008-6up.pdf
233 www.statlit.org/pdf/create-confidence-intervals-excel2010-1up.pdf
147 www.statlit.org/pdf/create-confidence-intervals-excel2010-6up.pdf

534 StatLit 2009 textbook: Chapter 4 Schield Audio 6up


















  1. Percentage Graphs in USA Today. Milo Schield 2006 ASA Proceedings. 

  2. Statistical Literacy: Uses & Abuses of Numbers by Andrew Nelson 6up 

  3. Presenting Confounding Graphically Using Standardization by Milo Schield. 2006 STATS magazine. 

  4. Statistical Literacy: A New Mission for Data Producers by Milo Schield.  2011 SJIAOS

  5. Univ. Texas San Antonio: Quantitative Scholarship - Final Draft    Press release 2009

  6. Statistics for Political Science Majors. Gary Klass 2004 ASA


  • Victor Cohn (1989), News and Numbers

  • Darrell Huff (1954), How To Lie with Statistics

  • Edward Tufte (1995), Visual Explanations




S/L Books

Q/L Books


Q/L Texts



W. M. Keck Statistical Literacy Project

Web-accessible articles presenting a general background or overview.

Statistical Literacy:


Quantitative Literacy:

Thirteen articles involving the W. M. Keck Statistical Literacy Project:


  •  Making 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 changes.  


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  • DSJIE (Decision Science Journal of Innovative Education) is soliciting submissions for two special issues.  Rethinking Undergraduate Business Education: In the Classroom and Beyond (Call: submission deadline June 1, 2014).  Educational Innovation and Reform in the Decision Sciences Using Multidisciplinary and Collaborative Practices (Call: submission deadline August 1, 2014). 

  • 2015 July. Willful Ignorance: The Mismeasure of Uncertainty by Herbert Weisberg

  • 2014 Feb 28. Improving [NZ] journalists’ statistical literacy via a new unit standard

  • 2014 Feb 16.  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 Jan 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.

  • 2014: Jan 31. Texas drops Algebra II requirement; offers statistics and algebraic reasoning as alternatives.   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 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             Statistics

  • 2014:  May 19-23.  eCOTS.   Program
    Presentation 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, Amherst College
    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 introductory statistics.

  • 2014: July 7-11. 2014 IMS Annual Meeting  Sydney, Australia

  • 2014: July 13-18ICOTS-9 Flagstaff, AZ. Theme: “Sustainability in Statistics Education”  Registration
    Jul 13 Sun:  6 PM Opening Ceremony.  7 PM Opening reception.
    Jul 14 Mon:  10:55 7B  Statistical literacy requirements for teachers
                                    5D  Improving statistical literacy without a statistics class, Jennifer Brown
                     13:45 7D  Best practices in developing statistical literacy
                     16:15 7F  Factors that affect statistical literacy II;
    Developing statistical literacy amongst in-service teachers through a collaborative project by
    Delia North
                                    Steve MacFeely More ways to Heaven than one: improving statistical literacy in Ireland by
    Steve MacFeely
                                    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
    Ian Hay
                     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
                     16:45 Closing

    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

    Section 7A:
    Statistical literacy 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

    Section 7B:  Statistical Literacy Requirements for Teachers: 
    7B1 Statistical Literacy Requirements for Teachers by
    Brian Beaudrie. 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 Towards statistical 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 Pierce (Australia).

    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 A 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 (Germany).   7E2 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 Ziegler Reconceptualizing statistical literacy: Developing an assessment for the modern introductory statistics course.  7F2  Alexandra Kapatou  Improving statistical literacy through supplemental instruction.   7F3 Antonio Orta Interpreting variation of data in risk-context by middle school students

    Section 8I: Research on Risk Literacy
    .  8I1: Getting alternative representations for risk into the school syllabus by David Spiegelhalter (UK).  8I3 Risk literacy: first steps in primary school by Christoph Till (Germany).   Comparing fast and frugal trees and Bayesian networks for risk assessment by Kathryn Laskey (United States). 

    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 States).
    *  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 (United States)
    *  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).
    *  C280* Reconceptualizing statistical literacy: Developing an assessment for the modern introductory statistics course by Laura Ziegler (US)
    *  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 (United States)

    Selected Posters:
    *  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 and Elizabeth Johnson
    *  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 Karen Nielsen

    *  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
    *  P45 Towards a more conceptual way of understanding and implementing inferential rules. Johanna Hardin and Thalia Rodriguez
    *  P48 Exploring statistical literacy in Northeast China Robert Giebitz
    *  P50 On statistics education innovation from statistics development perspectives. Yu Zhu Meng Wang

  • 2014: Aug 2-7ASA-JSM 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. 


QL = Q/L = Quantitative Literacy,   QR = Q/R = Quantitative Reasoning,    S/L = SL = Statistical Literacy,     S/R = SR = Statistical Reasoning

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