Milo Schield, Editor

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

2018 July: Confounding and Cornfield: Back to the Future.  By Milo Schield (2018) for ICOTS 10.  "Cornfield's minimum effect size is one of the greatest contributions of statistics to human knowledge alongside the Central limit theorem and Fisher's use of random assignment to statistically control for pre-existing confounders."  "To change the future, we need to go back to when Jerome Cornfield argued that smoking caused cancer."  "Our unwillingness to talk about observational causation, confounding and strength of evidence is arguably the primary reason our students' see little value in the introductory statistics normally taught in Stat 101."  "We need to teach multivariate statistics, confounding and the Cornfield conditions so students will appreciate statistics." 

2018 July: Statistical Literacy and the Lognormal Distribution By Milo Schield (2018) for ASA JSM. "There is no public data on the income share of the top 1% of households; those percentages are estimates. Those estimates vary from 5% to 40%. They are based on different data using different definitions and different models."

2018 July: Schield selected to be a Fellow by the American Statistical Association

2018 May: The Book of Why:  A New Science of Cause and Effect. Judea Pearl. Summary and TOCIndex
                   "Judea Pearl's new book, The Book of Why, is a must read for anyone interested in philosophy, science, machine learning or statistics.
                   "The Book of Why is arguably the most important book on causal statistics since Cornfield debated Fisher on whether
                     smoking caused lung cancer."  Schield (2018)

2018 May  Six Books that Sharpened my BS Detector  by Joseph Makasi.

2018 April   Seven Habits of Highly Numerate People by Doug Berdie. Minneapolis Star and Tribune.

2018 March 4: Gartner Advanced Analytics and Big Data Summit.
        Marc Isaacson and Milo Schield (Quant-Fluent) conduct a three-hour workshop on Data Literacy and Statistical Literacy

  • Schield interview with Ryan Dunlap: See bottom of Video page.

  • Quant-Fluent website opened. Marc Isaacson and Milo Schield  (April)

2017 June 21 SERJ Special Issue: Statistical Literacy

  • The future of Statistical Literacy is the future of statistics Editorial by guest editors Jim Ridgway and James Nicholson.
    "On an optimistic note, Milo Schield argues that the 2016 revision of the GAISE Guidelines marks a major step forward in promoting statistical literacy via its increased emphasis on evidence appropriate for decision making – such as paying attention to study design and multivariate data and associated concepts such as confounding."

    "Conclusion:  Statistical literacy is a pre-requisite for an informed democracy. Increasing statistical literacy is a key element in warding off the existential crisis we face. Revising current curricula in school and at university to ensure that there is an adequate focus on using evidence to make decisions in realistic contexts is an essential starting point. At least as important is for statistics educators to take a broader view of their task, and to engage directly with the illiteracies encountered in broadcast and social media – for example by direct critique, or by promoting statistical literacy directly. There is a need for disparate elements of the statistics community to come together; cultivating statistical literacy across the whole of society should be a goal that brings like-minded people together with a common cause."

Invited Editorials:

Research Papers:

Regular Papers:

2017: June Panorama of Statistics: Perspectives, puzzles and paradoxes in statistics by Eric Sowey & Peter Petocz. 

"The authors guide readers, who already know something of statistics, to see the richness of the discipline and to let them discover its fascinations. Among the chapters you can find aspects of statistics (e.g. statistical literacy, intellectual history, and epistemology) that are outside the conventional instructional mainstream and beyond the scope of most textbooks. This is a book which can engage curious students, teachers, and consumers of statistics, as well as practitioners of statistics and of statistics-using disciplines." 

Table of Contents: Part I. Introduction. 1) Why is statistics such a fascinating subject? 2) How statistics differs from Mathematics 3) Statistical literacy - essential in the 21st century! 4) Statistical inquiry on the web. Part II: Statistical description 5) Trustworthy statistics are accurate, meaningful and relevant 6) Let hear it for the standard deviation! 7) Index numbers - time travel for averages 8) The beguiling ways of bad statistics I 9) The beguiling ways of bad statistics II Part III: Preliminaries to inference 10) Puzzles and paradoxes in probability 11) Some paradoxes of randomness 12) Hidden risks for gamblers 13) Models in statistics 14) The normal distribution: history, computation and curiosities Part IV Statistical inference 15) The pillars of applied statistics I - estimation 16) The pillars of applied statistics II - hypothesis testing 17) 'Data snooping' and the significance level in multiple testing 18) Francis Galton and the birth of regression 19) Experimental design - piercing the veil of random variation 20) In praise of Bayes Part V: Some statistical byways 21) Quality in statistics 22) History of ideas: statistical personalities and the personalities of statisticians 23) Statistical eponymy 24) Statistical 'laws' 25) Statistical artifacts Part VI: Answers to chapter questions

2017 May 16 Atlantic. Protecting the Public Commons by Alexander B. Howard May. "A core component of a high school education should include teaching people how to judge risk, statistical literacy, and how to exercise our rights to access public information."

2017 May 12  New ISI Objective: To advocate and foster statistical literacy, the use of statistics and data in decision making by governments, businesses and individuals. ISI 2017 Update of Mission and Objectives 2016 Mission and Objectives.

2017 April 29: Schield invited to talk on Statistical Literacy in Toronto at the Field Institute Math-Ed forumSchedule
Statistical Literacy: What is it...  Who needs it...  What is stopping it...
Slides  Audio  PPTX

2016 July: Offering STAT 102: Social Statistics for Decision Makers. Schield IASE Roundtable in Berlin.

2015: Introduction to Statistical Investigations by Tintle, Chance, Cobb, Rossman, Roy, Swanson & VanderStoep (2015).   Wiley Description & TOC  2014pb

The Math Myth and Other STEM Delusions Book by Andrew Hacker. The Wrong Way to Teach Math 2/2016. NY Times  Is Algebra Necessary? 7/2012 NY Times.  Reviews: Goldstein "Hecker: Down with Algebra II".   2012 Rebuttals: Mehta, Devlin.  2016 Rebuttals:  Devlin


Aug 4.   "Willful Ignorance" by Herb Weisberg (picture above) is now available!!  [Editor:  This book is my #1 pick for 2014.]   Weisberg's grasp of statistical history is comprehensive without being over-whelming.  But this is more than just a history book on statistics.  Weisberg has a point to make -- that statisticians have mis-measured uncertainty!  And this mis-measurement involves "willful ignorance"!!!   These are fighting words for statisticians who consider the proper measurement of uncertainty to be their primary task.  For more details on Herbert Weisberg, visit his page.    If you buy one statistics book this year, buy this one!  Amazon US

Two Big Ideas for Teaching Big Data: Coincidence and Confounding by Milo Schield. ECOTS invited paper downloaded 4,200 times in the seven months it has been posted in 2014.   See also Schield slides presented at Big Data panel.

"I hope that...statistical literacy will...rise to the top of your advocacy list"  Ruth Carver, ASA 2012 Presidential Address

29% of US Freshman took stats in high school (15% took AP Stats), so 14% took non-AP Stats. 2012 Am. Freshman

Spurious Correlations (More than 9,000 computer-generated as of 5/2014): For example: Number of people who died by becoming tangled in their bed sheets correlates with Total revenue generated by skiing facilities (US).  [Great examples, but a high correlation coefficient between two times series does not imply statistical significance -- much less a causal connection. See Cross-correlation.  Editor]

2014 10: Highest Monthly Downloads: October had 45,000 downloads from this site: the highest number in our ten-year history. Last year's monthly  high was 26,000 in May.  The biggest cause is the download of the the PowerPoint demos to create various statistics and models using Excel: over 67,000 YTD.  The "Create-Lognormal-Excel2013" demo has had 36,000 downloads so far this year.

2014 11: Highest Monthly Index Views @ StatLit.org:  November had 6,200 index views -- 33% more than last year's monthly high.



"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.

UK Parliament Briefing paper on Statistical Literacy

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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2014, 13, 12, 11, 10, 09, 8, 7, 6, 5, 4,  03

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Yearly highlights of grants, new books, conference papers (ICOTS, ISI, JSM, JMM), and events involving statistical literacy. 


Newest StatLit.org web pages: 


If you read just one article, read Appendix B of the 2016 update to the ASA GAISE recommendations. This paper argues that introductory statistics courses should include multivariate thinking (and confounding). 


The second-most important paper introduces confounding as 'one of the two major themes in statistical analysis'.  See Challenging the state of the art in post-introductory statistics by Tintle, Chance, Cobb, Rossman, Roy, Swanson and VanderStoep (2013).  The third by the same authors is Introduction to Statistical Investigations (2016).


"By introducing confounding, these three papers are arguably the most important non-Schield papers in statistical education since 2002 when Howard Wainer publicized 'The BK-Plot: Making Simpsons' Paradox Clear to the Masses'.  Together they mark a new beginning of statistics education for the 21st century."  Milo Schield, StatLit Editor



01  Challenge Statistical Claims in Media, Martinez-Dawson ASA 2013

2015 SLIDES and WORKSHEETS HOSTED (by month)

07  2013 MSMESB: MS Business Analytics program.  Nargundkar.    slides



12  AMSTAT: Causality in Statistics Education Award 2013. ASA News

12  Most stat analysis not done by statisticians Simply Statistics 2013

12  Simpson's Paradox in Psychological Science by Kievit et al. 2013.

12  Statistical Literacy Explained by Hewson, Teaching Statistics, 2013

12  Headlines in a Math-Literate World by Orlin, Huffington Post, 2013

12  RSS GetStats Statistical Literacy Campaign and Initiatives. 2014

12  SIGMAA-QL 2013 Newsletter.   Bennet: Writing for general public.

11  Call for Statistical Literacy papers. 2014 Stat-Ed Research Jrnl.

11  Relative Risk Cutoffs for Statistical Significance. Schield 2014

10  SRTL-9 Proposal: Informal Doorways to Modeling. Schield  2014

09  Limitations and Uses of Convenience Samples Kriska et al. ASA 2013

09  Seeing how Statistical Significance is Contextual.  Schield 2003.

08  Simpson's Paradox #30 Classic Problems in Probability. Gorroochurn

08  Simon Schild Maps: Bellenberg Germany & Benton County IA. 2014

08  Schild Family journey from Bellenberg Germany to America. 2002

07  2013 MSMESB/DSI Annual Report by Robert Andrews

07  Odyssey: Lifelong Statistical Literacy Schield 2014 ICOTS  slides
05  Two Big Ideas for Teaching Big Data  Schield ECOTS 2014  slides

04  Teaching Big Data at Georgetown. Sigman et al. Decision Line 2014

03  Augsburg TIDES Proposal: Summary AACU Schield 2014 Full proposal

02  Augsburg's NSF Proposal: Summary.  Schield 2014 Full

01  Visualization of Economic Indicators. Thompson+Wallace. ASA 2013.

01  Fusion & causal analysis in big marketing data.  Mandel ASA 2013

01  Check Distributional Assumption: Benford’s Law. Goodman  ASA 2013

01  Challenge Statistical Claims in Media, Martinez-Dawson ASA 2013

2014 SLIDES and WORKSHEETS HOSTED (by month)

11  Business Analytics and Data Science. Schield DSI 2014 slides

10  Statistical Literacy+Coincidence. Schield NNN1 Workshop 2014 slides
10  Explore Log-Normal Incomes Schield NNN2 2014 Slides xls  Update

10  Creating Distributions Empirically. M. Schield. NNN3 Workshop Slides

10  Statistically-Significant Correlations. Milo Schield. NNN4 2014 Slides

10  Segmented Linear Regression. Schield. NNN5 Workshop 2014 Slides

08  Top 30 Learning Goals for Introductory Sociology. Persell  2010  List

08  Social Science Reasoning & QL Learning Goals Caulfield+Persell'06List

07  2013 MSMESB: Predictive Analytics course. Levine et al.         slides

07  2013 MSMESB: Spreadsheet Analytics. James R. Evans.          slides

07  2013 MSMESB: Implications of Big Data for Stat Ed. Berenson  slides

07  2013 MSMESB: Big Data & Statistics Instruction. Berenson      slides

07  2013 MSMESB: Big Data in Stat 101: Small changes. McKenzie slides

07  2013 MSMESB: Create Business Analytics class. Kirk Karawan.  slides

07  2013 MSMESB: Getting Analytics into the curriculum. Karawan. slides

07  2013 MSMESB: Analytics and the Evolving Workforce. LaBarr.   slides

07  2013 MSMESB: MS Business Analytics program.  Nargundkar.    slides


Top 20 Downloads of Papers        (# months stats tabulated)
5,318 Field Guide to Lies: Information Age (TOC+Intro) Levitin 2016   (10)

3,042 Interpreting Cumulative Frequency Distribution Winkler 2009     (11)
2,904 Substantive significance of regression coef.  Miller 2008 ASA    (12)
2,862 Likert & Visual Analog Scales Tom Knapp 2013                        ( 8)
2,829 Common Statistical Fallacies Social Indicator Data Klass 2008   (12)
2,516 Framework Interpreting Tables & Graphs  Kemp/Kissane 2010    (12)

2,489 Statistical Literacy Guide.  Bolton, UK  2009                           (11)
2,377 Unpublished Quantitative Research Methods Book Knapp 2016   ( 8)
1,874 Percentage Graphs in USA Today Schield 2006 Total 100,052    (12)
1,525 Making Statistics Memorable: New Mnemonics. Lesser 2011 JSM ( 8)
1,308 Practical Approach Intro Poli-Sci Statistics Course Klass 2004    ( 8)
1,301 Statistical Literacy: Thinking Critically about Stats Schield 1999 ( 9)

1,219 Learning Statistics through Playing Cards. Knapp 2012              ( 8)
1,183 To Pool or Not to Pool.  Knapp 2013                                      ( 6)

1,069 Presenting Confounding Graphically/Standardization Schield '06  ( 6)
1,057 Three Paradoxes in Interpreting Group Differences  Wainer 2004 ( 5)
  920 Two Big Ideas for Teaching Big Data    Schield 2014 ECOTS       ( 6)
  873 Numeracy: New Literacy for Data-Drenched Society Steen 1999 ( 6)
  782 Statistical Literacy Curriculum Design    Schield, 2004 IASE        ( 6)

Top Downloads of Excel-Related Slides (All by Schield)
1. 13,167 Create lognormal in Excel 2013. Slides

2.  5,717 Model using Linear Trendline 2Y1X Excel 2013.  Slides

3.  4,989 T-Test command with Excel 2013   Slides

4.  2,381 Create histograms using functions w Excel 2013 Slides

5.  1,930 Create Pivot Tables using Excel 2008  Slides

6.  1,889 Model using Linear Trendline Excel 2013  Slides

7.  1,468 Graph nominal data w Excel 2013 Slides  

8.  1,367 Using the Z-test via functions in Excel 2008  Slides

9.    952 Model Logistic Regression MLE using Excel 2013. Slides

10   802 Model Toolpak Regress linear 3 factor 1Y2X Excel 2013. Slides

11   762 Model Logistic Regression OLS1C Excel 2013  Slides


















76,209  Index to StatLit.org website   [This page]

 3,261   Howard Wainer author page

 3,148   Quantitative Literacy/Reasoning Textbooks as of 2015.  Schield

 2,910   Statistical Literacy Articles by Year posted to www.StatLit.org

 2,845   StatLit News: 2009  News of the Year

 2,749   StatLit News: 2013  News of the Year

 2,689   Joel Best author page

 2,684   Standardizing.   Different techniques by Schield

 2,440   StatLit News: 2012  News of the Year

 2,372   StatLit News: 2011  News of the Year

 2,311   StatLit News: 2010 News of the Year

2,217   StatLit Tools   Excel-based tools for analyzing statistics

2,153   Gerald Bracey author page

2,061   StatLit News: 2008 News of the Year

1,967   Gerd Gigerenzer author page

1,949   Adult Numeracy page

1,849   StatLit News: 2014  News of the Year

1,224   StatLit News: 2007  News of the Year

1,179   Blastland author page

1,110   StatLit News: 2006  News of the Year

1,087   Milo Schield  author page with related items

Note: the leading number is the number of page reads for each page


  • 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:

Fifteen 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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IMS             2018 PROFESSIONAL EVENTS             Statistics

2018 July 29-Aug 3  ASA Online Program JSM Vancouver

SUNDAY: 4:00 - 5:50 CC-West Ballroom A The Good, the Bad, and the Ugly: The Future of Statistics and the Public — Invited Panel ISI.
             Panelists: David Spiegelhalter RSS, Dan Wagner, Civis Analytics, Richard Coffin, USAFacts, Mark Hansen, Columbia University
MONDAY: 8:30-10:20 StatEd #128 CC-West 115 StatEd Curricular Considerations
       9:05 Teaching Bayesian statistics in undergraduate classes — Ananda Jayawardhana, Pittsburg State
       9:20 Statistics Projects in a PIC-MATH Course — Debra Hydorn, University of Mary Washington
       9:35 Statistical Literacy and the Log-Normal Distribution — Milo Schield, Augsburg U. Abstract
       9:50 A Venn-Diagram Analysis of the Role of Statistics in Data Science — John McKenzie, Babson College
11:50 CC-East 19 Income Inequality Grew Faster Than Reflected by Standard Measures — J. Gastwirth, George Washington U
12:30 Roundtable. New Quasi-Experimental Devices for Observational Studies Dylan Small, U. Penn
2:05 #236 CC-West 115 A Classical Regression Framework for Mediation Analysis. Christina Saunders
4:00 Stat Ed Booth Discussion Group.  Jeff Witmer: Teaching Stats at small liberal arts college
TUESDAY =============================================
7:00 - 8:15 Roundtable #270 TL08: What's for Breakfast? How about Empiricism? — Robert Carver, Brandeis
8:30-10:20 #287 CC-West 210  Simulation-Based Inference. 
        9:35  Results on the Progression and Retention of Student Learning Using Simulation-Based Inference — Nathan Tintle, Dordt
        9:55  Simulation-inference in conceptual 2nd course. Karen McGaughey, Cal Poly
10:30 #348  StatEd CC-West 112.
       10:35 Survey of Motivational Attitudes Toward Statistics — Unfried+Coffin Cal State Monterey; Kerby Winona State
       11:50 Concept Maps, Feedback, and Statistics Learning: — Terry Hickey, St. Martin's University
       12:05 Statistics Education Across University: Systematic Review — Aimee Schwab-McCoy, Creighton
10:55 #325 Bayes CC-West 110 Uncertainty in Design Stage Observational Studies. Matthew Cefalu, RAND; Corwin Zigler, Harvard
12:05 #532 CC-West 223 Can Statistics Inform Social Decisions?    12:05 Can Data Beat Anecdotes? Joseph Van Matre  School of Business.  U.A.B.
12:30 Birds of feather. Stat Ed Booth.  Teaching diverse student populations. Brianna Heggeseth: Macalester.
2:00 StatEd CC-West 206/207
      2:05 Inference in Three Hours, and More Time for the Good Stuff — Allen Downey, Olin College of Engineering
      2:25 Multivariable Thinking with Data Visualization — Kari Lock Morgan, Pennsylvania State U
      2:45 Multivariate thinking, intro stats & observational data — Horton+Seto, Amherst; Anoke, Harvard
      3:05 Intro Stats and Intro Data Science: Do we need both? — Mine Cetinkaya-Rundel, Duke University
      3:25 Discussant: Jeff Witmer, Oberlin College
2:00-3:50 #211 CC-West 405 Effectively Explaining Statistical Concepts to Researchers from other Fields —
               Panelists: Natalie Blades, Beth Chance, Paul Roback, Heather Smith, Kim Love, K. R. Love.
6:00-6:30 New Fellow Rehearsal+Group Picture CC-West Ballroom BC 6:30-7:30 New Fellow's Reception: CC-W. Ballroom D. Normandie Lounge
8:00–9:30 ASA Presidential Address and Founders & Fellows Recognition. Lisa LaVange, U. North Carolina. 9:30-12 Dance Party
WEDNESDAY ========================
8:30-10:20 #205 CC-West 205 Large-Enrollment Statistics — Topic Contributed Papers
      8:35 Effective Pedagogy in Large-Enrollment Statistics Courses — Matthew D Beckman, Penn State U
      8:55 Large-scale interactives for large-enrolment courses — Anna Fergusson, Univ. Auckland
      9:35 Statistical Thinking: Fostering a Student-Active Learning in a Large Class — Catherine Case
      9:55 Discussant: Chris Wild, University of Auckland
8:30-10:20 #483 CC-West 114
      8:50 Moderate Effect Modification in Observational Studies. Kwonsang Lee, Harvard; Dylan Small and Paul Rosenbaum, U. Penn
      9:50 When Confounders Are Confounded — Carlos Leonardo Kulnig Cinelli, UCLA ; Judea Pearl, UCLA ; Bryant Chen, IBM
      11:50 Discovering Effect Modification in Observational Studies. Small, Hsu, Rosenbaum, Lee, Zubizarreta and Silber
10:30 - 12:20 CC-West 217 Fresh Approaches to Statistical Pedagogy — Contributed Papers
      11:20 Early Intro of Hypothesis Tests in IntroStats — Wei Wei, Metro State; Heidi Hulsizer, Benedictine College; Aminul Huq, U Mn
      11:35 STEM Storytellers: Improving Graduate Students' Oral Communication Skills — Jennifer L Green, Shannon Willoughby,
              Brock LaMeres,  Bryce Hughes, Leila Sterman, Christopher Organ, Montana State U.
10:30 - 12:20 532 - Can Statistics Inform Decisions in Social, Economic, and Political Event?
12:30 Lunch Roundtable: WL10 When Do We Really Need Randomized Clinical Trials? Christopher Hane, OptumLabs.
                                  WL22 Visualizing Uncertainty for the General Public.  Edward Mulrow, NORC at the University of Chicago
2:05 #576 Biopharm CC-West 214. Translate Real World Data to Robust Evidence for Decision Making.
               Hongwei Wang, Weili He, Yabing Mai, Meijing Wu, AbbVie; Dajun Tian, Chiltern
2:00-3:50 CC-West 212 Innovations in Teaching Undergraduate Probability — Invited Papers
      2:05 Teaching Probability via Stories and Mistakes — Joseph Blitzstein, Harvard University
2:00-3:50 #579 CC-East 10 Panel: Building Bridges with Industry and Business for Statistical Programs — Topic Contributed Panel
       Panelists: Mark Grindeland, Coda Signature; Sudipta Dasmohapatra, Duke University; Mark Morreale, SAS; Bill Thomas, Raytheon
THURSDAY  =============================
8:30-10:20 CC-West 210 GAISEing into Introductory Service Courses in Light of Analytics/Data Science —
        Topic Contributed Panel. Amy Phelps, Beverly Wood, Mark Eakin, Mia Stephens and George Recck

2018 July 8-13 ICOTS-10 Kyoto, Japan      Submissions   Deadlines 

Sun 9:30 - 16:30  Workshop Social "Civic" Statistics by Iddo Gal 


Day 11:00 14:00 16:00
Monday 3C, 4B 1G, 3I, 7B 3H
Tuesday     3E
Wednesday   Open Open
Thursday 1F 1D, 7C 3G, 4J, 8C
Friday 3F, 7A 1C, 1E  

ICOTS Topic 1: Statistics education: Looking back; looking forward. 
Session 1A: Panel: Chris Wild (NZ): “Revolution of statistical education: past, current, and future” (10 min including Q&A)
Session 1C: Statistics Education: What, how and with whom?  [Fri 14:00]

 Session 1D: Out of the past and into the future: a global perspective [Thurs 14:00]

  • 1D2:The challenges of teaching statistics to undergraduate business and economics students In Spain. Luis F. Rivera-Galicia (Spain)

 Session 1E: Assessment: its lessons and effects [Fri 14:00]

  • 1E1: Improving student learning+instructional effectiveness through...automated analysis of formative assessments. A. Lyford, J. Kaplan (US)

  • 1E2: Real-world contexts in statistics components of UK maths exams: aiming forward, walking backwards. J. Nicholson, J. Ridgway (UK)

  • 1E3: Looking for the development of statistical literacy, reasoning and thinking. Ana Gómez-Blancarte, Alberto Santana (México)

Session 1F: Statistics as a Liberal Art and the Real World [Thurs 11:00]

  • 1F1: Rethinking the statistics curriculum: Holistic, purposeful and layered Katie Makar (Australia)

  • 1F2: Statistics IS a liberal arts major K. Scott Alberts, Hyun-Joo Kim, Jillian Downey (US)

Session 1G: Backwards and forwards with research [Mon 14:00]

  • 1G1: The nature and use of theories in statistics education – looking back, looking forward. Per Nilsson (Sweden), Maike Schindler (Germany)

  • 1G2: Storytelling and Teaching Statistics Carl Sherwood (Australia)


Topic 3: Statistics education at the post-secondary level Session

Session 3C: Modern data and visualizations in the introductory statistics course   [Mon 11:00]

  • 3C3: Students’ Understanding of Data Visualizations by Charlotte Bolch and Tim Jacobbe (U. Florida, US)

Session 3E: Students’ negative attitudes towards statistics: an arduous challenge [Tues 16:00]

  • 3E1: Attitudes towards Research as a source for negative Statistics Attitudes Florian Berens (Germany)

  • 3E2: Attitudes towards Statistics in Biology freshmen: an exploratory survey Jorge Navarro-Alberto, Roberto Barrientos-Medina (Mexico)

  • 3E3: The views of undergraduate students about their introductory statistics course process Zeynep Medine Özmen and Adnan Baki (Turkey)

Session 3F: Statistical computing and communication  [Fri 11:00]

  • 3F3: Statistics as rhetoric: why a statistics education must incorporate communication skills by Brad Quiring (Mount Royal University, Canada)

Session 3G: Developing understanding of statistical concepts  [Thurs 16:00]

  • 3G3: Enhancing civic statistical knowledge of secondary pre-service math teachers. Susanne Podworny, D. Frischemeier, R. Biehler (Germany)

Session 3H: New approaches to teaching statistic [Mon 16:00]

  • 3H1: Developing students’ causal understanding of sampling variability. Ethan Brown and Robert delMas (U. Minn, US) [Swamping + heaping]

  • 3H2: Learning through Induced Errors: A Garden-path Approach to Introductory Statistics by John Blake (U. of Aizu, Japan)

  • 3H3: Problem-driven approach for teaching statistics at the African Institute for Mathematical Sciences by Emanuele Giorgi (Lancaster U., UK)

Session 3I: An experience in designing statistics courses for higher education in challenging environments (panel): [Mon 14:00]

  • 3I1 Giovanna De Giusti () and David Stern (UK).


Topic 4: Improving teaching and capacity in statistics education

Session 4B: An Inquiry Teaching Environment for Data Producers [Mon 11:00]

  • 4B2: Data representations in STEM context: Catapult performance. Noleine Fitzallen, Bruce Duncan, J. Watson and Suzie Wright (Tasmania)

Session 4J: Innovative projects for statistical education [Thurs 16:00]

  • 4J2: Students' attitudes change and performance improvement in a flip class. By Aklilu Zeleke (Mich State US) and Carl Lee (Central Mich US)


Session 7A: Promoting understanding of civic statistics: Linking conceptual frameworks, datasets, visualizations, and resources [Fri 11:00]

  • 7A1: Understanding statistics about society: A framework of knowledge and skills needed to engage with Civic Statistics: Rosie Ridgway (University of Durham, United Kingdom) Iddo Gal (Univesity of Haifa, Israel) James Nicholson (University of Durham, United Kingdom)

  • 7A2: Developing Official Statistics Literacy: A proposed model and implications. Iddo Gal (Israel) and Irena Ograjenšek (Slovenia)

  • 7A3: The StatsMap - Mapping Datasets, Viz tools, Statistical Concepts and Social Themes. Pedro Campos (University of Porto, Portugal) James Nicholson (University of Durham, United Kingdom) Jim Ridgway (Durham University, United Kingdom) Paula Lopes (University of Porto, Portugal) Sonia Teixeira (University of Porto, Portugal)

Session 7B: ISLP past and now [Mon 14:00]

  • 7B1: How to collaborate with the media to enhance statistical literacy of the general public Pim Bellinga and Thijs Gillebaart (Netherlands)

  • 7B2: History of the Statistical Graphs Role in Statistical Literacy Developments. Kazunori Yamaguchi and Michiko Watanabe (Japan)

  • 7B3: ISLP past and now Reija Helenius (Finland)

Session 7C: Promoting statistical literacy with visualisation.   Organizer: Andreas Eichler (Germany) : Session chair [Thurs 14:00]

  • 7C1: Visualizing statistical information with unit squares. Katharina Böcherer-Linder, Andreas Eichler and Markus Vogel (Germany)
  • 7C2: T(h)ree steps to improve Bayesian reasoning Karin Binder, Georg Bruckmaier, Jörg Marienhagen and Stefan Krauss (Germany)
  • 7C3: Vocational training students’ reading levels of statistical graphs. Pedro Arteaga, C. Batanero, J, M. Vigo and J. M. Contreras (España)

Session 8C: Reasoning.   Organizer and Session Chair: Lucía Zapata-Cardona (Colombia) [Thurs 16:00]

  • 8C1: Using Toulmin model of argumentation to validate students' inferential reasoning. María G. Tobías-Lara, Ana Gómez-Blancarte (México)

  • 8C2: Assessing statistical literacy and statistical reasoning Anelise Sabbag (Brazil), Andrew Zieffler (US) and Joan Garfield (US)

  • 8C3: Students’ understanding of relationship between study design and conclusions in intro statistics. Elizabeth Fry (U. of Minnesota, US)



  • C113: Enhancing Statistical Literacy through Real World Examples: A Collaborative Study. Sashi Sharma (New Zealand)

  • C169: Finding meaning in a multivariable world: A conceptual approach to an algebra-based second course in statistics. Karen McGaughey, Beth Chance, Nathan Tintle, Soma Roy, Todd Swanson and Jill VanderStoep (US).

  • C213: Overcoming challenges with service courses in Statistics. Matina Rassias (UK)

  • C285: Prospective teachers’ critical thinking regarding statistical and probabilistic information in a newspaper article on medical research. Mehtap Kus and Erdinc Cakiroglu (Turkey)

2018 May 21-25 eCOTS 2018:  Top 8 Sessions    All times are EDT. 
5/21 Mon 11-12:45  Activities to Clarify the Meanings of Key Words Used in Statistics Neal Rogness, Grand Valley State U. and Jennifer Kaplan (U. Georgia)
5/23 Wed 11-12:45  Multivariable thinking in algebra-based second courses Beth Chance (Cal Poly), Karen McGaughey (Cal Poly), Nathan Tintle (Dordt)
5/23 Wed 1:00-2:00 Data Science for all!! Sure! But when, where, how, and why? Richard DeVeaux, Williams College
5/23 Wed 2:15-3:00 Data Science and Intro Stat Breakout: With Kari Lock Morgan
5/23 Wed 4:30-5:00 Is the Central Limit Theorem Still Central to the Introductory Course? Discussion: Eric Reyes (Rose-Hulman Institute of Technology)
5/24 Thu 2:15-3:00  Writing About Data: A Cross-Curricular Approach Brianna Kurtz & Sarah Jensen (Crooms Academy of Information Technology)
5/24 Thu 3:00-3:45  The Evolution of Regression Modeling
5/25 Fri 12:30-1:00 What recedes as data science rises?

Tues 11:00    14:00 None   16:00

2017 May 18-20 USCOTS 2017: www.causeweb.org/cause/uscots/uscots17
Thursday - Saturday at the Penn Stater Conference Center Hotel. State College, Pennsylvania

KEYNOTE: Prestatistics: Acceleration and New Hope for Non-STEM Majors With Jay Lehmann (College of San Mateo).  He is Professor of Mathematics at the College of San Mateo, where he has taught for the past 22 years and received the “shiny apple award” for excellence in teaching. He is the author of "A Pathway to Introductory Statistics" (848 pages, $120).

Abstract: Many community college students come ill prepared for college work. In fact, only about 20% of students progress through the two-course algebra sequence in one try to reach statistics. A small but growing number of community colleges have created a prestatistics course, which is an accelerated path for non-STEM students. By removing an exit point and preparing students solely for statistics, there is great potential for success. Instead of focusing on computations, my department emphasizes concepts, interpretations, and portions of descriptive statistics that students typically find challenging. We will discuss how to design and teach such a course as well as how to avoid potential problems.

WORKSHOPS (Monday - Thursday):

  • Wed W07: Challenging Introductory Statistics Students with Collaborative Data Visualization. Lynette Hudiburgh & Lisa Werwinski (Miami U)
  • Wed W15: Real world data and real world questions in the introductory statistics curriculum with Lisa Dierker (Wesleyan University)
  • Thurs W12: Critical Thinking with Data Visualization With Leanna House (Virginia Tech)
  • Thurs W14: Adapting and Adopting High Impact, Little Time (HILT) Activities to Clarify the Meanings of Key Words Used in Statistics With Neal Rogness, Jackson Fox, Lori Hahn (Grand Valley State University); and Jennifer Kaplan (University of Georgia)

BREAKOUT SESSIONS (Friday and Saturday only):

  • Fri  1:00 1C: Implementing the 2016 GAISE Recommendations.  Mocko, Carver, Gabrosek, Witmer and Wood
  • Fri  3:00 2C: Multiple Variables and Data Visualization in Intro Stat With Kari Lock Morgan (Penn State University)
  • Fri  3:00 2D: High Impact, Little Time (HILT) Activities to Clarify the Meanings of Key Words with Rogness, Fox, Hahn and Kaplan.
  • Fri  3:00 2F: Critical Thinking with Data Visualization With Leanna House (Virginia Tech)
  • Fri  3:00 2G: Why Statistics is not Data Science.  Chris Malone (Winona State U)
  • Sat 11:00 3C: Multiple Variables and Data Visualization in Intro Stat With Kari Lock Morgan (Penn State University)
  • Sat 11:00 3H: Show me the Business Statistics Data with Deborah Rumsey (Ohio State U) and Camille Fairbourn (Utah State U)
  • Sat  1:30 4A: Deepening Conceptual Understanding: Mini-Essays to the Rescue! by Jay Lehmann (College of San Mateo)
  • Sat  1:30 4C: Implementing GAISE 2016 Recommendations by Mocko, Carver, Gabrosek, Witmer and Wood.
  • Sat  1:30 4H: Helping English Language Learners Navigate Probability Vocabulary & Concepts. A. Wagler & L. Lesser (U. Texas El Paso) Slides

2017 Nov 24-26  National Numeracy Network (NNN) Annual Conference Barnard College, New York City

2017 Oct 11-13 ASA Symposium on Statistical Inference: Scientific Method for the 21st Century: A World Beyond p < 0.05.   Bethesda, MD. This symposium follows up on the historic ASA Statement on p-Values and Statistical Significance. This symposium will focus attention on the “Do’s.”  Discussions will center on specific approaches for improving statistical practice as it intersects with three broad components of research activities: (1) Conducting research in the 21st century (2) Using research in the 21st century (3) Sponsoring, disseminating, and replicating research in the 21st century.  The symposium will drive change that leads to lasting improvements in statistical research, communicating and understanding uncertainty, and decision making. Details

2017 July 30-Aug 4 ASA JSM Baltimore:   Program.   Selected sessions.  Links are to abstracts.



7/30  2:00 PM       27 - Professional Development for Statistics Teachers.  Panel: Lee, Halvorsen, Mojica, Weber, Mutlu, Posner
SUN   4:00 PM       80 - Education Topics for Specialized Audiences


7/31    8:30 AM   116 - Essential Connections between Industry & Statistics Education.  Panel:  Carver, Levine, Stephens, Tony and Anderson.
MON  10:30 AM   173.  Bayes for Beginners: Witmer.  Statistics Educator Interviews: Rossman.  Logistic Regression: Schield Slides  PPT

7/31  12:30 PM   195 - StatEd Roundtable (Fee)  ML24 Most Common Terms in Statistics from the Last 20 Years? — John McKenzie

MON    2:00 PM   213 - Training Statisticians to Be Effective Instructors.  Panel: Short, Kaplan, Buchannan, Stephenson and Loy.


8/1     7:00 AM    259 - StatEd Roundtable Discussion (Added Fee).  TL04: Why Do Students Hate Statistics? — Michael DeDonno

TUE    8:30 AM    266 - Novel Approaches to First Statistics / Data Science Course
8/01 10:30 AM    334 - Speed 11:15 McKenzie. 11:45 P-Value as Strength of Evidence. S. Liu.  11:50 Confusion About Independence — Molnar
TUE  12:30 PM    369 - StatEd Roundtable Discussion (Added Fee).  A Course in Business Analytics — David Levine
8/01   3:05 PM    424 - Posters  9: McKenzie.    14: P-Value as Strength of Evidence. S. Liu.  15: Confusion About Independence — Molnar

8/2     8:30 AM   440 - Causal Inference as Essential.   8:35 AM Causal Inference — Balzar.   9:05 AM Teach Causality Before Statistics? — Elwert

WED 10:30 AM   480 - Modernizing the Undergraduate Statistics Curriculum   

8/2     2:00 PM   575 - Modernizing the Statistics Curriculum for Non-Statistics Majors  Panel: DeVeaux, Stine, James, Cochran, Keeling.


8/3   10:30 AM   656 - Introducing Bayesian Statistics at Courses of Various Levels

2017 July 20-21  Virtual Conference on Data Literacy, Univ. Michigan.
Themes: 1.Big Data, including citizen science 2.Ethical data use 3.Personal data management

2017 July 11-14  IASE Satellite Conference, Rabat Morocco
Theme: Teaching Statistics in a Data Rich World.  Within the overall theme, we will focus on these sub-topics: Topic 1. Big data era, what does it mean for us statistics educators? Topic 2: Creating socially responsible societies with statistics, Topic 3: Statistics for social scientists, researchers and workers, Topic 4: Employability skills for statistics graduates, Topic 5: Special Session on Statistics Education in Africa.


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

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