Herb Weisberg


Milo Schield, Editor

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

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

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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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 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



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):
8,172 Responsible Stats...to Shape Public Opinion by Nelson 6up '11 (12)
3,348 Percentage Graphs in USA Today Schield 2006 Total 100,052  (12)
2,674 Two Big Ideas for Teaching Big Data Schield 2014 ECOTS         (9)

2,404 Practical Approach Intro Poli-Sci Statistics Course Klass 2004  (12)
2,231 Framework Interpreting Tables & Graphs  Kemp/Kissane 2010   (12)
2,196 Statistical Literacy Guide.  Bolton, UK  2009                         (11)
2,020 Likert & Visual Analog Scales Tom Knapp 2013                        (8)
1,518 Making Statistics Memorable: New Mnemonics Lesser 2011 JSM (9)

1,367 Presenting Confounding Graphically/Standardization Schield '06  (8)
1,336 Statistical Literacy Curriculum Design    Schield, 2004 IASE       (8)
1,063 Substantive significance of regression coef.  Miller 2008 ASA     (7)
  993 Statistical Literacy in Adult College Students Wade 2009 Thesis (4)
  894 Check Distributional Assumption: Benford’s Law  Goodman 2013  (5)
  869 Interpreting the Cumulative Frequency Distribution Winkler 2009 (6)
  700 Statistical Literacy: New Mission for Data Producers Schield '11 (3)
  653 Statistical Inference for Managers  Schield ASA 2015               (3)

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


















  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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IMS             2017 PROFESSIONAL EVENTS             Statistics

2017 May 18-20 USCOTS 2017: www.causeweb.org/cause/uscots/uscots17
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.


  • 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)


  • 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. Amy Wagler & Larry Lesser (U. Texas El Paso)


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

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