Milo Schield, Editor

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

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 JSM Vancouver    Schield Abstract

2018 July 8-13 ICOTS-10 Kyoto, Japan      Submissions   Deadlines  
ICOTS Topic 1: Statistics education: Looking back; looking forward. 
Session 1C: Statistics Education: What, how and with whom?

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?

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

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.

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 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. Rutgers.  11:50 Definition & Confusion About Independence — R. Molnar

TUE  12:30 PM    369 - StatEd Roundtable Discussion (Added Fee).  A Course in Business Analytics — David Levine

8/01   3:05 PM    424 - Poster session  9: McKenzie.    14: P-Value as Strength of Evidence. S. Liu. Rutgers.  15: Definition and Confusion About Independence — R. Molnar


8/2     8:30 AM   440 - Causal Inference as Essential.   8:35 AM Causal Inference — L. Balzar.   9:05 AM Teaching Causality Before Statistics? — F. 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 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 Nov 24-26  National Numeracy Network (NNN) Annual Conference Barnard College, New York City


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

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