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."
The Book of Why: A New Science of Cause and Effect.
Summary and TOC.
"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 March 4:
Analytics and Big Data Summit.
Marc Isaacson and Milo Schield (Quant-Fluent)
three-hour workshop on Data Literacy and Statistical Literacy
2017 June 21
Issue: Statistical Literacy
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
the Classroom to the Workplace: How Social Science Students Are Learning
to do Data Analysis for Real by Jackie Carter, Mark Brown, and
Stories, Landing Planes and Getting Them Moving – A Holistic Approach to
Developing Students' Statistical Literacy by Julie Scott Jones and
John E. Goldring
Opening Real Science: Evaluation of an Online Module on Statistical
Literacy for Pre-Service Primary Teachers by Ayse Aysin Bombaci
Bilgin, Elizabeth Date-Huxtable, Carmel Coady, Vincent Geiger, Michael
Cavanagh, Joanne Mulligan, and Peter Petocz
Developing Statistical Literacy in the Final School Year by
Stephanie Budgett and Drusilla Rose
Interpretation of Statistical Data: The Importance of Affective
Expressions by Tamires Queiroz, Carlos Monteiro, Liliane Carvalho,
and Karen François
The Development of Statistical Literacy at School by Rosemary
Callingham and Jane M. Watson
Success Factors for Statistical Literacy Poster Competitions by
Steve MacFeely, Pedro Campos, and Reija Helenius.
Statistical Literacy in Public Debate – Examples from the UK 2015
General Election by Phoebe Arnold
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
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
2016 Mission and Objectives.
2017 April 29: Schield invited to talk on
Statistical Literacy in Toronto at the
Institute Math-Ed forum.
Statistical Literacy: What is it... Who needs it... What is
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
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:
"Hecker: Down with Algebra II". 2012 Rebuttals:
Devlin. 2016 Rebuttals:
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!
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
"I hope that...statistical literacy
will...rise to the top of your advocacy list" Ruth Carver,
29% of US Freshman took stats in high school
(15% took AP Stats), so 14% took non-AP Stats.
2012 Am. Freshman
(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
2014 10: Highest Monthly Downloads: October had
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 @
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"
Development Dictionary (move slider to "s") [link broken/missing in
Statistical literacy: "understanding
the basic language of statistics (e.g., knowing what statistical terms and
symbols mean and being able to read statistical graphs), and understanding
some fundamental ideas of statistics."
GAISE College Report
Search StatLit Site
Yearly highlights of grants, new books, conference papers (ICOTS, ISI,
JSM, JMM), and events involving statistical literacy.
Newest StatLit.org web pages:
THREE MOST IMPORTANT
STATISTICAL LITERACY ARTICLES SINCE 2002
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,
ARTICLES/SLIDES POSTED in
2015 ARTICLES POSTED TO
STATLIT.ORG (by Month)
Challenge Statistical Claims in Media, Martinez-Dawson ASA 2013
2015 SLIDES and WORKSHEETS HOSTED
07 2013 MSMESB: MS Business Analytics program.
ARTICLES/SLIDES POSTED in
2014 ARTICLES POSTED TO
STATLIT.ORG (by Month)
AMSTAT: Causality in Statistics Education Award 2013.
stat analysis not done by statisticians Simply Statistics 2013
Simpson's Paradox in Psychological Science by Kievit et al. 2013.
Statistical Literacy Explained by Hewson, Teaching Statistics, 2013
in a Math-Literate World by Orlin, Huffington Post, 2013
Statistical Literacy Campaign and Initiatives. 2014
SIGMAA-QL 2013 Newsletter. Bennet: Writing for general
Call for Statistical
Literacy papers. 2014 Stat-Ed Research Jrnl.
Cutoffs for Statistical Significance. Schield 2014
SRTL-9 Proposal: Informal Doorways to Modeling. Schield 2014
and Uses of Convenience Samples Kriska et al. ASA 2013
Seeing how Statistical Significance is Contextual. Schield 2003.
Simpson's Paradox #30 Classic Problems in Probability. Gorroochurn
Simon Schild Maps: Bellenberg Germany & Benton County IA. 2014
journey from Bellenberg Germany to America. 2002
2013 MSMESB/DSI Annual Report
by Robert Andrews
Odyssey: Lifelong Statistical Literacy Schield 2014 ICOTS
Two Big Ideas for Teaching Big Data Schield ECOTS 2014
Teaching Big Data at
Georgetown. Sigman et al. Decision Line 2014
Proposal: Summary AACU Schield 2014
Augsburg's NSF Proposal:
Summary. Schield 2014
Visualization of Economic Indicators.
Thompson+Wallace. ASA 2013.
Fusion & causal analysis in big marketing data. Mandel ASA 2013
Distributional Assumption: Benford’s Law. Goodman ASA 2013
Challenge Statistical Claims in Media, Martinez-Dawson ASA 2013
2014 SLIDES and WORKSHEETS HOSTED
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
10 Creating Distributions Empirically. M.
Schield. NNN3 Workshop
10 Statistically-Significant Correlations. Milo
Schield. NNN4 2014
10 Segmented Linear Regression. Schield. NNN5
08 Top 30 Learning Goals for
Introductory Sociology. Persell 2010
08 Social Science Reasoning & QL Learning Goals Caulfield+Persell'06List
07 2013 MSMESB: Predictive Analytics
course. Levine et al.
07 2013 MSMESB: Spreadsheet Analytics. James R.
07 2013 MSMESB: Implications of Big Data for Stat
Ed. Berenson slides
07 2013 MSMESB: Big Data & Statistics
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
07 2013 MSMESB: MS Business Analytics program.
TOP DOWNLOADS: 2017
Top 20 Downloads of Papers
(# months stats tabulated)
to Lies: Information Age (TOC+Intro) Levitin 2016 (10)
Interpreting Cumulative Frequency
Distribution Winkler 2009 (11)
Substantive significance of
regression coef. Miller 2008 ASA (12)
Visual Analog Scales Tom Knapp 2013
Common Statistical Fallacies Social
Indicator Data Klass 2008 (12)
2,516 Framework Interpreting
Tables & Graphs Kemp/Kissane 2010 (12)
Literacy Guide. Bolton, UK 2009
2,377 Unpublished Quantitative Research
Methods Book Knapp 2016 ( 8)
Graphs in USA Today Schield 2006 Total 100,052 (12)
1,525 Making Statistics Memorable: New
Mnemonics. Lesser 2011 JSM ( 8)
Practical Approach Intro Poli-Sci
Statistics Course Klass 2004 ( 8)
1,301 Statistical Literacy: Thinking
Critically about Stats Schield 1999 ( 9)
Learning Statistics through Playing Cards. Knapp 2012
1,183 To Pool or Not
to Pool. Knapp 2013
Presenting Confounding Graphically/Standardization Schield '06
1,057 Three Paradoxes in
Interpreting Group Differences Wainer 2004 ( 5)
Two Big Ideas for Teaching Big Data
Schield 2014 ECOTS ( 6)
873 Numeracy: New
Literacy for Data-Drenched Society Steen 1999 ( 6)
Literacy Curriculum Design Schield, 2004 IASE (
Top Downloads of Excel-Related Slides (All by Schield)1. 13,167 Create lognormal in Excel 2013.
Model using Linear Trendline 2Y1X Excel
3. 4,989 T-Test command with Excel
histograms using functions w Excel 2013
5. 1,930 Create Pivot Tables using Excel 2008
Model using Linear Trendline Excel 2013
Graph nominal data w Excel 2013
8. 1,367 Using the Z-test via functions in Excel
952 Model Logistic
Regression MLE using Excel 2013.
10 802 Model Toolpak Regress linear 3
factor 1Y2X Excel 2013.
11 762 Model Logistic Regression OLS1C
POPULAR STATLIT AUTHORS IN 2017
ACADEMIC STATLIT AUTHORS IN 2017
MOST POPULAR STATLIT PAGES in 2017
Index to StatLit.org website [This page]
Howard Wainer author page
Quantitative Literacy/Reasoning Textbooks as of
Statistical Literacy Articles by Year posted
StatLit News: 2009 News of the Year
StatLit News: 2013 News of the Year
Joel Best author page
techniques by Schield
StatLit News: 2012 News of the Year
StatLit News: 2011 News of the Year
StatLit Tools Excel-based tools for analyzing
Gerald Bracey author page
StatLit News: 2008 News of the Year
Gerd Gigerenzer author page
Adult Numeracy page
StatLit News: 2014 News of the
StatLit News: 2007 News of the
1,179 Blastland author page
1,110 StatLit News: 2006
News of the Year
Milo Schield author page with related items
Note: the leading number is the number of
page reads for each page
OTHER RECOMMENDED INTRO BOOKS
Victor Cohn (1989),
News and Numbers
How To Lie with Statistics
Edward Tufte (1995),
presenting a general background or overview.
Fifteen articles involving the W. M. Keck Statistical Literacy Project:
Sense of Statistics by Nigel Hawkes and Leonor Sierra. Section
1: If a statistic is the answer, what was the question?
Section 2: Common pitfalls. Section 3: How sure are we?
Section 4: Percentages and risk; knowing the absolute and relative
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2018 GENERAL INTEREST NEWS
2018 July 29-Aug 3 ASA
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.
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
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
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
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
8:30-10:20 #205 CC-West 205 Large-Enrollment Statistics — Topic
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
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
10:30 - 12:20 CC-West 217 Fresh Approaches to Statistical Pedagogy —
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,
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,
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,
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
Sun 9:30 - 16:30 Workshop
Statistics by Iddo Gal
SESSIONS OF INTEREST BY DAY:
||1G, 3I, 7B
||3G, 4J, 8C
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
Session 1C: Statistics Education: What, how and with whom?
1D: Out of the past and into the future: a
global perspective [Thurs 14:00]
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
1F2: Statistics IS a
liberal arts major K. Scott Alberts, Hyun-Joo Kim, Jillian Downey
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
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
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]
Session 3G: Developing
understanding of statistical concepts [Thurs 16:00]
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)
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]
Topic 4: Improving teaching
and capacity in statistics education
Session 4B: An Inquiry
Teaching Environment for Data Producers [Mon 11:00]
Session 4J: Innovative
projects for statistical education [Thurs 16:00]
Session 7A: Promoting
understanding of civic statistics: Linking conceptual frameworks,
datasets, visualizations, and resources [Fri 11:00]
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
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
- 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
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)
statistical literacy and statistical reasoning Anelise Sabbag
(Brazil), Andrew Zieffler (US) and Joan Garfield (US)
understanding of relationship between study design and conclusions
in intro statistics. Elizabeth Fry (U. of Minnesota, US)
Enhancing Statistical Literacy through Real World Examples: A
Collaborative Study. Sashi Sharma (New Zealand)
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
Overcoming challenges with service courses in Statistics. Matina
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
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
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
2017 May 18-20
Saturday at the Penn Stater Conference Center Hotel. State College,
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,
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
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
- 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)
(Friday and Saturday only):
- Fri 1:00 1C: Implementing the 2016 GAISE
Recommendations. Mocko, Carver, Gabrosek, Witmer
- Fri 3:00 2C: Multiple Variables and Data
Visualization in Intro Stat With Kari Lock Morgan (Penn
- 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
- 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
- Sat 1:30 4H: Helping English Language Learners
Navigate Probability Vocabulary & Concepts. A. Wagler
& L. Lesser (U. Texas El Paso)
2017 Nov 24-26 National
Numeracy Network (NNN) Annual Conference
Barnard College, New York City
2017 Oct 11-13
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.
2017 July 30-Aug 4 ASA JSM Baltimore:
Program. Selected sessions. Links are to
7/30 2:00 PM
Professional Development for
Statistics Teachers. Panel: Lee, Halvorsen,
Mojica, Weber, Mutlu, Posner
Education Topics for Specialized Audiences
Essential Connections between Industry & Statistics Education. Panel: Carver,
Levine, Stephens, Tony and Anderson.
173. Bayes for Beginners: Witmer.
Logistic Regression: Schield
7/31 12:30 PM
195 - StatEd Roundtable (Fee)
ML24 Most Common Terms in
Statistics from the Last 20 Years? — John McKenzie
213 - Training Statisticians to Be Effective
Instructors. Panel: Short, Kaplan, Buchannan, Stephenson
259 - StatEd Roundtable Discussion (Added Fee).
TL04: Why Do
Students Hate Statistics? — Michael DeDonno
266 - Novel Approaches to First
Statistics / Data Science Course
8/01 10:30 AM 334 - Speed 11:15
P-Value as Strength of Evidence. S. Liu.
Independence — Molnar
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
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
575 - Modernizing the Statistics Curriculum
for Non-Statistics Majors Panel: DeVeaux, Stine, James, Cochran, Keeling.
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
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.