Added StatLit.org webpage for
Call for Grant Ideas.
Submit by July 31, 2015.
The Improbability Principle: Why Coincidences, Miracles, and Rare Events
Happen Every Day by David Hand.
TOC:1 The Mystery; 2 A Capricious Universe; 3. What is Chance? 4 The Law of
Inevitability; 5 The Law of Truly Large Numbers; 6 The Law of Selection; 7
The Law of the Probability Lever; 8 The Law of Near Enough; 9 The Human
Mind; 10 Life, the Universe and Everything; 11 How to Use the Improbability
wrong with THE Introductory Statistics Course. Schield USCOTS
New classroom video:
Statisticians: Making our World a
Better Place. Schield 2015 USCOTS. 4.5 minutes
Statistical Literacy online course for
teachers (no credit): May 11-June 29. Syllabus,
Textbook and Registration
April 29: Congratulations go to Tyler
VanderWeele, winner of the 2015 ASA “Causality in Statistics Education
Award” for his book “Explanation in
Causal Inference” (Oxford, 2015). The award ceremony will take place at the
2015 JSM conference, August 8-13, in Seattle. Another good news, Google has
joined Microsoft in sponsoring next year’s award, so please upgrade your
2016 nominations. For details of nominations and selection criteria, see
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.
Causality in Statistical Education Award. The committee is pleased to announce that a
gift from Microsoft Research will enable the prize to double in 2015. A
$10,000 prize or two $5,000 prizes will awarded this year.
For additional information about the award,
winner and the
Nominations and questions should be sent to the ASA office at firstname.lastname@example.org.
The nomination deadline is February 15, 2015. Visit www.amstat.org/education/causalityprize/
for nomination information.
"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
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MOST IMPORTANT StatEd JOURNAL
ARTICLE SINCE 2002
If you read just one article,
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,
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: 2014
Top 20 Downloads of Papers (or
slides if no paper):
Graphs in USA Today Schield 2006 ASA. Total 100,052
7,043 Responsible Stats...to Shape Public Opinion by A Nelson
Two Big Ideas for Teaching Big Data
Schield 2014 ECOTS [**May]
3,769 Framework for Interpreting
Tables & Graphs Kemp & Kissane 2010
Literacy in Adult College Students B. Wade 2009 Thesis
Quantitative Scholarship: From Literacy to Mastery U.Texas 2009
Statistical Literacy: A New Mission for Data Producers Schield '11
Interpreting the Cumulative Frequency
Distribution Winkler 2009
2,613 Check Distributional
Assumption: “Benford’s Law” Goodman 2013
Visual Analog Scales Tom Knapp 2013
Presenting Confounding Graphically via Standardization Schield '06
Practical Approach to Intro Poli-Sci
Statistics Course Klass 2004
To Pool or Not to
Pool by Tom Knapp 2013
Literacy Guide. Bolton, UK 2009
Statistical Literacy by Haack 1978, Teaching Statistics
Literacy Curriculum Design. Schield, 2004 IASE
The Undetectable Difference: The “Problem” of p-Values.
1,055 Developing a Test of Normality
in the Classroom Jernigan 2012 ASA
Substantive significance of
regression coefficients. Miller 2008 ASA
Students’ Attitudes by
& Schau 2010 ASA.
Top Downloads of Excel-Related Slides (All by Schield)
37,203 Create lognormal in Excel 2013.
2. 7,278 Model Logistic
Regression using Excel 2013.
3. 6,117 Using the Z-test function in Excel
4. 4,008 COUNTIF
histograms: Excel 2013
5. 3,905 Confidence
intervals with Excel 2010
6. 2,577 T-Test command
with Excel 2013
7. 2,475 Trendline 2Y1X with different scales.
1,900 Create Pivot Tables using Excel 2008
Using the T-Test function in Excel 2008.
10 508 Regress
3 Factor using Linear Trendline Excel 2013 6up
Create lognormal distribution Excel 2008.
12 273 Model using Linear Trendline in Excel
POPULAR STATLIT AUTHORS IN 2013
ACADEMIC STATLIT AUTHORS IN 2013
MOST POPULAR STATLIT PAPERS in 2013
Graphs in USA Today. Milo Schield 2006 ASA Proceedings.
Statistical Literacy: Uses & Abuses of Numbers by Andrew Nelson
Presenting Confounding Graphically Using Standardization
by Milo Schield. 2006 STATS magazine.
Literacy: A New Mission for Data Producers by Milo Schield. 2011
Univ. Texas San Antonio: Quantitative Scholarship - Final Draft
Press release 2009
Statistics for Political Science
Majors. Gary Klass 2004 ASA
OTHER RECOMMENDED INTRO BOOKS
Victor Cohn (1989),
News and Numbers
How To Lie with Statistics
Edward Tufte (1995),
presenting a general background or overview.
Thirteen 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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2015 GENERAL INTEREST NEWS
IMS UPCOMING PROFESSIONAL EVENTS
2015 Aug 9-13. Joint
Statistical Meeting in Seattle, WA.
* 3:05 #040 Looking Deeper into
Student's Engagement, Learning Style, and Attitudes by Chauhan* & Zubovic
(Purdue U. Fort Wayne)
* 4:45 #067 Causal Inference in Environmental Science and Agriculture:
Opportunities and Challenges by Molly Davies
* 5:05 #065 Intro Stats in the 21st century by Richard De Veaux*
* 7:00 #095 Roundtable Writing in the Statistics Classroom by Kim
Love-Myers* (Statistical Consulting Center, UGA)
* 8:50 #143 Teaching Study Design Principles vs. Data Analysis by
Tisha Hooks* and April Kerby (Winona State)
* 9:05 #143 What Would Fisher Do? Promoting a Rich Understanding of
Model Construction by Couton & Stroup (U. Nebraska - Lincoln)
* 9:20 #143 Including a History of Statistics Course in your
Curriculum by Phyllis Curtiss* and Kirk Anderson (Grand Valley State)
* 10:35 #185 Teaching Meta-Analysis: Concepts, Controversies, and Resources
by Deborah Dawson (Univ. Iowa)
* 10:50 #185 3 Related Paradoxes by Harry James Norton, Carolinas Medical
Center & George Divine, Henry Ford Hospital
* 11:05 #185 From Measurement Errors to Normal Distributions: History and
Pedagogical Implications by Ilhan Izmirli (GMU)
* 11:05 #186 SBSIG Visual Analytics and the Introductory Statistics
Course: Time for a Paradigm Shift by Benjamin Adams (U. AL)
* 12:05 #182 Graphical causal models: the next multimodel inference
regime change needed in Ecology? Irvine* and Gitelman (Oregon St)
* 2:00 #240 Panel: GAISE in Increasingly Data-Centric World Rob
Carver, John Gabrosek, Megan Mocko, Paul Velleman, Beverly Wood
* 7:00 #276 Roundtable Resampling in the Undergraduate Curriculum by
Tim Hesterberg (Google)
* 10:30 #377 Poster #12: Estimating Causal Effects..in RCTs w. Provider-Subject Noncompliance by Elisa Sheng*
& Xiao-Hua Zhou (U. Wa)
* 10:35 #330 A new criterion for confounder selection
by Tyler VanderWeele* (Harvard) and Ilya Shpitser (U. Southampton)
* 11:50 #362
Graphical Framework for Causal Reasoning: Multivariate,
Multilevel & Longitudinal Settings. Theobald & Richardson (U. Wa)
* 11:20 #366 Reading Assignments for the Statistics Classroom by Scott Mcclintock* and Steve Soltys (Elizabethtown College)
* 11:35 #366 Quantitative Writing: Communicating Data by Kimberly Massaro*
and Gail Pizzola (UTSA)
* 11:50 #366 Children statistical literacy: Empowering & informing future
decision makers by Matilde Sanchez-Pena & J. Main (Purdue)
* 12:05 #366
Statistical Literacy for Managers by Milo Schield (Augsburg)
* 12:30 #389 Roundtable: Innovative Ways for Teaching Large Statistics
Courses by Stacey Hancock (U. Calif. Irvine)
* 2:00 #404 Invited Panel: Statistics Education via Online Courses.
John McGready, James Rosenberger, Simon Sheather & Camille Fairbourn
* 7:00 #456 Roundtable Quantitative Statistics Courses Are Very
Qualitative by Leanna House* and Scotland Leman (Virginia Tech)
* 8:55 #480 Unraveling Bias in Online [Natural] Experimentation by
Chris Harland (Microsoft)
* 9:15 #480 How Credible are Observational Estimates of Causal Effects
from 'Big Data' by Eytan Bakshy* and Dean Eckles (Facebook)
* 9:15 #487 Relationship b/t Verbal Reasoning Skills and
Statistical Literacy... by Elizabeth Johnson*(GMU) & D. Keosayian
* 9:20 #487 what statistically significant relationship looks like
[in scatter plots] by Aaron Fisher*, Anderson, Peng & Leek (John Hopkins)
* 9:30 #487 Reinforcing Experimental Design with Activities by Paul
Stephenson*, Curtiss, Richardson and Reischman (Grand Valley State)
* 9:40 #487 Changing How Students Think About Statistics by Paul
Plummer (U. Central Missouri)
* 9:55 #487 Are Pie Charts Really So Bad? by Michael Posner* and
Joseph Reiter (Villanova)
* 10:05 #490 Causal inference for ordinal outcomes by Alexander Volfovsky*,
Edo Airoldi and Donald Rubin (Harvard)
* 10:50 #545..Evolution of Statistical Terms such as Analytics, Big Data,
and Data Science by John McKenzie, Babson College
* 11:00 #517 ...Delivering Impactful End-to-End Stories to Executive
Audiences by Paul Swiontkowski (Microsoft)
* 3:05 #597 On causal interpretations of race in regressions adjusting
for confounding and mediating variables by Whitney Robinson (UNC)
* 9:05 #654 Peer Assessment in the Statistics Classroom by Dennis
Sun (Stanford & Google)
* 10:35 #691 Bias Amplification: The Case of Fixed-effects by Joel
Middleton*, Marc Scott, Jennifer Hill and Ronli Diakow
2016 July 24-31:
Start planning for the 13th International Congress on
Mathematical Education (ICME13), July 24-31, 2016, Hamburg
Germany. (FYI, just before ICME13, IASE plans a Roundtable
Topic Study Groups: TSG14 (Teaching and learning of
Probability), TSG15 (Teaching and learning of Statistics),
TSG23 (Mathematical Literacy). Also look at TSG6 (Adult
lifelong learning of mathematics) and TSG3 (Mathematics
education in and for work). Deadline for submission of
papers for TSGs = Oct 1, 2015