Seven Pillars of Statistical Wisdom by Stephen M. Stigler.
"What gives statistics its unity as a science?
Stephen Stigler sets forth the seven foundational ideas of statistics―a
scientific discipline related to but distinct from mathematics and computer
science." "I will not try to tell you what statistics is -- or
is not. I will attempt to formulate seven principles, seven pillars that
have supported our field in different ways in the past and promise to do so
into the indefinite future." TOC 1. Aggregation: From Tables and Means
to Least Squares 2. Information: Its Measurement and Rate of Change 3.
Likelihood: Calibration on a Probability Scale 4. Inter-comparison:
Within-Sample Variation as a Standard 5. Regression: Multivariate Analysis,
Bayesian Inference and Causal Inference 6. Design: Experimental Planning and
the Role of Randomization 7. Residual: Scientific Logic, Model Comparison
and Diagnostic Display. "The usefulness of [these] seven basic statistical
ideas: 1. The value of targeted reduction or compression of data.
2. The diminishing value of an increased amount of data. 3. How to put
probability measuring stick to what we do. 4. How to use internal variation
in the data to help in that. 5. How asking questions from different
perspectives can lead to revealingly different answers. 6. The essential
role of the planning of observations. 7. How all these ideas can be used in
exploring and comparing competing explanations in science.
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.
Goldstein "Hecker: Down with Algebra II". 2012 Rebuttals:
Devlin. 2016 Rebuttals:
Inference for Managers. Milo Schield. ASA 2015 JSM
June 2016. Added StatLit.org webpage for
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
The Many Facets of Statistics Education: 175 Years of Common Themes
by Jessica Utts. The American Statistician 69(2) April 2015
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
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:
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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2016 GENERAL INTEREST NEWS
IMS 2016 UPCOMING PROFESSIONAL EVENTS
2016 July 19-22:
Roundtable Conference (Invited).
schedule. Held at the Max
Planck Institute for Human Development (MPIB) in Berlin,
Germany. "About 40-50 folks from around the world and
the host country will work together on a unique theme:
“Promoting understanding of statistics about society“. The
Roundtable aims to advance current knowledge about ways to
improve the understanding of data and statistics related to
key social phenomena."
Theme & papers
proposal (Accepted). Schield:
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
TSG15 (Teaching and learning of Statistics),
(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
IASE Presidential Session, IPS104 (Sat. July 30,
09:00 to 10:10) will feature two distinguished colleagues,
Jim Ridgway (Durham, UK) and Joachim Engel (Ludwigsburg,
Germany) who will jointly speak about: "Implications of a
data-rich, multivariate world: New needs and directions in
Lunch Roundtable Discussion: "What do we do about the 'literacy'
in statistical literacy, at the college level?"
(LRTD-09; leader: Iddo Gal, Thurs. July 28, 12:30 to
Wed. July 27:
*IPS021* - Statistical Literacy for Decision Makers,
Room 207, Jul 27
*IPS043* - Which is the right approach to learn statistics?
Cues from different educational contexts, Room 202C, Jul 27,
*IPS079* - Statistical Methods in Computerized Adaptive
Testing, Room 201AB, Jul 27
*IPS039* - Brazilian Statistical Association (ABE):
Contributions to Education and Dissemination of Statistics
in Brazil, Room 207, Jul 27
*IPS138* - The LISA 2020 Program to Build Statistical
Capacity and Research Infrastructure in Developing
Countries, Room 201C, Jul 27
*STS053* - Challenges and obstacles in improving
statistical literacy, Room 204C, Jul 27
*STS074* - Apps and e-learning resources for training in
Official Statistics, Room 204A, Jul 27
*IPS041* - Open Data, Civil Society and Monitoring Social
Progress: Challenges for Statistics Education, Room 201C,
*STS056* - Big Data Analytics - experiences and
perspective on education and talent training, Room 204B, Jul
*IPS042* - Quantitative practices that might arise with the
use of new technological capabilities for exploring data,
Room 205, Jul 30
*IPS076* - Multilevel modeling in
evaluation and large-scale assessments, Room 210, Jul 31
*IPS040* - Ethics in teaching and practicing statistics:
Learning from real-life ethical dilemmas, Room 210, Jul 31
2016 Aug 1-4
selection of StatEd papers.
#129 Monday 8:30 - 10:20 Advancing Statistical Literacy —
* 8:35 Top 5 Reasons You Can't Blame. Students for Not
Getting Inference — Chris Malone, Winona State U.
* 8:50 Twelve Big Ideas for Introductory Statistics — Milo
Schield, Augsburg College
* 9:05 Students' Ratings of the Utility of Key Concepts in
Intro Statistics — Rossi A Hassad, Mercy College
* 9:20 Effect size really does matter — Jeffrey Witmer,
* 9:35 Emphasizing critical thinking in introductory
statistics — Roger Woodard, North Carolina State University
* 9:50 Students' Understanding of Expected Value — Hyung Kim
; Tim Fukawa-Connelly, Temple University
*10:05 Importance of retention: Preparing students for
workforce — Michael Posner & Meghan Buckley, Villanova U.
2016 May 16-20: ECOTS
Access to sessions/papers.
classification of sessions and posters.
Call for proposals #1
Submit by Feb 1. "Proposals should be related to the
conference theme of "Changing with Technology". Advances in
technology provide both new opportunities and new demands
when it comes to statistics education. Technology is
inspiring change in many different aspects of statistics
education: what we teach (curriculum), how we teach
(pedagogy), who we teach (audience), and why we teach
(goals). Proposals can address technology-induced change in
any of these areas. All proposals are due by February 1,
2016. For more details about eCOTS 2016 and to
submit a proposal, please visit www.causeweb.org/ecots/ecots16/.
If you have any questions, please contact Kari Lock Morgan
at email@example.com. Thanks!"