Technical News in 2017

Nov 24-26:

National Numeracy Network (NNN) Annual Conference Barnard College, New York City

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

Jul 30-Aug 4:

ASA JSM Baltimore  Program  Selected sessions  Links are to abstracts

Date	Time	Subject
7/30 Sun	2:00 PM	27 - Professional Development for Statistics Teachers   Panel: Lee, Halvorsen, Mojica, Weber, Mutlu, Posner
	4:00 PM	80 - Education Topics for Specialized Audiences
7/31 Mon	8:30 AM	116 - Essential Connections between Industry & Statistics Education   Panel: Carver, Levine, Stephens, Tony and Anderson
	10:30 AM	173 - Bayes for Beginners: Witmer.  Statistics Educator Interviews: Rossman.  Logistic Regression: Schield Slides  PPTX
	12:30 PM	195 - StatEd Roundtable (Fee)  ML24 Most Common Terms in Statistics from the Last 20 Years? — John McKenzie
	2:00 PM	213 - Training Statisticians to Be Effective Instructors   Panel: Short, Kaplan, Buchannan, Stephenson and Loy
8/1 Tue	7:00 AM	259 - StatEd Roundtable Discussion (Added Fee).  TL04: Why Do Students Hate Statistics? — Michael DeDonno
	8:30 AM	266 - Novel Approaches to First Statistics / Data Science Course
	10:30 AM	334 - Speed 11:15 McKenzie ;  11:45 P-Value as Strength of Evidence — S. Liu;  11:50 Confusion About Independence — Molnar
	12:30 PM	369 - StatEd Roundtable Discussion (Added Fee)  A Course in Business Analytics — David Levine
	3:05 PM	424 - Posters  9: McKenzie  14: P-Value as Strength of Evidence — S. Liu  15: Confusion About Independence — Molnar
8/2 Wed	8:30 AM	440 - Causal Inference as Essential   8:35 AM Causal Inference — Balzar  9:05 AM Teach Causality Before Statistics? — Elwert
	10:30 AM	480 - Modernizing the Undergraduate Statistics Curriculum
	2:00 PM	575 - Modernizing the Statistics Curriculum for Non-Statistics Majors   Panel: DeVeaux, Stine, James, Cochran, Keeling
8/3 Thu	10:30 AM	656 - Introducing Bayesian Statistics at Courses of Various Levels

Jul 20-21:

Virtual Conference on Data Literacy, Univ. Michigan

Themes: 1. Big Data, including citizen science;  2. Ethical data use;  3.Personal data management

Jun:

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:

• Statistical Literacy in the Data Science Workplace by Robert Grant
• Data Literacy Is Statistical Literacy by Robert Gould
• Interactive Visualisations and Statistical Literacy by Sinclair Sutherland and Jim Ridgway
• Statistical Literacy as the Earth Moves by Chris J. Wild
• Statistical Literacy in Data Revolution Era: Building Blocks and Instructional Dilemmas by Theodosia Prodromou and Tim Dunne
• Statistical Literacy for Active Citizenship: A Call for Data Science Education by Joachim Engel

• GAISE 2016 Promotes Statistical Literacy by Milo Schield

  “Conclusion: By introducing multivariate thinking and, more importantly, confounding, the GAISE revision committee has accepted — and greatly advanced — statistical literacy in the process. The revised guideline (GAISE, 2016) is the most important institutional paper advancing statistical literacy to date. Including multivariate thinking and confounding in introductory statistics courses and textbooks will be the biggest change in statistical education in decades. These are exciting times for statistical literacy and for statistics education.”

• Against Inferential Statistics: How and Why Current Statistics Teaching Gets It Wrong by Patrick White and Stephen Gorard
• A Response to White and Gorard: Against Inferential Statistics by James Nicholson and Jim Ridgway
• Still against Inferential Statistics: Rejoinder to Nicholson and Ridgway by Stephen Gorard and Patrick White

Research Papers:

• From the Classroom to the Workplace: How Social Science Students Are Learning to do Data Analysis for Real by Jackie Carter, Mark Brown, and Kathryn Simpson
• Telling 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 Litercy 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
• Key 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

Regular Papers:

• Is the P-value Really Dead? Assessing Inference Learning Outcomes for Social Science Students in an Introductory Statistics Course by Sharon Lane-Getaz

 

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

May:

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.”

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.

May 18-20:

USCOTS 2017

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 with 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 with 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. with A. Wagler & L. Lesser (U. Texas El Paso)  Slides

Apr:

Schield invited to talk on Statistical Literacy in Toronto at the Field Institute Math-Ed forum Schedule
Statistical Literacy: What is it... Who needs it... What is stopping it... Slides Audio PPTX