Stats + Stories

In honor of this year's Joint Statistical Meetings, this week's episode is a repost of a conversation John Bailer and Brain Tarran, of Significance Magazine, had at JSM 2019 about communicating statistical information at a large-scale professional event. John Bailer is “the stats guy” and co-creator of Stats+Stories. He is also a University Distinguished Professor and chair of the Department of Statistics at Miami University in Oxford, Ohio. He is currently President-elect of the International Statistical Institute and previously served on the previously on the ASA Board of Directors. His scholarly interests include the design and analysis of environmental toxicology experiments and occupational health studies, quantitative risk estimation, gerontological data analysis, promoting quantitative literacy, and enhancing connections between statistics and journalism.

Communicating statistics effectively can be a difficult task it can sometimes be hard to know how much information someone needs in order to understand a particular set of numbers. Jargon can be another stumbling block to clearly communicating what a statistical finding means. Communicating stats clearly is the focus of this episode of Stats and Stories with guest Kevin McConway Kevin McConway is Emeritus Professor of Applied Statistics at the Open University in the UK, where he taught statistics, mainly to adult students in a wide range of disciplines. He has researched collaboratively across natural and social science. Kevin has developed a strong interest and involvement in statistics in the media. In particular he was as adviser for eleven years and an occasional contributor to the BBC radio program More or Less, which aims to support the public understanding of numbers in the news. He has worked with and helped train journalists in understanding and communicating statistics, often through the UK’s Science Media Centre where he is a member of the advisory committee. He tweets on @kjm2.

About 20 years ago, most people would have been unfamiliar with the term crowdfunding. Now, when it comes to the arts, you can crowdfund anything from comic books to Mystery Science Theater 3 Thousand to musical compositions. What it takes to successfully crowdfund a rock project is the focus of this episode of Stats and Stories with guests Moinak Bhaduri, Dominique Haughton and Piaomu Liu. Moinak Bhaduri is an Assistant Professor, Mathematical Sciences at Bentley University who studies spatio-temporal Poisson processes and others like the self-exciting Hawkes or log-Gaussian Cox processes that are natural generalizations. His primary interest includes developing change-detection algorithms in systems modeled by these processes, especially through trend permutations. His research has found applications in computer science, finance, reliability and repairable systems, geoscience, and oceanography. Dominique Haughton is a Professor of Mathematical Sciences and Global Studies at Bentley University, and Affiliated Researcher at the Université Paris 1 (Pantheon-Sorbonne). Research interests include applied statistics, business analytics, global analytics, music analytics, data mining, and model selection. Professor Haughton’s work concentrates on how to best leverage modern analytics techniques in order to address questions of business or societal interest. Piaomu Liu is an Assistant Professor, Mathematical Sciences at Bentley University. Her research interests include Lifetime data analysis (recurrent event & competing risks), joint dynamic modeling, and semiparametric methods.

Our lives are framed by numbers tracking our performance in school, our financial health, and our physical and emotional wellbeing. While this information can help us figure out what we might do to improve a situation, it’s only part of the statistical story. There’s other information, other data, that might be useful as well. The importance of linking data is the focus of this episode of Stats and Stories, where we explore the statistics behind the stories and the stories behind the statistics with guest Katie Harron Harron is an Associate Professor in quantitative methods at the UCL Great Ormond Street Institute of Child Health as well as the 2021 Wood Medal recipient for, “her outstanding methodological work on record linkage.” . Her research focuses on the development of statistical methods and synthetic data for data linkage, and particularly for evaluating the quality of linkage. She aims to develop methods to exploit the rich data that are collected about populations as we interact with services throughout our lives. Her work facilitates the wider use of these population-based administrative and electronic data sources for epidemiological research, to support clinical trials, and to inform policy. Harron’s applied research focuses on maximizing the use of existing data sources to improve services for vulnerable mothers and families. Her current research links data from health, education and social care at a national level, in order to improve our understanding of the health of individuals from birth to young adulthood.

Over the course of the last year, statistics have framed our lives in very obvious ways. From COVID cases to unemployment rates, stats have helped us understand what’s happening in the wider world. As we contemplate how to “build back better” in the aftermath of the pandemic, official statistics could help guide our way, at least, that’s what the authors of a recent Significance Magazine article think. That’s the focus of this episode of Stats and Stories with guest Paul Allin. Paul is a visiting professor of statistics in the department of mathematics at Imperial College London, UK. His research interests are the measurement of national wellbeing and progress, and the use of these measures in politics, policy, business, and everyday life. He also chairs the Statistics User Forum, an ‘umbrella’ organization that brings together producers and groups of users of UK official statistics. Paul previously spent forty years as a professional statistician, researcher, and policy analyst in the Office for National Statistics and other departments and agencies, including as the director of the Measuring National Wellbeing program. His social media usage is limited to LinkedIn and StatsUserNet.

For years now, the utility of the P-value in scientific and statistical research has been under scrutiny – the debate shaped by concerns about the seeming over-reliance on p-values to decide what’s worth publishing or what’s worth pursuing. In 2016 the American Statistical Association released a statement on P-values, meant to remind readers that, “The P-values was never intended to be a substitute for scientific reasoning.” The statement also laid out six principles for how to approach P-values thoughtfully. The impact of that statement is the focus of this episode of Stats and Stories with guest Robert Matthews. Robert Matthews is a visiting professor in the Department of Mathematics, Aston University in Birmingham, UK. Since the late 1990s, as a science writer, he has been reporting on the role of NHST in undermining the reliability of research for several publications including BBC Focus, and working as a consultant on both scientific and media issues for clients in the UK and abroad. His latest book, Chancing It: The Laws of Chance and How They Can Work for You is available now.  His research interests include the development of Bayesian methods to assess the credibility of new research findings – especially “out of the blue” claims; A 20-year study of why research findings fade over time and its connection to what’s now called “The Replication Crisis”; Investigations of the maths and science behind coincidences and “urban myths” like Murphy’s Law: “If something can go wrong, it will”; Applications of Decision Theory to cast light on the reliability (or otherwise) of earthquake predictions and weather forecasts; The first-ever derivation and experimental verification of a prediction from string theory.

Every two years the International Prize in Statistics is given out to recognize an individual or team for major contributions to the field of statistics particularly those that have practical applications or which lead to breakthroughs in other disciplines. The winner is chosen in a collaboration between the American Statistical Association, the Institute for the Mathematical Sciences, the International Biometric Society, the International Statistical Institute, and the Royal Statistical Society. The 2021 honoree is Nan Laird and her award and career is the focus of this episode of Stats and Stories. Laird is the Harvey V. Fineberg Professor of Biostatistics at Harvard University. During her more than forty years on the faculty, she developed many simple and practical statistical methods for pressing public health and medical problems. Her work on the EM Algorithm, with Art Dempster and Don Rubin, is among the top 100 most cited of all published articles in science. She’s also developed popular and widely used methods for meta-analysis, longitudinal data, and statistical genetics. She has worked in several areas of application including the quantification of adverse events in hospitals, childhood obesity, and genetic studies in Alzheimer’s disease, bipolar disorder, asthma, and lung disease. Laird was awarded the 2021 International Prize in Statistics for, "her work on powerful methods that have made possible the analysis of complex longitudinal studies."

The universe seems unbelievably vast, a sky filled with countless stars and worlds. Well, maybe not so countless as there’s a whole field devoted to crunching the numbers associated with an ever-expanding universe. Astrostatistics is the focus of this episode of Stats and Stories Jessi Cisewski-Kehe is an assistant professor in the Department of Statistics at the University of Wisconsin-Madison. Her research focuses on methodological development for complicated datasets of which closed-form models and likelihood functions are not able to fully capture the desirable and interesting features of the observations. Statistical challenges in astronomy, astrophysics, and cosmology (i.e., astrostatistics) are a primary focus of her work. Chad Schafer is a professor in the Department of Statistics & Data Science at Carnegie Mellon University. Since his Ph.D. work at the University of California at Berkeley, he has worked on statistical challenges that arise in astronomy, with a particular focus on the handling of complex estimation problems. He is currently involved with the Legacy Survey of Space and Time, to be conducted at the under-construction Vera C. Rubin Observatory, co-chairing its Informatics and Statistics Science Collaboration since 2015.

Enter with your meme for a chance to appear on the show. All entries are submitted through Twitter through the official Stats+Stories account (https://twitter.com/statsandstories) by July 10th for entry.

Where are the best locations for food pantries? What are the patterns and use of a crisis call center? How can services be improved for the senior population of Wahtenaw County in Michigan? These questions share a common denominator, they represent data and analysis needs of community service organizations. Statistics in the service of the community is the focus of this episode of Stats and Stories with guests. Emily Morris and Tom Braun. Tom Braun is a Professor in the Department of Biostatistics and has been a faculty advisor for STATCOM for the past three years. Dr. Braun is an international expert in the design of Bayesian adaptive designs for oncology clinical trials, and he has more recently expanded his research into snSMART designs for clinical trials for rare diseases. Dr. Braun has collaborated with a variety of medical and public health investigators, including bone marrow transplantation, cancer of the mouth, breast, and lungs, periodontal disease, and development of anthrax vaccines. Tom also is an active member in University of Michigan committees working to address issues of incivility, rankism, and harassment in academia, and he also active in developing new pedagogy for teaching biostatistics and data science. Emily Morris is a PhD candidate in the Department of Biostatistics and former co-president of Statistics in the Community (STATCOM) at the University of Michigan. In addition to the leadership role, her involvement in STATCOM projects ranges from summarizing patterns in counseling visits at a local nonprofit to identifying optimal locations for mobile food pantries in Toledo. Her research primarily involves machine learning methods applied to brain imaging analysis.