Podcasts about American Statistical Association

Practical Significance focuses on the passion and commitment members bring to advancing the ASA’s mission of promoting the practice and profession of statistics. Cohosts Donna LaLonde and Ron Wasserstein chat with two guests—Susan Paddock, executive vice president and chief scientist at NORC at the University of Chicago, and Bo Li, professor at Washington University in St. Louis—about the career-changing and life-enhancing impact of their service to the American Statistical Association community. From their first “yes” to now helping shape the future of our profession, Susan and Bo share personal stories, unexpected ... The post Practical Significance | Episode 54: The Power of Saying, “Yes”— Volunteering & Service in the ASA first appeared on Amstat News.

Synopsis: What does bold biotech leadership look like in 2025? In this episode of Biotech 2050, host Alok Tayi sits down with Stacy Lindborg, President & CEO of Imunon, to discuss bold innovation in ovarian cancer treatment and how harnessing the immune system through targeted gene therapy is reshaping survival outcomes. Stacy shares insights from her 30-year career—from her statistical roots at Eli Lilly to her mission-driven leadership at Imunon. She highlights the groundbreaking results from Imunon's IL-12 plasmid platform, which is showing a remarkable 13-month overall survival advantage in ovarian cancer patients and is now entering Phase 3 trials. They also discuss the evolving biotech landscape, how adaptive trial designs and AI are unlocking clinical potential, and why cultivating a bold, transparent company culture is key to advancing transformational science. Biography: Stacy R. Lindborg, PhD, was appointed President and Chief Executive Officer of Imunon in May 2024. Dr. Lindborg has served on Imunon's Board of Directors since June 2021. Dr. Lindborg has nearly 30 years of pharmaceutical and biotech industry experience with a particular focus on R&D, regulatory affairs, executive management and strategy development. She has designed, hired and led global teams, guiding long-term vision for growth through analytics and stimulating innovative development platforms to increase productivity. Prior to joining Imunon, Dr. Lindborg was Executive Vice President and Co-Chief Executive Officer at BrainStorm Cell Therapeutics where she will remain a member of the company's Board of Directors. At BrainStorm she was accountable for creating and executing clinical development strategies through registration and launch and progressed its novel cell therapy for ALS through a positive Phase 3 Special Protocol Assessment (SPA) study with the U.S. Food and Drug Administration. She interacted frequently with investors and analysts, represented the company in the scientific community as well as with the media, and played an active role in discussions with potential business partners. Dr. Lindborg previously was Vice President & Global Analytics and Data Sciences Head, responsible for R&D and marketed products at Biogen. She began her biopharmaceutical career at Eli Lilly and Company where over the course of 16 years she assumed positions of increasing responsibility, including Head of R&D strategy. Dr. Lindborg received an M.A. and Ph.D. in statistics, and a B.A. in psychology and math from Baylor University. She has authored more than 200 presentations and 90 manuscripts that have been published in peer-reviewed journals, including 20 first-authored. She has held numerous positions within the International Biometric Society and American Statistical Association and was elected Fellow in 2008.

In this episode Gudrun speaks with Nadja Klein and Moussa Kassem Sbeyti who work at the Scientific Computing Center (SCC) at KIT in Karlsruhe. Since August 2024, Nadja has been professor at KIT leading the research group Methods for Big Data (MBD) there. She is an Emmy Noether Research Group Leader, and a member of AcademiaNet, and Die Junge Akademie, among others. In 2025, Nadja was awarded the Committee of Presidents of Statistical Societies (COPSS) Emerging Leader Award (ELA). The COPSS ELA recognizes early career statistical scientists who show evidence of and potential for leadership and who will help shape and strengthen the field. She finished her doctoral studies in Mathematics at the Universität Göttingen before conducting a postdoc at the University of Melbourne as a Feodor-Lynen fellow by the Alexander von Humboldt Foundation. Afterwards she was a Professor for Statistics and Data Science at the Humboldt-Universität zu Berlin before joining KIT. Moussa joined Nadja's lab as an associated member in 2023 and later as a postdoctoral researcher in 2024. He pursued a PhD at the TU Berlin while working as an AI Research Scientist at the Continental AI Lab in Berlin. His research primarily focuses on deep learning, developing uncertainty-based automated labeling methods for 2D object detection in autonomous driving. Prior to this, Moussa earned his M.Sc. in Mechatronics Engineering from the TU Darmstadt in 2021. The research of Nadja and Moussa is at the intersection of statistics and machine learning. In Nadja's MBD Lab the research spans theoretical analysis, method development and real-world applications. One of their key focuses is Bayesian methods, which allow to incorporate prior knowledge, quantify uncertainties, and bring insights to the “black boxes” of machine learning. By fusing the precision and reliability of Bayesian statistics with the adaptability of machine and deep learning, these methods aim to leverage the best of both worlds. The KIT offers a strong research environment, making it an ideal place to continue their work. They bring new expertise that can be leveraged in various applications and on the other hand Helmholtz offers a great platform in that respect to explore new application areas. For example Moussa decided to join the group at KIT as part of the Helmholtz Pilot Program Core-Informatics at KIT (KiKIT), which is an initiative focused on advancing fundamental research in informatics within the Helmholtz Association. Vision models typically depend on large volumes of labeled data, but collecting and labeling this data is both expensive and prone to errors. During his PhD, his research centered on data-efficient learning using uncertainty-based automated labeling techniques. That means estimating and using the uncertainty of models to select the helpful data samples to train the models to label the rest themselves. Now, within KiKIT, his work has evolved to include knowledge-based approaches in multi-task models, eg. detection and depth estimation — with the broader goal of enabling the development and deployment of reliable, accurate vision systems in real-world applications. Statistics and data science are fascinating fields, offering a wide variety of methods and applications that constantly lead to new insights. Within this domain, Bayesian methods are especially compelling, as they enable the quantification of uncertainty and the incorporation of prior knowledge. These capabilities contribute to making machine learning models more data-efficient, interpretable, and robust, which are essential qualities in safety-critical domains such as autonomous driving and personalized medicine. Nadja is also enthusiastic about the interdisciplinarity of the subject — repeatedly changing the focus from mathematics to economics to statistics to computer science. The combination of theoretical fundamentals and practical applications makes statistics an agile and important field of research in data science. From a deep learning perspective, the focus is on making models both more efficient and more reliable when dealing with large-scale data and complex dependencies. One way to do this is by reducing the need for extensive labeled data. They also work on developing self-aware models that can recognize when they're unsure and even reject their own predictions when necessary. Additionally, they explore model pruning techniques to improve computational efficiency, and specialize in Bayesian deep learning, allowing machine learning models to better handle uncertainty and complex dependencies. Beyond the methods themselves, they also contribute by publishing datasets that help push the development of next-generation, state-of-the-art models. The learning methods are applied across different domains such as object detection, depth estimation, semantic segmentation, and trajectory prediction — especially in the context of autonomous driving and agricultural applications. As deep learning technologies continue to evolve, they're also expanding into new application areas such as medical imaging. Unlike traditional deep learning, Bayesian deep learning provides uncertainty estimates alongside predictions, allowing for more principled decision-making and reducing catastrophic failures in safety-critical application. It has had a growing impact in several real-world domains where uncertainty really matters. Bayesian learning incorporates prior knowledge and updates beliefs as new data comes in, rather than relying purely on data-driven optimization. In healthcare, for example, Bayesian models help quantify uncertainty in medical diagnoses, which supports more risk-aware treatment decisions and can ultimately lead to better patient outcomes. In autonomous vehicles, Bayesian models play a key role in improving safety. By recognizing when the system is uncertain, they help capture edge cases more effectively, reduce false positives and negatives in object detection, and navigate complex, dynamic environments — like bad weather or unexpected road conditions — more reliably. In finance, Bayesian deep learning enhances both risk assessment and fraud detection by allowing the system to assess how confident it is in its predictions. That added layer of information supports more informed decision-making and helps reduce costly errors. Across all these areas, the key advantage is the ability to move beyond just accuracy and incorporate trust and reliability into AI systems. Bayesian methods are traditionally more expensive, but modern approximations (e.g., variational inference or last layer inference) make them feasible. Computational costs depend on the problem — sometimes Bayesian models require fewer data points to achieve better performance. The trade-off is between interpretability and computational efficiency, but hardware improvements are helping bridge this gap. Their research on uncertainty-based automated labeling is designed to make models not just safer and more reliable, but also more efficient. By reducing the need for extensive manual labeling, one improves the overall quality of the dataset while cutting down on human effort and potential labeling errors. Importantly, by selecting informative samples, the model learns from better data — which means it can reach higher performance with fewer training examples. This leads to faster training and better generalization without sacrificing accuracy. They also focus on developing lightweight uncertainty estimation techniques that are computationally efficient, so these benefits don't come with heavy resource demands. In short, this approach helps build models that are more robust, more adaptive to new data, and significantly more efficient to train and deploy — which is critical for real-world systems where both accuracy and speed matter. Statisticians and deep learning researchers often use distinct methodologies, vocabulary and frameworks, making communication and collaboration challenging. Unfortunately, there is a lack of Interdisciplinary education: Traditional academic programs rarely integrate both fields. It is necessary to foster joint programs, workshops, and cross-disciplinary training can help bridge this gap. From Moussa's experience coming through an industrial PhD, he has seen how many industry settings tend to prioritize short-term gains — favoring quick wins in deep learning over deeper, more fundamental improvements. To overcome this, we need to build long-term research partnerships between academia and industry — ones that allow for foundational work to evolve alongside practical applications. That kind of collaboration can drive more sustainable, impactful innovation in the long run, something we do at methods for big data. Looking ahead, one of the major directions for deep learning in the next five to ten years is the shift toward trustworthy AI. We're already seeing growing attention on making models more explainable, fair, and robust — especially as AI systems are being deployed in critical areas like healthcare, mobility, and finance. The group also expect to see more hybrid models — combining deep learning with Bayesian methods, physics-based models, or symbolic reasoning. These approaches can help bridge the gap between raw performance and interpretability, and often lead to more data-efficient solutions. Another big trend is the rise of uncertainty-aware AI. As AI moves into more high-risk, real-world applications, it becomes essential that systems understand and communicate their own confidence. This is where uncertainty modeling will play a key role — helping to make AI not just more powerful, but also more safe and reliable. The lecture "Advanced Bayesian Data Analysis" covers fundamental concepts in Bayesian statistics, including parametric and non-parametric regression, computational techniques such as MCMC and variational inference, and Bayesian priors for handling high-dimensional data. Additionally, the lecturers offer a Research Seminar on Selected Topics in Statistical Learning and Data Science. The workgroup offers a variety of Master's thesis topics at the intersection of statistics and deep learning, focusing on Bayesian modeling, uncertainty quantification, and high-dimensional methods. Current topics include predictive information criteria for Bayesian models and uncertainty quantification in deep learning. Topics span theoretical, methodological, computational and applied projects. Students interested in rigorous theoretical and applied research are encouraged to explore our available projects and contact us for further details. The general advice of Nadja and Moussa for everybody interested to enter the field is: "Develop a strong foundation in statistical and mathematical principles, rather than focusing solely on the latest trends. Gain expertise in both theory and practical applications, as real-world impact requires a balance of both. Be open to interdisciplinary collaboration. Some of the most exciting and meaningful innovations happen at the intersection of fields — whether that's statistics and deep learning, or AI and domain-specific areas like medicine or mobility. So don't be afraid to step outside your comfort zone, ask questions across disciplines, and look for ways to connect different perspectives. That's often where real breakthroughs happen. With every new challenge comes an opportunity to innovate, and that's what keeps this work exciting. We're always pushing for more robust, efficient, and trustworthy AI. And we're also growing — so if you're a motivated researcher interested in this space, we'd love to hear from you." Literature and further information Webpage of the group G. Nuti, Lluis A.J. Rugama, A.-I. Cross: Efficient Bayesian Decision Tree Algorithm, arxiv Jan 2019 Wikipedia: Expected value of sample information C. Howson & P. Urbach: Scientific Reasoning: The Bayesian Approach (3rd ed.). Open Court Publishing Company. ISBN 978-0-8126-9578-6, 2005. A.Gelman e.a.: Bayesian Data Analysis Third Edition. Chapman and Hall/CRC. ISBN 978-1-4398-4095-5, 2013. Yu, Angela: Introduction to Bayesian Decision Theory cogsci.ucsd.edu, 2013. Devin Soni: Introduction to Bayesian Networks, 2015. G. Nuti, L. Rugama, A.-I. Cross: Efficient Bayesian Decision Tree Algorithm, arXiv:1901.03214 stat.ML, 2019. M. Carlan, T. Kneib and N. Klein: Bayesian conditional transformation models, Journal of the American Statistical Association, 119(546):1360-1373, 2024. N. Klein: Distributional regression for data analysis , Annual Review of Statistics and Its Application, 11:321-346, 2024 C.Hoffmann and N.Klein: Marginally calibrated response distributions for end-to-end learning in autonomous driving, Annals of Applied Statistics, 17(2):1740-1763, 2023 Kassem Sbeyti, M., Karg, M., Wirth, C., Klein, N., & Albayrak, S. (2024, September). Cost-Sensitive Uncertainty-Based Failure Recognition for Object Detection. In Uncertainty in Artificial Intelligence (pp. 1890-1900). PMLR. M. K. Sbeyti, N. Klein, A. Nowzad, F. Sivrikaya and S. Albayrak: Building Blocks for Robust and Effective Semi-Supervised Real-World Object Detection pdf. To appear in Transactions on Machine Learning Research, 2025 Podcasts Learning, Teaching, and Building in the Age of AI Ep 42 of Vanishing Gradient, Jan 2025. O. Beige, G. Thäter: Risikoentscheidungsprozesse, Gespräch im Modellansatz Podcast, Folge 193, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2019.

In this episode Gudrun speaks with Nadja Klein and Moussa Kassem Sbeyti who work at the Scientific Computing Center (SCC) at KIT in Karlsruhe. Since August 2024, Nadja has been professor at KIT leading the research group Methods for Big Data (MBD) there. She is an Emmy Noether Research Group Leader, and a member of AcademiaNet, and Die Junge Akademie, among others. In 2025, Nadja was awarded the Committee of Presidents of Statistical Societies (COPSS) Emerging Leader Award (ELA). The COPSS ELA recognizes early career statistical scientists who show evidence of and potential for leadership and who will help shape and strengthen the field. She finished her doctoral studies in Mathematics at the Universität Göttingen before conducting a postdoc at the University of Melbourne as a Feodor-Lynen fellow by the Alexander von Humboldt Foundation. Afterwards she was a Professor for Statistics and Data Science at the Humboldt-Universität zu Berlin before joining KIT. Moussa joined Nadja's lab as an associated member in 2023 and later as a postdoctoral researcher in 2024. He pursued a PhD at the TU Berlin while working as an AI Research Scientist at the Continental AI Lab in Berlin. His research primarily focuses on deep learning, developing uncertainty-based automated labeling methods for 2D object detection in autonomous driving. Prior to this, Moussa earned his M.Sc. in Mechatronics Engineering from the TU Darmstadt in 2021. The research of Nadja and Moussa is at the intersection of statistics and machine learning. In Nadja's MBD Lab the research spans theoretical analysis, method development and real-world applications. One of their key focuses is Bayesian methods, which allow to incorporate prior knowledge, quantify uncertainties, and bring insights to the “black boxes” of machine learning. By fusing the precision and reliability of Bayesian statistics with the adaptability of machine and deep learning, these methods aim to leverage the best of both worlds. The KIT offers a strong research environment, making it an ideal place to continue their work. They bring new expertise that can be leveraged in various applications and on the other hand Helmholtz offers a great platform in that respect to explore new application areas. For example Moussa decided to join the group at KIT as part of the Helmholtz Pilot Program Core-Informatics at KIT (KiKIT), which is an initiative focused on advancing fundamental research in informatics within the Helmholtz Association. Vision models typically depend on large volumes of labeled data, but collecting and labeling this data is both expensive and prone to errors. During his PhD, his research centered on data-efficient learning using uncertainty-based automated labeling techniques. That means estimating and using the uncertainty of models to select the helpful data samples to train the models to label the rest themselves. Now, within KiKIT, his work has evolved to include knowledge-based approaches in multi-task models, eg. detection and depth estimation — with the broader goal of enabling the development and deployment of reliable, accurate vision systems in real-world applications. Statistics and data science are fascinating fields, offering a wide variety of methods and applications that constantly lead to new insights. Within this domain, Bayesian methods are especially compelling, as they enable the quantification of uncertainty and the incorporation of prior knowledge. These capabilities contribute to making machine learning models more data-efficient, interpretable, and robust, which are essential qualities in safety-critical domains such as autonomous driving and personalized medicine. Nadja is also enthusiastic about the interdisciplinarity of the subject — repeatedly changing the focus from mathematics to economics to statistics to computer science. The combination of theoretical fundamentals and practical applications makes statistics an agile and important field of research in data science. From a deep learning perspective, the focus is on making models both more efficient and more reliable when dealing with large-scale data and complex dependencies. One way to do this is by reducing the need for extensive labeled data. They also work on developing self-aware models that can recognize when they're unsure and even reject their own predictions when necessary. Additionally, they explore model pruning techniques to improve computational efficiency, and specialize in Bayesian deep learning, allowing machine learning models to better handle uncertainty and complex dependencies. Beyond the methods themselves, they also contribute by publishing datasets that help push the development of next-generation, state-of-the-art models. The learning methods are applied across different domains such as object detection, depth estimation, semantic segmentation, and trajectory prediction — especially in the context of autonomous driving and agricultural applications. As deep learning technologies continue to evolve, they're also expanding into new application areas such as medical imaging. Unlike traditional deep learning, Bayesian deep learning provides uncertainty estimates alongside predictions, allowing for more principled decision-making and reducing catastrophic failures in safety-critical application. It has had a growing impact in several real-world domains where uncertainty really matters. Bayesian learning incorporates prior knowledge and updates beliefs as new data comes in, rather than relying purely on data-driven optimization. In healthcare, for example, Bayesian models help quantify uncertainty in medical diagnoses, which supports more risk-aware treatment decisions and can ultimately lead to better patient outcomes. In autonomous vehicles, Bayesian models play a key role in improving safety. By recognizing when the system is uncertain, they help capture edge cases more effectively, reduce false positives and negatives in object detection, and navigate complex, dynamic environments — like bad weather or unexpected road conditions — more reliably. In finance, Bayesian deep learning enhances both risk assessment and fraud detection by allowing the system to assess how confident it is in its predictions. That added layer of information supports more informed decision-making and helps reduce costly errors. Across all these areas, the key advantage is the ability to move beyond just accuracy and incorporate trust and reliability into AI systems. Bayesian methods are traditionally more expensive, but modern approximations (e.g., variational inference or last layer inference) make them feasible. Computational costs depend on the problem — sometimes Bayesian models require fewer data points to achieve better performance. The trade-off is between interpretability and computational efficiency, but hardware improvements are helping bridge this gap. Their research on uncertainty-based automated labeling is designed to make models not just safer and more reliable, but also more efficient. By reducing the need for extensive manual labeling, one improves the overall quality of the dataset while cutting down on human effort and potential labeling errors. Importantly, by selecting informative samples, the model learns from better data — which means it can reach higher performance with fewer training examples. This leads to faster training and better generalization without sacrificing accuracy. They also focus on developing lightweight uncertainty estimation techniques that are computationally efficient, so these benefits don't come with heavy resource demands. In short, this approach helps build models that are more robust, more adaptive to new data, and significantly more efficient to train and deploy — which is critical for real-world systems where both accuracy and speed matter. Statisticians and deep learning researchers often use distinct methodologies, vocabulary and frameworks, making communication and collaboration challenging. Unfortunately, there is a lack of Interdisciplinary education: Traditional academic programs rarely integrate both fields. It is necessary to foster joint programs, workshops, and cross-disciplinary training can help bridge this gap. From Moussa's experience coming through an industrial PhD, he has seen how many industry settings tend to prioritize short-term gains — favoring quick wins in deep learning over deeper, more fundamental improvements. To overcome this, we need to build long-term research partnerships between academia and industry — ones that allow for foundational work to evolve alongside practical applications. That kind of collaboration can drive more sustainable, impactful innovation in the long run, something we do at methods for big data. Looking ahead, one of the major directions for deep learning in the next five to ten years is the shift toward trustworthy AI. We're already seeing growing attention on making models more explainable, fair, and robust — especially as AI systems are being deployed in critical areas like healthcare, mobility, and finance. The group also expect to see more hybrid models — combining deep learning with Bayesian methods, physics-based models, or symbolic reasoning. These approaches can help bridge the gap between raw performance and interpretability, and often lead to more data-efficient solutions. Another big trend is the rise of uncertainty-aware AI. As AI moves into more high-risk, real-world applications, it becomes essential that systems understand and communicate their own confidence. This is where uncertainty modeling will play a key role — helping to make AI not just more powerful, but also more safe and reliable. The lecture "Advanced Bayesian Data Analysis" covers fundamental concepts in Bayesian statistics, including parametric and non-parametric regression, computational techniques such as MCMC and variational inference, and Bayesian priors for handling high-dimensional data. Additionally, the lecturers offer a Research Seminar on Selected Topics in Statistical Learning and Data Science. The workgroup offers a variety of Master's thesis topics at the intersection of statistics and deep learning, focusing on Bayesian modeling, uncertainty quantification, and high-dimensional methods. Current topics include predictive information criteria for Bayesian models and uncertainty quantification in deep learning. Topics span theoretical, methodological, computational and applied projects. Students interested in rigorous theoretical and applied research are encouraged to explore our available projects and contact us for further details. The general advice of Nadja and Moussa for everybody interested to enter the field is: "Develop a strong foundation in statistical and mathematical principles, rather than focusing solely on the latest trends. Gain expertise in both theory and practical applications, as real-world impact requires a balance of both. Be open to interdisciplinary collaboration. Some of the most exciting and meaningful innovations happen at the intersection of fields — whether that's statistics and deep learning, or AI and domain-specific areas like medicine or mobility. So don't be afraid to step outside your comfort zone, ask questions across disciplines, and look for ways to connect different perspectives. That's often where real breakthroughs happen. With every new challenge comes an opportunity to innovate, and that's what keeps this work exciting. We're always pushing for more robust, efficient, and trustworthy AI. And we're also growing — so if you're a motivated researcher interested in this space, we'd love to hear from you." Literature and further information Webpage of the group G. Nuti, Lluis A.J. Rugama, A.-I. Cross: Efficient Bayesian Decision Tree Algorithm, arxiv Jan 2019 Wikipedia: Expected value of sample information C. Howson & P. Urbach: Scientific Reasoning: The Bayesian Approach (3rd ed.). Open Court Publishing Company. ISBN 978-0-8126-9578-6, 2005. A.Gelman e.a.: Bayesian Data Analysis Third Edition. Chapman and Hall/CRC. ISBN 978-1-4398-4095-5, 2013. Yu, Angela: Introduction to Bayesian Decision Theory cogsci.ucsd.edu, 2013. Devin Soni: Introduction to Bayesian Networks, 2015. G. Nuti, L. Rugama, A.-I. Cross: Efficient Bayesian Decision Tree Algorithm, arXiv:1901.03214 stat.ML, 2019. M. Carlan, T. Kneib and N. Klein: Bayesian conditional transformation models, Journal of the American Statistical Association, 119(546):1360-1373, 2024. N. Klein: Distributional regression for data analysis , Annual Review of Statistics and Its Application, 11:321-346, 2024 C.Hoffmann and N.Klein: Marginally calibrated response distributions for end-to-end learning in autonomous driving, Annals of Applied Statistics, 17(2):1740-1763, 2023 Kassem Sbeyti, M., Karg, M., Wirth, C., Klein, N., & Albayrak, S. (2024, September). Cost-Sensitive Uncertainty-Based Failure Recognition for Object Detection. In Uncertainty in Artificial Intelligence (pp. 1890-1900). PMLR. M. K. Sbeyti, N. Klein, A. Nowzad, F. Sivrikaya and S. Albayrak: Building Blocks for Robust and Effective Semi-Supervised Real-World Object Detection pdf. To appear in Transactions on Machine Learning Research, 2025 Podcasts Learning, Teaching, and Building in the Age of AI Ep 42 of Vanishing Gradient, Jan 2025. O. Beige, G. Thäter: Risikoentscheidungsprozesse, Gespräch im Modellansatz Podcast, Folge 193, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2019.

Lisa LaVange is the 2018 President of the American Statistical Association and she is PhD, is Professor and Associate Chair of the Department of Biostatistics { add link to dept } in the Gillings School of Global Public Health { add link to Gillings SPH } at the University of North Carolina at Chapel Hill. She is also director of the department's Collaborative Studies Coordinating Center (CSCC), overseeing faculty, staff, and students involved in large-scale clinical trials and epidemiological studies coordinated by the center. Ronald L. (Ron) Wasserstein is the executive director of the American Statistical Association (ASA). Wasserstein assumed the ASA's top staff leadership post in August 2007. Prior to joining the ASA, Wasserstein was a mathematics and statistics department faculty member and administrator at Washburn University in Topeka, Kan., from 1984–2007. During his last seven years at the school, he served as the university's vice president for academic affairs.

Jon Hartley and Federal Reserve Governor Adriana Kugler discuss the stance of monetary policy, the Federal Reserve balance sheet, the natural rate of interest (r-star), inflation, labor markets, productivity, entrepreneurship, the US economy, and the recent growth in Miami. Recorded on February 7, 2025. ABOUT THE SPEAKERS: Dr. Adriana D. Kugler took office as a member of the Board of Governors of the Federal Reserve System on September 13, 2023, to fill an unexpired term ending January 31, 2026. Prior to her appointment on the Board, Dr. Kugler served as the U.S. Executive Director at the World Bank Group. She is on leave from Georgetown University where she is a professor of Public Policy and Economics and was vice provost for faculty. Previously, she served as chief economist at the U.S. Department of Labor from 2011 to 2013. Dr. Kugler was also a research associate of the National Bureau of Economic Research and of the Center for the Study of Poverty and Inequality at Stanford University. Dr. Kugler's other professional appointments include being the elected chair of the Business and Economics Statistics Section of the American Statistical Association. She was also a member of the Board on Science, Technology, and Economic Policy of the National Academies of Sciences and served on the Technical Advisory Committee of the Bureau of Labor Statistics. Dr. Kugler received a BA in economics and political science from McGill University and a PhD in economics from the University of California, Berkeley. Jon Hartley is the host of the Capitalism and Freedom in the 21st Century Podcast at the Hoover Institution and an economics PhD Candidate at Stanford University, where he specializes in finance, labor economics, and macroeconomics. He is also currently an Affiliated Scholar at the Mercatus Center, a Senior Fellow at the Foundation for Research on Equal Opportunity (FREOPP), and a Senior Fellow at the Macdonald-Laurier Institute. Jon is also a member of the Canadian Group of Economists, and serves as chair of the Economic Club of Miami. Jon has previously worked at Goldman Sachs Asset Management as well as in various policy roles at the World Bank, IMF, Committee on Capital Markets Regulation, US Congress Joint Economic Committee, the Federal Reserve Bank of New York, the Federal Reserve Bank of Chicago, and the Bank of Canada.  Jon has also been a regular economics contributor for National Review Online, Forbes, and The Huffington Post and has contributed to The Wall Street Journal, The New York Times, USA Today, Globe and Mail, National Post, and Toronto Star among other outlets. Jon has also appeared on CNBC, Fox Business, Fox News, Bloomberg, and NBC, and was named to the 2017 Forbes 30 Under 30 Law & Policy list, the 2017 Wharton 40 Under 40 list, and was previously a World Economic Forum Global Shaper. ABOUT THE SERIES: Each episode of Capitalism and Freedom in the 21st Century, a video podcast series and the official podcast of the Hoover Economic Policy Working Group, focuses on getting into the weeds of economics, finance, and public policy on important current topics through one-on-one interviews. Host Jon Hartley asks guests about their main ideas and contributions to academic research and policy. The podcast is titled after Milton Friedman‘s famous 1962 bestselling book Capitalism and Freedom, which after 60 years, remains prescient from its focus on various topics which are now at the forefront of economic debates, such as monetary policy and inflation, fiscal policy, occupational licensing, education vouchers, income share agreements, the distribution of income, and negative income taxes, among many other topics. For more information, visit: capitalismandfreedom.substack.com/

In this milestone 50th episode, co-hosts Donna LaLonde and Ron Wasserstein continue their tradition of introducing the American Statistical Association’s incoming president. Ji-Hyun Lee, the ASA’s 120th president, joins them to share her vision for 2025, which is centered on “building bridges.” As she begins her presidential year, Lee shares both her excitement and jitters, reflecting, “This presidency is the honor of a lifetime—and with that honor comes an incredible sense of responsibility.” Several promising initiatives are already underway. One example is her groundbreaking collaboration with Nature Medicine, which by integrating statistical ... The post Practical Significance | Episode 50: Building Bridges with ASA 2025 President Ji-Hyun Lee first appeared on Amstat News.

The American Statistical Association's Excellence in Statistical Reporting Award recognizes media professionals for their presentation of the science of statistics and its role in public life. During the 2024 Joint Statistical Meetings in Portland, Oregon, this summer, Harry Stevens of The Washington Post was honored with this award for his Climate Lab story series—which covers environmental change in a clear, personalized, transparent, and reproducible way. Joining Practical Significance co-hosts Donna LaLonde and Ron Wasserstein, Stevens says he writes about compelling climate trends and their effect on the natural world, presenting the ... The post Practical Significance | Episode 46—Data Is My Superpower: A Conversation with the Excellence in Statistical Reporting Award Recipient first appeared on Amstat News.

Wendy Martinez has been serving as the Director of the Mathematical Statistics Research Center at the Bureau of Labor Statistics (BLS) for six years. Prior to this, she served in several research positions throughout the Department of Defense. She held the position of Science and Technology Program Officer at the Office of Naval Research, where she established a research portfolio comprised of academia and industry performers developing data science products for the future Navy and Marine Corps. She was honored by the American Statistical Association when she received the ASA Founders Award at the JSM 2017 conference. Wendy is also proud and grateful to have been elected as the 2020 ASA President.

Sports generate a lot of data among them individual player metrics, team performance data, and specific game statistics. And there are a lot of tools to crunch all those numbers. Learning to use them can be a challenge and is the focus of many sport analytics classes offered in the United States. We hear about one professor's approach to teaching sports stats in this episode of Stats and Stories, where we explore the statistics behind the stories with guest Mark Glickman. Glickman is a Fellow of the American Statistical Association, a Senior Lecturer on Statistics in the Harvard University Department of Statistics, and Senior Statistician at the Center for Healthcare Organization and Implementation Research. His research interests are primarily in the areas of statistical models for rating competitors in games and sports, and in statistical methods applied to problems in health services research. He served as an elected member of the American Statistical Association's Board of Directors as representative of the Council of Sections Governing Board from 2019 to 2021.

The democratic engine of the United States relies on accurate and reliable data to function. A year-long study of the 13 federal agencies involved in U.S. data collection, including the Census Bureau, Bureau of Labor Statistics, and the National Center for Education Statistics suggests that the nation's statistics are at risk. The study was produced by the American Statistical Association in partnership with George Mason University and supported by the Sloan Foundation and is the focus of this episode of Stats+Stories. Constance (Connie) Citro is a senior scholar with the Committee on National Statistics and an independent consultant in which capacity she worked on the project that produced A Nation's Data at Risk. She was previously CNSTAT director from 2004-2017 and senior study director from 1986-2003. Citro was an American Statistical Association/National Science Foundation/Census Bureau research fellow and is a fellow of the American Statistical Association and an elected member of the International Statistical Institute. She served as president of the Association of Public Data Users and its representative to the Council of Professional Associations on Federal Statistics, edited the Window on Washington column for Chance magazine, and served on the Advisory Committee of the Journal of Survey Statistics and Methodology. In 2018, the American Statistical Association established the Links Lecture Award in honor of Citro, Robert Groves, and Fritz Scheuren. She will give the 32nd Morris Hansen Lecture in September 2024. Jonathan Auerbach is an assistant professor in the Department of Statistics at George Mason University. His research covers a wide range of topics at the intersection of statistics and public policy, including urban analytics, open data, and official statistics. His methodological interests include the analysis of longitudinal data, particularly for data science and causal inference. He is the current president of the Washington Statistical Society and the former science policy fellow at the American Statistical Association

One thing that we always value at Stat+Stories is the story of, “How did we get here?”. Today's episode follows our colleague, from work that she did in the federal government to now leading the charge at a biocomplexity institute. That's the focus of this episode of Stats and Short Stories. Stephanie Shipp is a research professor at the Biocomplexity Institute, University of Virginia. She co-founded and led the Social and Decision Analytics Division in 2013, starting at Virginia Tech and moving to the University of Virginia in 2018. Dr. Shipp's work spans topics related to using all data to advance policy, the science of data science, community analytics, and innovation. She leads and engages in local, state, and federal projects to assess data quality and the ethical use of new and traditional data sources. She is leading the development of the Curated Data Enterprise (CDE) that aligns with the Census Bureau's modernization and transformation and their Statistical Products First approach. She is a member of the American Statistical Association's Committee on Professional Ethics, Symposium on Data Science and Statistics (SDSS) Committee, and the Professional Issues and Visibility Council. She is an elected member of the International Statistical Institute, an American Association for the Advancement of Science Fellow, and an American Statistical Association (ASA) Fellow. She received the ASA Founder's award in 2022.

The American Statistical Association recently joined forces with Instats, an organization dedicated to enhancing global research practices by offering expert-led training to researchers through its online platform. The ASA/Instats partnership will bring members a broad set of learning opportunities. Practical Significance co-hosts Donna LaLonde and Ron Wasserstein engage with Michael Zyphur, director of Instats, to explore this innovative learning experience—including the unique features that set the Instats platform apart. ASA members will have access to live seminars, on-demand seminars, and structured courses. They can also easily filter these offerings based on their ... The post Practical Significance | Episode 44: Lifelong Learning—New Opportunities for ASA Members first appeared on Amstat News.

What fundamental values should data scientists and statisticians bring to their work? What principles should guide the work of data scientists and statisticians? What does right and wrong mean in the context of an analysis? That's the topic of today's stats and stories episode with guests Stephanie Shipp and Donna LeLonde Stephanie Shipp is a research professor at the Biocomplexity Institute, University of Virginia. She co-founded and led the Social and Decision Analytics Division in 2013, starting at Virginia Tech and moving to the University of Virginia in 2018. Dr. Shipp's work spans topics related to using all data to advance policy, the science of data science, community analytics, and innovation. She leads and engages in local, state, and federal projects to assess data quality and the ethical use of new and traditional data sources. She is leading the development of the Curated Data Enterprise (CDE) that aligns with the Census Bureau's modernization and transformation and their Statistical Products First approach. She is a member of the American Statistical Association's Committee on Professional Ethics, Symposium on Data Science and Statistics (SDSS) Committee, and the Professional Issues and Visibility Council. She is an elected member of the International Statistical Institute, an American Association for the Advancement of Science Fellow, and an American Statistical Association (ASA) Fellow. She received the ASA Founder's award in 2022. Donna LaLonde is the Associate Executive Director of the American Statistical Association (ASA) where she works with talented colleagues to advance the vision and mission of the ASA. Prior to joining the ASA in 2015, she was a faculty member at Washburn University where she enjoyed teaching and learning with colleagues and students; she also served in various administrative positions including interim chair of the Education Department and Associate Vice President for Academic Affairs. At the ASA, she supports activities associated with presidential initiatives, accreditation, education, and professional development. She also is a cohost of the Practical Significance podcast which John and Rosemary appeared on last year.

In Episode 131, Ben chats with Jared Van, a Ph.D student at Penn State University studying under precision teaching legend Dr. Rick Kubina. This episode is all about precision teaching through the lens of abolitionist behaviour science!   Continuing Education Units (CEUs): https://cbiconsultants.com/shop BACB: 1.5 Learning  IBAO: 1.5 Cultural QABA: 1.5  DEI Contact: Jared Van https://www.jaredvan.com/ https://www.tiktok.com/@jaredv_ https://instagram.com/jaredvan https://www.facebook.com/jbonevan   Links: Rick Kubina at Penn State https://ed.psu.edu/directory/dr-richard-kubina-jr https://www.instagram.com/rickkubina/   The Precision Teaching Book by Rick Kubina https://www.amazon.com/The-Precision-Teaching-Book/dp/0615554202   Steve Graff http://www.stevegraf.org/ The Standard Celeration Society https://celeration.org/ Rose Wrist Systemic Racism  https://www.reddit.com/r/VaushV/comments/he1wos/rose_wrists_research_doc_on_systemic_racism/ Morningside Academy https://morningsideacademy.org/   Fit Learning https://fitlearners.com/   Amy Evans at Octave Innovation https://octavetraining.com/about     Behaviour Speak Podcast Episodes Referenced   Kaelynn Partlow https://www.behaviourspeak.com/e/episode-50-the-experiences-of-an-autistic-rbt-with-kaelynn-partlow/   Valeria Parejo https://www.behaviourspeak.com/e/episode-109-behaviour-analysis-in-brasil-with-valeria-parejo/   Articles Referenced: Binder C. (1996). Behavioral fluency: Evolution of a new paradigm. The Behavior Analyst, 19(2), 163–197. https://doi.org/10.1007/BF03393163 Fisher, I. (1917). The “Ratio” Chart for Plotting Statistics. Publications of the American Statistical Association, 15(118), 577–601. https://doi.org/10.2307/2965173      

Avinash is the global Chief Strategy Officer of Croud, a leading full-service marketing Agency. His prior professional experience includes a sixteen-year stint at Google, and roles at Intuit, DirecTV, Silicon Graphics in the US & DHL in Saudi Arabia. Through his newsletter “The Marketing < > Analytics Intersect”, his blog “Occam's Razor,” and his best-selling books “Web Analytics: An Hour A Day” and “Web Analytics 2.0,” Avinash has become recognized as an authoritative voice on how marketers, executives' teams and industry leaders can leverage data to fundamentally reinvent their digital existence. Avinash puts a common-sense framework around the often-frenetic world of web analytics and combines that with the philosophy that investing in talented analysts is the key to long-term success. He passionately advocates customer centricity and leveraging bleeding edge competitive intelligence techniques. Avinash has received rave reviews for bringing his energetic, inspiring, and practical insights to companies like Unilever, Dell, Time Warner, Vanguard, Porsche, and IBM. He has delivered keynotes at a variety of global conferences, including Ad-Tech, Monaco Media Forum, Search Engine Strategies, JMP Innovators' Summit, The Art of Marketing and Web 2.0. Acting on his passion for teaching, Avinash has lectured at major universities such as Stanford University, University of Virginia, University of California - Los Angeles and University of Utah. Among the awards Avinash has received are Statistical Advocate of the Year from the American Statistical Association, Most Influential Industry Contributor from the Web Analytics Association, and Founder's Award from Google.

In this episode, we discuss Quantifauxcation, described by statistician Philip Stark as “situations in which a number is, in effect, made up, and then is given credence merely because it is quantitative.” We give examples of quantifauxcation in psychology, including errors of the third kind. We spend the second half of the podcast discussing how to develop quantitative measures that are meaningful and bridge the divide between qualitative and quantitative observations.   Shownotes Statistics textbook by Philip Stark. Stark, P. B. (2022). Pay No attention to the model behind the curtain. Pure and Applied Geophysics, 179(11), 4121–4145. https://doi.org/10.1007/s00024-022-03137-2  Burgess, E. W. (1927). Statistics and case studies as methods of sociological research, Vol 12(3), 103-120. (Thanks to Andy Grieve!) Nick Brown's role in pointing out flaws in the positivity ratio. Retraction notice of the positivity ratio paper. Blog by Tania Lombrozo on nonsensical formulas in abstracts. Kimball, A. W. (1957). Errors of the third kind in statistical consulting. Journal of the American Statistical Association, 52(278), 133–142. https://doi.org/10.1080/01621459.1957.10501374 Type III errors: Philip Stark's post of Deborah Mayo's blog Brower, D. (1949). The problem of quantification in psychological science. Psychological Review, 56(6), 325–333. https://doi.org/10.1037/h0061802 Guttman scales Wilson, M. (2023). Constructing measures: An item response modeling approach. Taylor & Francis. Wilson, M., Bathia, S., Morell, L., Gochyyev, P., Koo, B. W., & Smith, R. (2022). Seeking a better balance between efficiency and interpretability: Comparing the likert response format with the Guttman response format. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000462 Bhatti, H.A., Mehta, S., McNeil, R., Wilson, M. (2023). A scientific approach to assessment: Rasch measurement and the four building blocks. In X. Liu & W. Boone (Eds.), Advances in Applications of Rash Measurement in Science Education. Springer Nature.   

The history of statistics is filled with interesting facts about the development of the field and stories of the people who helped shape it. A new column at CHANCE magazine will explore the history of stats which is the focus of this episode of Stats+Stories with guest Chiatra Nagaraja Chaitra Nagaraja is a Senior Lecturer at the University of Exeter. Her research interests are primarily in measurement, particularly macroeconomic and socioeconomic indicators, time series, and the history of statistics. Prior to joining Exeter, she was a faculty member at the Gabelli School of Business at Fordham University in New York City where she wrote the 2019 book Measuring Society and a research mathematical statistician at the U.S Census Bureau, focusing on the American Community Survey. The book is a history of US official statistics like unemployment, inflation, and poverty. In addition to her university research and teaching, she is the chair of the American Statistical Association's Scientific and Public Affairs Advisory Committee, a member of the Royal Statistical Society's History of Statistics Section, and the book review editor for the International Statistical Review. She also recently accepted a co-editorship position for the new history of statistics column in CHANCE magazine.

#enterpriseai #generativeai #aiethics Harvard Business School professor Iavor Bojinov explains how to make enterprise AI projects successful, given their high rate of failure. Here are his key points:► Prioritize Projects: Focus on AI initiatives that align with your business goals and are feasible to implement.►Leadership and Culture: Make sure your company culture is open to AI, with leaders who grasp both the technical and commercial dimensions.►Scalable Systems: Develop an 'AI factory' to streamline and scale AI development.►Vendor Flexibility: When working with outside partners, don't lock yourself into one platform. Be ready to switch if necessary.►Trust: Ensure that AI systems are transparent, audit for bias, and establish clear lines of accountability for failures.Bojinov stresses that each stage of an AI project, from conception to ongoing management, needs expertise, adaptability, a receptive culture, and trust. With a well-thought-out strategy, companies can navigate typical challenges and harness AI's full potential.Our guest co-host for this episode is QuHarrison Terry.Read the complete transcript and see more of CXOTalk: www.cxotalk.com/episode/harvard-business-school-professor-how-to-lead-enterprise-aiSubscribe for more: www.cxotalk.com/subscribeIavor Bojinov is an Assistant Professor of Business Administration and the Richard Hodgson Fellow at Harvard Business School. He is the co-PI of the AI and Data Science Operations Lab and a faculty affiliate in the Department of Statistics at Harvard University and the Harvard Data Science Initiative. His research and writings center on data science strategy and operations, aiming to understand how companies should overcome the methodological and operational challenges presented by the novel applications of AI. His work has been published in top academic journals such as Annals of Applied Statistics, Biometrika, The Journal of the American Statistical Association, Quantitative Economics, Management Science, and Science, and has been cited in Forbes, The New York Times, The Washingon Post, and Reuters, among other outlets. Before joining Harvard Business School, Professor Bojinov worked as a data scientist leading the causal inference effort within the Applied Research Group at LinkedIn. He holds a Ph.D. and an MA in Statistics from Harvard and an MSci in Mathematics from King's College London.QuHarrison Terry is head of growth marketing at Mark Cuban Companies, a Texas venture capital firm, where he advises and assists portfolio companies with their marketing strategies and objectives.Michael Krigsman is an industry analyst and publisher of CXOTalk. For three decades, he has advised enterprise technology companies on market messaging and positioning strategy. He has written over 1,000 blogs on leadership and digital transformation and created almost 1,000 video interviews with the world's top business leaders on these topics. His work has been referenced in the media over 1,000 times and in over 50 books. He has presented and moderated panels at numerous industry events around the world.

On April 21, 2021, the American Statistical Association became a full member of CSAB, joining the world's two largest professional and technical societies for computing—the Association for Computing Machinery (ACM) and the IEEE Computer Society (IEEE-CS). This month, Practical Significance co-hosts Donna LaLonde and Ron Wasserstein welcome to the show newly appointed CSAB Executive Director Andrew (Andy) Phillips to get an update on data science accreditation. In addition to data science programs, CSAB is the lead ABET member society for accreditation of degree programs in computer science, cybersecurity, information systems, ... The post Practical Significance | Episode 32: Getting the ‘Data' on CSAB with Andrew (Andy) Phillips first appeared on Amstat News.

Dr. Mark Glickman, a Fellow of the American Statistical Association and Senior Lecturer on Statistics at the Harvard University Department of Statistics, joins John Williams to tell us what you need to know if you want to have a better chance of winning the lottery.

Dr. Mark Glickman, a Fellow of the American Statistical Association and Senior Lecturer on Statistics at the Harvard University Department of Statistics, joins John Williams to tell us what you need to know if you want to have a better chance of winning the lottery.

Dr. Mark Glickman, a Fellow of the American Statistical Association and Senior Lecturer on Statistics at the Harvard University Department of Statistics, joins John Williams to tell us what you need to know if you want to have a better chance of winning the lottery.

In this episode, we discuss the issue of publication bias. We discuss issues like: Do we learn anything from null results, given the current state of research practices? Is poorly done research still worth sharing with the scientific community? And how can we move toward a system where null results are informative and worth publishing?   Shownotes Bones, A. K. (2012). We Knew the Future All Along Scientific Hypothesizing is Much More Accurate Than Other Forms of Precognition—A Satire in One Part. Perspectives on Psychological Science, 7(3), 307–309. https://doi.org/10.1177/1745691612441216 Carter, E. C., & McCullough, M. E. (2014). Publication bias and the limited strength model of self-control: Has the evidence for ego depletion been overestimated? Frontiers in Psychology, 5. https://doi.org/10.3389/fpsyg.2014.00823 Greenwald, A. G. (1975). Consequences of prejudice against the null hypothesis. Psychological Bulletin, 82(1), 1–20. https://doi.org/10.1037/h0076157 Fidler, F., Singleton Thorn, F., Barnett, A., Kambouris, S., & Kruger, A. (2018). The epistemic importance of establishing the absence of an effect. Advances in Methods and Practices in Psychological Science, 1(2), 237-244. Pickett, J. T., & Roche, S. P. (2017). Questionable, Objectionable or Criminal? Public Opinion on Data Fraud and Selective Reporting in Science. Science and Engineering Ethics, 1–21. https://doi.org/10.1007/s11948-017-9886-2 Scheel, A. M., Schijen, M. R. M. J., & Lakens, D. (2021). An Excess of Positive Results: Comparing the Standard Psychology Literature With Registered Reports. Advances in Methods and Practices in Psychological Science, 4(2), 25152459211007468. https://doi.org/10.1177/25152459211007467 Sterling, T. D. (1959). Publication Decisions and Their Possible Effects on Inferences Drawn from Tests of Significance—Or Vice Versa. Journal of the American Statistical Association, 54(285), 30–34. JSTOR. https://doi.org/10.2307/2282137 The FDA Trial tracker to see which trials have not shared their results: https://fdaaa.trialstracker.net/   

#ai #aifailure Join us on CXOTalk episode 778 as we delve into the complexities of AI failure with our guest, Iavor Bojinov, Assistant Professor at Harvard Business School, and QuHarrison Terry, Head of Growth Marketing at Mark Cuban Companies. In this episode, we explore the various types of AI failures that can occur, the causes behind them, and how they differ from traditional technology and IT projects.Our experts also address the implications of these differences and distinguish between technology failures and human errors that can cause AI failures. In the second segment, we discuss solutions to address AI failure, including practical advice on how to avoid it in the first place. We also touch on the crucial ethical and privacy considerations associated with AI.This is a must-watch episode for business leaders building an AI group within their organization. Our guests offer valuable insights into AI governance, which can help prevent failures and maximize opportunities.The conversation includes these topics:► Understanding AI operations and ai project failures► Differences between AI projects and traditional technology or IT projects: AI is probabilistic► Five steps of an AI project--- Step 1: Project Selection - Choosing the Right Project--- Step 2: Development - Building the Prototype--- Step 3: Evaluation - Testing on Real People--- Step 4: Deployment and Scaling - Going from Pilot to Full Launch--- Step 5: Management - Monitoring and Preventing Failures► The shift from rule-based expert systems to probabilistic AI► Implementing AI in small businesses► AI pilot projects should have direct implications on revenue► Categories of AI failure► Successful projects integrate AI into processes and business operations► Data sets are a key difference between traditional IT projects and AI projects► Digital Transformation vs. AI► Ethical considerations of AI► Principles of privacy by design► Addressing bias in decision-making based on data► Governance and regulation of AI► Advice to business and technology leaders on preventing AI failuresSubscribe to the CXOTalk newsletter: https://www.cxotalk.comRead the complete transcript and watch more interviews: https://www.cxotalk.com/episode/why-ai-projects-fail-how-stop-itIavor Bojinov is an Assistant Professor of Business Administration and the Richard Hodgson Fellow at Harvard Business School. He is the co-PI of the AI and Data Science Operations Lab and a faculty affiliate in the Department of Statistics at Harvard University and the Harvard Data Science Initiative. His research and writings center on data science strategy and operations, aiming to understand how companies should overcome the methodological and operational challenges presented by the novel applications of AI. His work has been published in top academic journals such as Annals of Applied Statistics, Biometrika, The Journal of the American Statistical Association, Quantitative Economics, Management Science, and Science, and has been cited in Forbes, The New York Times, The Washingon Post, and Reuters, among other outlets.Professor Bojinov is also the co-creator of the first-year required MBA course “Data Science for Managers” and has previously taught the “Competing in the Age of AI” and “Technology and Operations Management” courses. Before joining Harvard Business School, Professor Bojinov worked as a data scientist leading the causal inference effort within the Applied Research Group at LinkedIn. He holds a Ph.D. and an MA in Statistics from Harvard and an MSci in Mathematics from King's College London.QuHarrison Terry is Head of Growth Marketing at Mark Cuban Companies, a Dallas, Texas venture capital firm, where he advises and assists portfolio companies with their marketing strategies and objectives.Previously, he led marketing at Redox, focusing on lead acquisition, new user experience, events, and content marketing. QuHarrison has been featured on CNN, Harvard Business Review, WIRED, Forbes and is the co-host of CNBC's Primetime Series – No Retreat: Business Bootcamp. As a speaker and moderator QuHarrison has presented at CES, TEDx, Techsylvania in Romania, Persol Holdings in Tokyo, SXSW in Austin, TX, and more. QuHarrison is a 4x recipient of Linkedin's top voices in Technology award.

In many countries in the Global South, partnerships and collaborations are crucial to moving forward projects of various kinds. A network based at the University of Colorado Boulder has facilitated the creation of statistics and data science collaboration labs in 10 countries, The LISA 2020 Global Network and it's efforts are the focus of this episode of Stats+Stories with guests Eric Vance and Olawale Awe. Eric Vance is an associate professor of applied mathematics at the University of Colorado Boulder and the director of LISA (Laboratory for Interdisciplinary Statistical Analysis), where he trains statisticians and data scientists to move between theory and practice to collaborate with domain experts to apply statistics to transform evidence into action. He is the global director of the LISA 2020 Network, which is a network of 30+ statistics and data science collaboration laboratories in 10 countries in Africa, South Asia, and Brazil. He is an Elected Member of the International Statistical Institute and a Fellow of the American Statistical Association.  Olawale Awe is an Elected Member of the International Statistical Institute (ISI) and a Fellow of the African Scientific Institute, USA. He is an Affiliate member of the African Academy of Sciences (AAS) and an immediate past Council Member of the International Society for Business and Industrial Statistics (ISBIS) (2017-2021). He is the First LISA Fellow and presently the Global Vice-President of Engagement and Public Relations in the LISA 2020 Global Network of the University of Colorado, Boulder, USA.His research interests include Computational Statistics, Machine Learning, Time Series Econometrics and Statistics Education. He has served on some important ISI committees and has facilitated several capacity-building workshops and seminars globally. Olawale holds a PhD in Statistics from the University of Ibadan, Nigeria and MBA from Obafemi Awolowo University, Ile-Ife, Nigeria. He is the lead editor (with Kim Love and Eric Vance) of the soon-to-be-released book titled “Promoting Statistical Practice and Collaboration in Developing Countries” by Taylor and Francis Group.

Practical Significance kicks off the new year with its January tradition of featuring the new ASA president. Co-hosts Donna and Ron welcome to the show Dionne Price, the 118th president of the American Statistical Association. Dionne—who is the deputy director of the Office of Biostatistics, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration—shares aspects of her “day” job and her goals for the year ahead. Listen to how an internship sparked her passion for biostatistics and how she puts the FDA and ASA missions ... The post Practical Significance | Episode 25: Meet the New ASA President, Dionne Price first appeared on Amstat News.

Dr. Google is often our first stop when we want to understand a symptom we're experiencing and find out what we can do about it. But the internet is a bottomless well of information–and disinformation. Even when we dig into actual research studies, the conclusions aren't always reliable or consistent, which is why it's hard to get a straight answer on complex issues like hormone therapy. It can be frustrating and confusing, but there are ways you can evaluate the information you find to determine how much confidence to put into any given conclusion, which is where this week's guest biostatistician Leslie McClure comes in. Leslie explains how to do your own research and how to assess the information you find, including what makes a good study, causation versus correlation, features of information you can trust, and much more. Leslie also talks about her own menopause experience, which started when she was 39 and has continued for 9 years and how it has impacted her own training and fitness. Leslie is the Chair of the Department of Epidemiology and Biostatistics at Drexel University's Dornsife School of Public Health, where she works with other scientists to help them better formulate, design, analyze, and present their science. She is currently the Director of the Coordinating Center for the Diabetes LEAD Network, and the Director of the Data Coordinating Center for the Connecting the Dots: Autism Center of Excellence. Dr. McClure is a Fellow of the American Statistical Association, the Society for Clinical Trials, and of the American Heart Association.Join us for our first-ever Feisty Menopause Performance Retreat at Lake Nona atFeistymenopause.com/retreat**Support the Podcast** InsideTracker: 20% off at insidetracker.com/feistyPrevinex: 15% off your first order with code HITPLAY at https://www.previnex.com/ Bonafide: 20% off your first purchase when you subscribe to any product with code HITPLAY at hellobonafide.com/hitplayNutrisense: Go to nutrisense.io/hitplay for $30 off any subscription to the CGM programThis podcast uses the following third-party services for analysis: Podsights - https://podsights.com/privacyChartable - https://chartable.com/privacy

Max Kuhn is a software engineer at RStudio. He is currently working on improving R's modeling capabilities and maintains about 30 packages, including caret. He was a Senior Director of Nonclinical Statistics at Pfizer Global R&D in Connecticut. He was applying models in the pharmaceutical and diagnostic industries for over 18 years. Max has a Ph.D. in Biostatistics. He, and Kjell Johnson, wrote the book Applied Predictive Modeling, which won the Ziegel award from the American Statistical Association, which recognizes the best book reviewed in Technometrics in 2015. Their second book, Feature Engineering and Selection, was published in 2019 and his book with Julia Silge, Tidy Models with R, was published in 2022. Episode Notes Website at RStudio: https://www.rstudio.com/authors/max-kuhn/Twitter: https://twitter.com/topeposGithub: https://github.com/topepo R Packages:autoMLcaretQuartoRMarkdowntidymodelstidyverse Books from Max:Tidy Modeling with R: A Framework for Modeling in the TidyverseApplied Predictive ModelingFeature Engineering and Selection R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Garrett Grolemund and Hadley Wickham Related Episodes Episode #225: Julia SilgeEpisode #212: Dr. Cedric SchererEpisode #210: Dr. Tyler Morgan-WallEpisode #207: Tom MockEpisode #150: Learning REpisode #69: Hadley Wickham iTunes

Today we feature an exciting conversation with Dr. Mary Gray, Distinguished Professor of Mathematics and Statistics at American University in Washington DC. Dr. Gray earned her PHD in mathematics from the University of Kansas and her JD from the Washington College of Law at American University. As a statistician and lawyer, Dr. Gray's areas of research focus on applications of statistics to human rights, economic equity and education. She is the founder of the Association for Women in Mathematics, a fellow of the American Statistical Association and the American Association for the Advancement of Science, and a recipient of the Presidential Award for Excellence in Science, Engineering and Mathematics Mentoring. She has authored two books and over eighty articles. In this conversation, you will learn about the power of an effective undergraduate advisor, about recognizing and caring for critical issues long before they gain national attention, about working with others to effect change, and about how a gift of gratitude from a student led to an extensive collection of owls.

Guido W. Imbens, along with David Card and Joshua Angrist, shared the 2021 Nobel Prize in Economics for “methodological contributions to the analysis of causal relationships”. In 2017 he received the Horace Mann medal at Brown University. An honor shared by your host Professor Brian Keating. He is The Applied Econometrics Professor of Economics at the Stanford Graduate School of Business since 2012, and has also taught at Harvard University, UCLA, and UC Berkeley. He holds an honorary degree from the University of St Gallen. He is also the Amman Mineral Faculty Fellow at the Stanford GSB.  Imbens specializes in econometrics, and in particular methods for drawing causal inferences from experimental and observational data. He has published extensively in the leading economics and statistics journals. Together with Donald Rubin he has published a book, "Causal Inference in Statistics, Social and Biomedical Sciences”. He is a fellow of the Econometric Society, the Royal Holland Society of Sciences and Humanities, the Royal Netherlands Academy of Sciences, the American Academy of Arts and Sciences, and the American Statistical Association. He holds an honorary doctorate from the University of St. Gallen. In this episode, Professor Imbens give his lecture on his Nobel Prize-winning thesis. See the video with the slides here: https://youtu.be/X632K3n8PPI 00:00:00 Intro 00:04:23 Origin of the book Causal Inference in Statistics, Social and Biomedical Sciences 00:10:23 Define what you mean by the credibility revolution and what does it take to create a revolution in economics? 00:15:50 Are we in a “reproducibility crisis” in science and what can we do about it? 00:20:18 How should education and pedagogy be changed to meet the credibility challenge? 00:27:40 What is a day in your life like? 00:34:48 How has winning a Nobel Prize impacted you? 00:43:30 Guido's Nobel Prize Thesis Lecture Begins: The Critical Concepts in Causality 00:43:50 Guido's academic journey. 00:47:50 Correlation is not causality 00:53:00 Statistical traditions 00:55:30 Econometrics 01:05:00 Examples 01:38:22 End of lecture slides    01:38:00 Final four existential questions. 01:39:25 What would you put in your ethical will? 01:45:23 What is the greatest accomplishment in your field that should be preserved for posterity? 01:50:00 What have you changed your mind about? 01:54:25 What advice would you give your younger self to go into the impossible? Learn more about your ad choices. Visit megaphone.fm/adchoices

Guido W. Imbens, along with David Card and Joshua Angrist, shared the 2021 Nobel Prize in Economics for “methodological contributions to the analysis of causal relationships”. In 2017 he received the Horace Mann medal at Brown University. An honor shared by your host Professor Brian Keating. He is The Applied Econometrics Professor of Economics at the Stanford Graduate School of Business since 2012, and has also taught at Harvard University, UCLA, and UC Berkeley. He holds an honorary degree from the University of St Gallen. He is also the Amman Mineral Faculty Fellow at the Stanford GSB.  Imbens specializes in econometrics, and in particular methods for drawing causal inferences from experimental and observational data. He has published extensively in the leading economics and statistics journals. Together with Donald Rubin he has published a book, "Causal Inference in Statistics, Social and Biomedical Sciences”. He is a fellow of the Econometric Society, the Royal Holland Society of Sciences and Humanities, the Royal Netherlands Academy of Sciences, the American Academy of Arts and Sciences, and the American Statistical Association. He holds an honorary doctorate from the University of St. Gallen. In this episode, Professor Imbens give his lecture on his Nobel Prize-winning thesis. See the video with the slides here: https://youtu.be/X632K3n8PPI Connect with me:

Demographers study the way populations change. The things they might focus on include births and deaths, living conditions, and age distributions. In the United States, population changes are tracked nationally by the Census Bureau. A conversation with the retired chief demographer of the U.S Census Bureau with Howard Hogan is the focus of this episode of Stats+Stories. Hogan is the former chief demographer of the U.S. Census Bureau and studied at Princeton's Office of Population Research, and its School of Public Affairs. He then spent two years teaching at the University of Dar es Salaam and working on the Tanzanian census. He joined the Census in 1979. He worked on household surveys, business surveys, and the population census. He led the statistical design of the 2000 Census. He served as an expert witness in Utah v Evans, in which the Supreme Court considered the use of imputation in the Census 2000. He served as Associate Director for Demographic Programs and later as Census Bureau's Chief Demographer. He taught as an Adjunct Professor at the Department of Statistics of George Washington University. He is an Honorary Fellow of the American Statistical Association. He was awarded the 2018 Jeanne E. Griffith Mentoring Award. He retired from federal service in 2018.

In February 2022, the ASA Board of Directors approved revisions to the Ethical Guidelines for Statistical Practice, and this episode is a behind-the-scenes look at that process. Committee on Professional Ethics members Jing Cao and Stephanie Shipp join us to discuss their evolving understanding of the ethical practice and the two-year endeavor by the Committee on Professional Ethics to complete the revisions. They share the process by which the committee approached the revisions, the rationale for adopting specific terminology, and the decision to add an appendix to the guidelines. During the ... The post Practical Significance | Episode 22: What's in the 2022 Ethical Guidelines for Statistical Practice? first appeared on Amstat News.

In the early 1900s, Imogene Rechtin started a crusade to get people to stop kissing socially as a way to stop disease spread. Her argument was sound, but she was largely dismissed as being uptight. Research: “Health Society Bars Kisses.” The Taney Country Republican (Forsyth, Missouri). June 15, 1911. https://www.newspapers.com/image/legacy/859865029/?terms=Imogene%20Rechtin&match=1 “World's Health Organization Waging War Against Kissing.” The Evening-Times Star and Almeda Daily Argus.” Feb 23, 1911. https://www.newspapers.com/image/legacy/607117745/?terms=Imogene%20Rechtin&match=1 “An Assault on Kissing.” The Washington Post. Nov. 22, 1908. https://www.newspapers.com/image/legacy/28961790/?terms=%22kiss%22&match=1 “Declares Kiss Must Go.” Herald and Review. Decatur, Illinois. Nov. 27, 1908. https://www.newspapers.com/image/legacy/92535138/?terms=%22Declares%20Kiss%20Must%20Go%22&match=1 “Woman Doctor Says Kissing In Unseemly.” The Washington Times. Nov. 22, 1908. https://www.newspapers.com/image/legacy/80711073/?terms=%22kissing%20unseemly%22&match=1 Patterson, Ethel Lloyd. “Kiss is Under Ban of ‘Brains' in Quaker City.” Oakland Tribune. Nov. 30, 1908. https://www.newspapers.com/image/legacy/76453147/?terms=%22Kiss%20is%20Under%20Ban%20of%20%27Brains%27%20in%20Quaker%20City%22&match=1 “Fight Against Kissing.” The News (Frederick Maryland). June 17, 1910. https://www.newspapers.com/image/legacy/18372050/?terms=%22Fight%20Against%20Kissing%22&match=1 “To Kiss or Not to Kiss.” The San Francisco Call. July 31, 1910. Accessed through the National Endowment for the Humanities. https://chroniclingamerica.loc.gov/lccn/sn85066387/1910-07-31/ed-1/seq-16/ “Antikisser? Pshaw!” The Washington Post. June 29, 1910. https://www.newspapers.com/image/legacy/31555929/ “MORTALITY STATISTICS:1910.” Department of Commerce and Labor Bureau of the Census.” 1912. https://www.cdc.gov/nchs/data/vsushistorical/mortstatbl_1910.pdf Dublin, Louis I. and Jessamine Whitney. “On the Costs of Tuberculosis.” Quarterly Publications of the American Statistical Association , Dec., 1920, Vol. 17, No. 132 (Dec., 1920), pp. 441-450. https://www.jstor.org/stable/pdf/2965239.pdf “Cincinnati Woman in Fight Against Kissing.” The Tribune. Aug. 10 1910. https://www.newspapers.com/image/legacy/157436476/?terms=%22Fight%20Against%20Kissing%22&match=1 Last, John. “The Woman Who Fought to End the ‘Pernicious' Scourge of Kissing.” Smithsonian. May 31, 2022. https://www.smithsonianmag.com/history/the-woman-who-campaigned-against-the-pernicious-scourge-of-kissing-180980141/ Tesh, Sylvia. “POLITICAL IDEOLOGY AND PUBLIC HEALTH IN THE NINETEENTH CENTURY.” International Journal of Health Services, vol. 12, no. 2, 1982, pp. 321–42. JSTOR, http://www.jstor.org/stable/45130380 Baldwin, Peter C. “Dangers that Lurk in a Kiss: Quarantining the American Mouth, 1890–1920.”  Journal of Social History. Volume 55, Issue 3, Spring 2022, Pages 647–667. https://doi.org/10.1093/jsh/shab014 See omnystudio.com/listener for privacy information.

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. New episodes of Stats+Stories is returning next week.

Since the summer of Black Lives Matter in 2020, institutions all over the U.S. have been exploring their pasts. In order to understand how they may have contributed to or helped perpetuate systemic racism. Universities, private businesses, and non-profits have all been working to try to understand what it means to be Anti-Racist. The American Statistical Association launched an Anti-Racism Task Force to explore this very thing, and that's the focus of this episode of Stats+Stories with guests Dr. Adrian Coles and Dr. David Marker. Dr. Coles is an Associate Director of Biostatistics at Bristol Myers Squibb. He is a collaborative researcher who specializes in the design and implementation of clinical trials and the interpretation of clinical trial data to facilitate the assessment of benefit/risk for promising pharmaceutical innovations. He is also a subject matter expert in diversity, equity, and inclusion and chairs the American Statistical Association's Committee on Minorities in Statistics as well as the organization's Antiracism Taskforce. Dr. Marker is a senior statistician who recently retired after 37+ years at Westat. He is continuing to consult on topics of personal interest. He has worked on studies in the fields of public health, environmental pollution, homelessness, voting rights, and many others. He recently served as co-chair of the American Statistical Association's Anti-Racism Task Force. Dr. Marker is an internationally recognized consultant in total quality management, having advised the Swedish, Norwegian, Finnish, South African, Dutch, and Danish Governments on improving the quality of their data collection activities. He has also appeared as an expert witness before Federal, state, and local governments and on voting rights and language-minority rights before Federal, State, and Provincial courts. Dr. Marker is a Fellow of the ASA and American Academy for the Advancement of Science (AAAS), and an Elected member of the International Statistical Institute. He will receive a Founders Award from the ASA at this summer's Joint Statistical Meetings.

Practical Significance co-hosts Donna LaLonde and Ron Wasserstein have known each other for more than 30 years—since their days at Washburn University in Topeka, Kansas. In 2015, he invited her to join the American Statistical Association as the director of strategic initiatives and outreach. This month, he turns the tables on her, providing a fantastic opportunity to get to know her a little better. Donna discusses what she sees as the ASA's biggest challenges, including “feeling connected.” She also talks about the various opportunities for our community in the education realm ... The post Practical Significance | Episode 20: Ron Turns the Tables on Donna! first appeared on Amstat News.

Most articles that appear in academic journals are kind of mundane in that they're extending the work of scholars who have come before, or sometimes taking an old theory in a new direction. There are those moments however, when a piece of research holds the possibility of fundamentally remaking a field. How should those articles be handled? What's the ethical way to review such research? That's the focus of this episode of Stats and Stories with guest Andrew German. Andrew Gelman (@StatModeling) is a professor of statistics and political science, and director of the Applied Statistics Center at Columbia University. His research interests include voting behavior and outcomes, campaign polling, criminal justice issues, social network structure, and statistical and research methods. He has received the Outstanding Statistical Application award three times from the American Statistical Association, the award for best article published in the American Political Science Review, and the Council of Presidents of Statistical Societies award for outstanding contributions by a person under the age of 40. Timestamps Could you just describe what a big if true article is? (1:37), Editor motivations and making a splash (9:00), How can reviewers be better? (12:47), Attributing credit in this new post publishing review system (15:21), Why you felt compelled to start your ethics article (18:43), Changing thoughts? (25:03)

Michael Friendly is a Fellow of the American Statistical Association, a Professor of Psychology, founding Chair of the graduate program in Quantitative Methods at York University, and an Associate Coordinator with the Statistical Consulting Service. He received his doctorate in Psychology from Princeton University, specializing in Psychometrics and Cognitive Psychology. In addition to his research interests in psychology, Professor Friendly has broad experience in data analysis, statistics, and computer applications. He is the author of Discrete Data Analysis with R: Visualization and Modeling Techniques four Categorical and Count Data. He is also the author of SAS for Statistical Graphics, 1st Edition and Visualizing Categorical Data, both published by SAS Institute, and an Associate Editor of the Journal of Computational and Graphical Statistics and Statistical Science His recent work includes the further development of graphical methods for categorical data and multivariate linear models, as well as work on the history of data visualization. Howard Wainer is an independent statistician and author with experience in educational testing and data visualization. He received his PhD from Princeton University in 1968. He has taught at The University of Chicago, Princeton University and the Wharton School of the University of Pennsylvania. He was employed by the Educational Testing Service from 1980 until 2001 and was the Distinguished Research Scientist at the National Board of Medical Examiners from 2001 until 2016. He is a fellow of the American Statistical Association and American Educational Research Association. Episode Notes Michael Friendly and Howard Wainer, A History of Data Visualization & Graphic Communication Michael Friendly GitHub | https://friendly.github.io/HistDataVis/ Milestones Project: https://datavis.ca/milestones/ Michael Friendly Site | https://www.datavis.ca/ John W. Tukey, Exploratory Data Analysis Sandra Rendgen, The Minard System: The Complete Graphics of Charles-Joseph Minard Brit Rusert, Silas Munro, W. E. B. Du Bois's Data Portraits: Visualizing Black America Leland Wilkinson, The Grammar of Graphics Isabel Wilkerson, The Warmth of Other Suns: The Epic Story of America's Great Migration

Michael Friendly is a Fellow of the American Statistical Association, a Professor of Psychology, founding Chair of the graduate program in Quantitative Methods at York University, and an Associate Coordinator with the Statistical Consulting Service. He received his doctorate in Psychology from Princeton... The post Episode #218: Michael Friendly and Howard Wainer appeared first on PolicyViz.

Hollylynne Lee from North Carolina State University discusses articles from the Journal of Statistics and Data Science Education and CHANCE about Advanced Placement (AP) Statistics. Lee, H. S., & Harrison, T. (2021). Trends in teaching Advanced Placement Statistics: Results from a National Survey. Journal of Statistics and Data Science Education, 29(3), 317-327.  https://www.tandfonline.com/doi/full/10.1080/26939169.2021.1965509 Lee, H. S., Vaskalis, Z. T., Stokes, D. J., & Harrison, T. B. (2022). A look into the AP Statistics classroom: Who teaches it and what aspects of statistics do they emphasize? CHANCE, 35(1), 38-47. https://www.tandfonline.com/doi/full/10.1080/09332480.2022.2039028 Consortium for the Advancement of Undergraduate Statistics Education https://www.causeweb.org/cause/ K-12 Resources from American Statistical Association https://www.amstat.org/education/k-12-educators AP Statistics Teacher Community, College Board https://apcommunity.collegeboard.org/web/apstatistics/ Enhancing Statistics Teacher Education through E-Modules http://go.ncsu.edu/esteem Mathematics of Doing, Understanding, Learning, and Educating for Secondary Students https://modules2.com/statistics/ Amplifying Statistics and Data Science in Classrooms  a free online professional learning course where teachers can learn at their own pace and earn CEUs.  https://go.ncsu.edu/amplifystats Invigorating Statistics and Data Science Teaching through Professional Learninga free online professional learning platform launching in 2023.  https://instepwithdata.org List of episodes

We're officially now onto episode 2 of Season 3 - all about methods!Before you turn this off and tune out - hear us out... We know methods are an area that causes most doctoral students to run for the hills, but that's our goal with this season - give some insights on new methods, new data sets, and ways to build your methods toolbox.In our second episode of the season, Ashley and Alex interview Diana Hechavarría. Diana gives us background on how she got involved with the Panel Study of Entrepreneurial Dynamics (PSED) and Global Entrepreneurship Monitor (GEM), advice for how students can get involved with creating these large datasets, and her lessons learned from these projects and her career so far. You can reach Diane through her email at dianah@usf.edu and find the PSED dataset at http://www.psed.isr.umich.edu/psed/home.Some other resources and training that Diana recommends:https://mixtape.scunning.com/http://econ.msu.edu/estimate/index.phphttps://www.reddit.com/r/learnpython/Join your local American Statistical Association! They give great local continuing and professional education sessions from methods experts in your state.We would also like to take a minute to acknowledge our Ukrainian friends in PhD programs and post-doctoral positions across the world. If there is anything we or the ENT Division can do to help you during this time, please reach out to us via our email: tmientpod@gmail.com. We are thinking and praying for all of you during this incredibly difficult time!---------------------------------------------------------------------------------------------TMI is a "for the students, by the students" podcast hosted by the AOM Entrepreneurship Division, where students are able to ask questions and get to know faculty and others in the field.Be sure to follow the AOM ENT Division on their social accounts and be on the lookout for the Twitter Poll to send your questions or comments!Podcast Email - tmientpod@gmail.comENT Division Instagram - https://www.instagram.com/entdivaom/ENT Division Twitter - https://twitter.com/ENTDivAOMENT Division LinkedIn - https://www.linkedin.com/groups/4289160/ENT Division YouTube - https://www.youtube.com/channel/UCW1hLti5A9cUzbmHHRVAwPA

Meet Sears Merritt, Head Technology & Data, MA Mutual Investments. Sears has been recognized as one of the life insurance industry's top 25 innovators under 40 by LIMRA and an academic-industry partner by the American Statistical Association. He is a member of the board at Barings, an advisor to Antara Health and member of the Innovation Committee for the American Council of Life Insurers. Sears is a senior leader with expertise in the areas of data science and analytics as well as enterprise and internet technology. Over the past 15 years, Sears has spent time leading and innovating in numerous industries, including healthcare, telecommunications, and financial services. Sears currently leads MassMutual's technology and data functions, where his teams are focused on modernizing the firm's architecture and bringing digital, cloud-native, data-driven capabilities to bear throughout the company. Sears was also responsible for architecting one of the nation's first regional telehealth networks in Colorado. He holds a Ph.D. in Computer Science, M.S. in Telecommunications, and B.S. in Electrical Engineering from the University of Colorado at Boulder and an M.B.A. from the Sloan School at Massachusetts Institute of Technology. Follow the Insurtech Leadership Podcast airing weekly hosted by Joshua R. Hollander. We give you up-close access and personal insights from the leaders of the fastest-growing #insurtechs and most innovative #insurance carriers and brokers.

In this episode, we discuss a new article with some negative findings relating to administration of antenatal steroids to fetuses who go on to deliver at term. Then we revisit the literature that led to the recommendation for antenatal testing in obese patients. Finally, we discuss manipulation of P-values and the American Statistical Association's statements about the use of P-values and the term "statistical significance." 

Oliver Schabenberger is Chief Innovation Officer at SingleStore. He is a former academian and seasoned technology executive with more than 20 years global experience in data management, advanced analytics and AI. Oliver formerly served as COO and CTO of SAS, where he lead the design, development and go to-market-effort of massively scaleable analytic tools and solutions and helped organisations become more data driven. He is a fellow of the American Statistical Association, has co-authored three books, and earned Ph.D and M.S. degrees from Virginia Tech. Sponsor Information Visit www.yourheights.com and use acuriouslife10 for a 10% discount. Interview transcript Hadley: [00:00:00] Welcome to the show. Oliver. It's an absolute honor to have you on.  [00:00:04] Oliver: [00:00:04] Thank you, Hadley. I'm delighted to be here. I'm delighted to be on a curious life. And I'm curious what we're going to talk about in this podcast.  [00:00:13] Hadley: [00:00:13] Well, as you know, like Peter pan shadow, we're going to go off and find the essence of Oliver today. [00:00:20] Oliver: [00:00:20] I'm curious, curious what that essence is. [00:00:23]Hadley: [00:00:23] Let's do it. So, you know, this is a show where we look for a window into the lives of our guests. What makes you tick and, and essentially to understand the essence of you and to understand that the trait of curiosity has impacted your life in Korea. So how we do that is we imagine that we sitting around a campfire sharing stories about our life and tag. [00:00:44] You all right. So where we'll start is where you were born, whether you had siblings, you know, what were your parents like? What was your early life like? And we'll take that all the way through to today and onto tomorrow. But before we do that, the question that I ask all my guests is what does curiosity mean? [00:01:00] Well, I think curiosity is something in eight and all humans to different degrees. To me, it's a quality that relates to exploration, uh, inquisition and learning, you know, the drive to find out about something it's really the pursuit of knowledge. I sometimes call it lifelong learning. Um, but to me, it's about the strive to continuously improve and get. [00:01:26] Better at something. Awesome.  [00:01:28] And do you, do you think that's innate in children and suppressed as you get older or just inmate and the individual? I think it's  [00:01:36] Oliver: [00:01:36] innate in the individual. Um, but I think you can suppress it and, and you could, could block us around it. And I think we should encourage the opposite. [00:01:46] Um, for example, when we, when we look at the qualities we like to see in individuals, we work for, we went from defining skills to emotional quotient, and today it's also something called the adaptability quotient. And that's really the, your ability to ask what if questions instead of what is right. And so what would happen if, what would happen if your top five customers leave you tomorrow? [00:02:14] How would you deal with this? The ability to explore something. Overexploiting something and there is an immediate sense of, okay, what do I have available as technology right now? What have we built in the past? Let's start with that and build on top of that. That's exploitation, right? That's building on what you already know versus, okay. [00:02:35] Let's step back. Let's get, give our curiosity some room to roam and imagine what it would be. And sometimes you start from marketing, you start from scratch and you can actually get you to, to where you need to go faster because you're not encumbered and you're not weighted down  [00:02:54] Hadley: [00:02:54] by preconceptions, I guess. [00:02:56] Oliver: [00:02:56] Yeah. And the things you've built in the past, you know, the assumption that everything I've done before needs to be reflected in what I do now,  [00:03:01] Hadley: [00:03:01] that's true. Would you say...

We discuss Bang the Can Slowly: An Investigation into the 2017 Houston Astros with Ryan Elmore (@rtelmore) and Gregory J. Matthews (@StatsInTheWild). This paper was the winner of the Carnegie Mellon Sports Analytics Conference Reproducible Research Competition in October 2020. Ryan Elmore is an Assistant Professor in the Department of Business Information and Analytics in the Daniels College of Business at the University of Denver (DU). He earned his Ph.D. in statistics at Penn State University and worked as a Senior Scientist at the National Renewable Energy Laboratory prior to DU. He has over 20 peer reviewed publications in outlets such as Journal of the American Statistical Association, Biometrika, The American Statistician, Big Data, Journal of Applied Statistics, Journal of Sports Economics, among others. He is currently an Associate Editor for the Journal of Quantitative Analysis in Sports and recently organized the conference “Rocky Mountain Symposium on Analytics in Sports” hosted at DU. Gregory Matthews completed his Ph.D. In statistics at the University of Connecticut in 2011. From 2011-2014, he was a post-doc in the School of Public Health at the University of Massachusetts-Amherst. Since 2014, he has been a professor of statistics at Loyola University Chicago. He was recently promoted to Associate professor with tenure in March 2020. For additional references mentioned in the show: Tony Adams' (@adams_at) Houston Astros trash can banging data website: http://signstealingscandal.com/ Ryan and Greg's GitHub repository with code and data: https://github.com/gjm112/Astros_sign_stealing The causal effect of a timeout at stopping an opposing run in the NBA by Connor Gibbs (@cgibbs_10), Ryan Elmore, and Bailey Fosdick (@baileyfosdick)

Today, The Annex meets Rob Santos, Chief Methodologist at the Urban Institute and incoming President of the American Statistical Association. We discuss concerns about the politicization of the 2020 Census, and pursuing careers in private research. Special guest co-host Joshua De La Rosa, Senior Data Scientist for the City of New York and Adjunct Lecturer at Queens College's Data Analytics Program. Photo Credit. Public Domain, https://commons.wikimedia.org/w/index.php?curid=1334605

Amy Shi (SAS), Emily Griffith (North Carolina State University), and Elizabeth Mannshardt (EPA) discuss the many activities of the North Carolina Chapter of the American Statistical Association, including a lot of online activities that can be enjoyed even if you aren't in NC. The recording was made on the cusp of COVID...so updated information is posted below. NC ASA Activities NC ASA YouTube Channel: https://www.youtube.com/channel/UCPMPV3vCOY2dZka5ELPBWpA NC ASA Website: https://community.amstat.org/northcarolina/home

Friday on Adams on Agriculture RJ Karney with AFBF gives an update on the details of the disaster aid package, INTL FC Stone's Arlan Suderman gives his market outlook and Ron Wasserstein, Ex. Dir. of the American Statistical Association discusses his concerns with the relocation of ERS and NIFA.