Podcasts about statisticians

Content Warning: Themes of racism, violence, and police brutality are discussed throughout this episode. Please proceed with caution and care. The U.S. has persistently relied on armed law enforcement to enforce traffic laws. However, existing evidence does not support the notion that police traffic enforcement improves public safety. In fact, police traffic enforcement has serious adverse consequences for community health and well-being, with disproportionate impacts on Black communities. Last year, the Thurgood Marshall Institute published a research brief, Safe Roads for All, which found that police traffic enforcement is not associated with safer roads. This episode of Justice Above All builds upon what is discussed in that brief and presents a community-centered public health approach to traffic safety. Our guests come from the transit safety and police reform worlds. Together, they agree that we can reimagine traffic safety in a way that prioritizes public health and eliminates our heavy reliance on policing. Today's host is Dr. Sandhya Kajeepeta, Senior Researcher and Statistician at the Thurgood Marshall Institute. She is in conversation with the following guests: -Kim Saltz: Justice in Public Safety Project Legal Fellow, Legal Defense Fund Amber Sherman: Policy Organizer, Decarcerate Memphis Tiffany Smith: Program Manager, Vision Zero Network For more information on this episode, please visit https://tminstituteldf.org/driving-while-black. This episode was written and produced by Jakiyah Bradley and Dr. Sandhya Kajeepeta. Resonate Recording provided production support.If you enjoyed this episode please consider leaving a review and helping others find it! To keep up with the work of LDF please visit our website at www.naacpldf.org and follow us on social media at @naacp_ldf. To keep up with the work of the Thurgood Marshall Institute, please visit our website at www.tminstituteldf.org and follow us on Twitter at @tmi_ldf.

Aishwary Pawar discusses digital transformation in higher education and using data-driven insights to foster innovation and improve institutional outcomes. Aishwary is a Statistician at Southern Methodist University and he specializes in leveraging data analytics and predictive modeling to enhance student success and retention. Listen for three action items you can use today. Host, Kevin Craine Want to be a guest? https://DigitalTransformationPodcast.net/guest Do you want to be a sponsor? https://www.digitaltransformationpodcast.net/sponsor

Guest, Dr Sreelatha Meleth, EFT Practitioner, Statistician and Coach

There are concerns scrapping the traditional census won't deliver the desired results. Stats NZ is moving to a system using Government collected admin-data, saying the current five yearly Census is financially unsustainable. Census-style questions will still be asked in much smaller annual surveys looking at a small fraction of the population. Former national statistician Len Cook told Mike Hosking data-wise, this won't cut it. He says admin-data comes from about a dozen different sources, none of them complete. LISTEN ABOVE See omnystudio.com/listener for privacy information.

The strong message I continue to get from my household surveys is that many households feel trapped in the jaws of ever higher prices, and see a real devaluation in their effective income, which means they are going backwards. Many are working more hours and multiple jobs to try and get by. Yet the Government … Continue reading "Reality Versus The Statisticians: Yes You Do Have An Real Income Recession!"

In this special episode, I'm sharing the recording of a webinar I co-hosted with Cytel on March 20, 2025. I was joined by an expert panel of leaders in statistics and clinical development: Yannis Jemiai, Flaminia Chiesa, and Benjamin Piske. Together, we explored how the role of statisticians is rapidly evolving in response to industry changes, data innovations, and AI-driven transformation. This rich discussion dives into what it means to lead as a Clinical Data Scientist today—and why statisticians are uniquely positioned to influence strategy, innovation, and decision-making across the healthcare and pharmaceutical sectors.

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.

Lou Stagner is a statistician, coach, and one of the most influential voices in the golf industry. He joins The Mental Golf Show. Topics and Timestamps: 00:00 Emotional Control in Golf 05:07 Statistics and Anger 10:23 How much should we be thinking about stats? 12:56 The Importance of Data Tracking 14:49 Playing with Confidence vs. Emotion 19:15 Stat Tracking Apps 22:39 Does Lou practice what he preaches? 39:48 What to do when you don't know where the ball is going 41:31 How does Lou manage his own anger on the course? 46:20 Lou's Pre-shot Strategy Routine 48:25 What should our stat benchmark be? 55:33 Structuring Practice with Swing Changes 58:09 Does playing shorter tees help or hurt your handicap? 1:00:13 How can Mike & Eli of Chasing Scratch get to scratch? 1:04:25 Does Statistics Actually Help You Improve? 1:06:19 Lou's Role as Statistician at Princeton Golf 1:09:45 Lou Stagner ----- Lou's newsletter: Lou Stagner Golf Lou on X Lou on Instagram -----

As any Statistician on a juicy consulting wage from the NRL will tell you, records are made to be invented, and it's with this gusto that our heroes grapple with yet another glorious 'crowd record' declaration issued from Pyongyang. That, alongside the scheduling of a double header to take on the British and Irish Lions tour opener, assures us all the leadership coming from HQ remains- as ever- supreme.The game is feeling a bit 1998 at the moment, with unlimited tackles AND the reinstated presence of the Bears. A reverent look at Bears past unfolds, as our grizzled heroes pontificate their wisdom, while subtly referencing Wikipedia articles.Expansion is on the mind and in the gullet, from PNG to Perth, and next (we're pretty sure) the announcement of a team for the league loving province of Daugavpils in Latvia.Forward passes, high tackles, sin bins and tipping shit shows. Some harsh but fair calls on QLD Origin selections, while Johnny invites fellow male model James O'Connor back into the Wallaby fold- only to be met by a wall of disinterest from Joel, who is pretty sure no one would really bat an eyelid if he became an All Black. Our heroes also briefly mention a few Super rugby scores, amid wide eyed realisations they probably need to watch the games before talking about them. And so, we leave you with an ode to the game:As the whistle blows, and blows and blows and blowsThe spectacle known as rugby league gets more on the nose nose noseBut lets not stop PVL and co from making it grow grow growFrom Perth to Port Moresby...soon we'll have more teams than players to put on a show show show

You haven't heard from anyone like Professor Sir Peter Donnelly before. He is a statistician, professor, a knight of the realm and an entrepreneur. Not only has he given a Ted Talk about statistics that has been viewed more than 1.5 million times, but he is now working on genetics tests that could save lives and change the world by helping people understand how predisposed they are to becoming obese or getting certain illnesses. Donnelly left his career in academia to become the co-founder and chief executive of Genomics, which is developing these genetic tests and is one of the fastest-growing businesses in the UK. But this may only be the start of its story... Hosted on Acast. See acast.com/privacy for more information.

As someone who found my way into statistics late—only halfway through university—I often wonder: what if more young people knew about this path earlier? In this episode, I sit down with two wonderful guests and PSI volunteers, Emma Crawford and Alex Spiers, to explore exactly that: how we, as statisticians and scientists, can inspire the next generation. We talk about the why behind investing in STEM outreach, share personal stories, and get into the practical steps you can take—whether you want to volunteer at a school, present virtually, or simply start a conversation with a student.

Send us a textDid you know that statisticians are the secret heroes behind your Netflix recommendations, the cool Instagram filters you love, and even predicting which cricket team might win the World Cup? That's right! These number ninjas are basically the fortune tellers of the digital age, but with actual science backing them up!Connect With Kapeel Guptaor Click on the link: http://bit.ly/4jlql8sWhat You May Learn0:00 Introduction1:48 Mission Statement2:06 Scope in India & Abroad5:04 Nature of Work7:20 Skills & Educational qualifications required11:14 Salary in India and around the world13:26 Conclusion15:00 Call to actionSupport the show

John McGready discusses how to prepare future statisticians to succeed in the era of AI. 

In this episode, I'm sharing three personal stories where soft skills—or better yet, human skills—made a huge difference in my work as a statistician. Whether it was building trust to access critical data, presenting results in a way that truly resonated, or negotiating a fair contract, these experiences reminded me how essential these skills are alongside our technical expertise.

Clement Manyathela speaks with Risenga Maluleke, the Statistician-General of South Africa and Head of Statistics South Africa, about the ongoing crisis of crime in the country. Maluleke clarifies that crime in South Africa can be divided into two categories: reported crime, which is recorded in SAPS (South African Police Service) administrative records, and unreported crime – those incidents that are experienced but never reported to the police. He highlights some of the most frequently reported crimes in South Africa, including: Murders Sexual offences, with around 84% of these incidents reported Housebreaking (when the victim is not home) and home robberies "Housebreaking remains the highest crime in South Africa," Maluleke says. He further explains that a key indicator of a community's safety is whether people feel comfortable walking freely at night, confident in their security. Unfortunately, he notes, this is not the reality in South Africa, particularly for women. Maluleke also points out that many South Africans tend to trust the police more than the judicial system. He explains that, in the court of law, the process begins with the defendant pleading, followed by the trial. In contrast, in a traditional setting, the individual is first tried, and only then is the plea made. Thank you for listening to The Clement Manyathela podcast. Listen live - The Clement Manyathela is broadcast on weekdays from 09:00 am to noon on 702. See omnystudio.com/listener for privacy information.

President Trump recently cited from the oval office statistics about the state of the U.S. education system. What he didn't say is that we know about student achievement levels thanks to data gathered by the Education Department itself. One of the government's most vital activities is the gathering of reliable statistics to guide policy. My next guest says the quality of national statistics has become a concern. Consultant Nancy Potok is the former chief statistician of the United States, and she joins me now. Learn more about your ad choices. Visit podcastchoices.com/adchoicesSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

President Trump recently cited from the oval office statistics about the state of the U.S. education system. What he didn't say is that we know about student achievement levels thanks to data gathered by the Education Department itself. One of the government's most vital activities is the gathering of reliable statistics to guide policy. My next guest says the quality of national statistics has become a concern. Consultant Nancy Potok is the former chief statistician of the United States, and she joins me now. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Blue ballers, we're back and we're keeping it a buck reviewing our 3-goal, 2-1 victory over the Revs at Yankee Stadium. Jake & Trey talk the perennially underrated Keaton Parks, Martinez' most recent clinical finish in front of goal, the dawn (?) of Julian, and much more. Plus: vibe checks in the guts of Yankee Stadium, Outfield reports on a different big bird headed to NYCFC, and the eerie quiet of a drumless supporter's section. Then: a quick n' dirty precap of a Crew that's looking primed for a rebuild season — but call us Sluggo because no matter what, Nancy still poses a threat. Strap on your safety goggles — you've got Blue Balls.

We welcome William M. Briggs, known as Statistician to the Stars, to talk about feminism in the workplace and academia, as well as what the data can really show us. He explores the victim mindset and how we have built institutions around perceived oppression. Show Notes US Atty Ed Martin Tells Georgetown Law the DOJ will reject any applicant for a fellowship or internship program from a school with DEI programs | The Post Millennial  “Feminism Is A Disease Of The Rich” – William M. Briggs Jobs for the Girls (PDF) Letter to Women (June 29, 1995) | John Paul II  St. John Paul II's ‘Letter to Women' at 25: ‘Feminine Genius' Affirmed| National Catholic Register  Everything You Believe Is Wrong by William M Briggs The Church and the Culture War by Joyce A. Little Ungodly Rage: The Hidden Face of Catholic Feminism by Donna Steichen iCatholic Mobile The Station of the Cross Merchandise - Use Coupon Code 14STATIONS for 10% off | Catholic to the Max Read Fr. McTeigue's Written Works! "Let's Take A Closer Look" with Fr. Robert McTeigue, S.J. | Full Series Playlist Listen to Fr. McTeigue's Preaching! | Herald of the Gospel Sermons Podcast on Spotify Visit Fr. McTeigue's Website | Herald of the Gospel Questions? Comments? Feedback? Ask Father!

In this episode of The Effective Statistician, I dive into a crucial skill—building influence within a clinical trial team. As statisticians, we often need to negotiate timelines, advocate for better analysis methods, and ensure clear communication across teams. The stronger our influence, the more impact we can have on study outcomes and, ultimately, patients. I break down the key pillars of trust—care, character, and competence—and share practical strategies to help you collaborate effectively, align team priorities, and proactively tackle challenges. Tune in and start building your influence today!

Bob Kurland's Slide LinksIn all protein functions, parts of the proteins bind loosely to other parts of the protein and thus form appropriate structures that are essential to their function. This is shown very nicely in this TED YouTube video, by Professor Ken Dill https://www.youtube.com/watch?v=zm-3kovWpNQ Here is another nice YouTube video showing protein flexibility https://www.youtube.com/watch?v=yZ2aY5lxEGE Webinar TitleThe Anthropic Principle: “Are We Special?”--Did God make our “Goldilocks Universe” for man?Abstract The universe in which we live and came to be is not ordinary, but unusual. As the Church Lady in Saturday Night Live of old would say, “Now, isn't that special!” Or is it? Some scientists would agree with Roger Penrose – that if it weren't special, we wouldn't be here to remark on it. Many other scientists and philosophers would agree with Thomas Nagel that an explanation giving only the result is not an explanation. (And, of course, if it is special, then there is the implicit conclusion that this is so because of a Creating Intelligence, which we Catholics recognize as the Trinitarian God.) In my presentation I will discuss some of the so-called “anthropic coincidences” necessary for carbon-based life. Although some examples from cosmology and particle physics will be included, I'm going to focus on the wonderful parts of chemistry and molecular biology, processes that point to the hand of a Creating Intelligence. And of course the prophets of the Old Testament and saints of the early Church knew this all along, without the benefit of science. Dr. Robert Kurland, a convert to Catholicism in 1995, is a retired physicist who has applied magnetic resonance to problems of biological interest in his research (web search: “Kurland-McGarvey Equation”). Dr. Kurland is a graduate of Caltech (BS, 1951, “with honor”) and Harvard (PhD, 1956). His scientific career at Carnegie-Mellon, SUNY/AB, Cleveland Clinic, Geisinger Medical Center, has focused on biological applications of magnetic resonance, including MRI. Since his conversion to Catholicism, he has tried to spread the message that there's no war between Catholic teaching and science.Respondent: William M. Briggs, PhD Against the Anthropic Principle Dr. William M. Briggs, the Statistician to the Stars, has a background in statistics, philosophy, meteorology, and cryptography. Born in Detroit, he left the city when it was at its peak, which some might jokingly suggest led to its decline. Briggs holds a PhD in Mathematical Sciences and an MS in Atmospheric Physics, and has served in various roles including professor, consultant, and statistician. He is known for his work in probability and statistics, as well as his cultural commentary on various social and scientific issues.

Nate Silver (On the Edge, The Signal and the Noise, Baseball Between the Numbers) is a statistician, author, and founder of FiveThirtyEight. Nate joins the Armchair Expert to discuss his youthful aspirations for a starter job as US president with a promotion to baseball commissioner, how code-switching as a gay man of his cohort can translate to success, and defying the odds by quitting his first job to play online poker. Nate and Dax talk about learning statistical models as a hobbyist because academics don't have the street smarts, the phenomenon of sore winners in tech, and the adage that the more shabbily you show up for your first meeting the more trustworthy you are. Nate explains that the dopamine felt especially by men during a winning streak is effectively a narcotic, how figures like Sam Bankman-Fried are kind of degenerate gamblers at heart, and why the new alpha move in industry is just to trust your gut.Follow Armchair Expert on the Wondery App or wherever you get your podcasts. Watch new content on YouTube or listen to Armchair Expert early and ad-free by joining Wondery+ in the Wondery App, Apple Podcasts, or Spotify. Start your free trial by visiting wondery.com/links/armchair-expert-with-dax-shepard/ now.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Clement Manyathela speaks to Risenga Maluleke, the Statistician-General and Head of Statistics South Africa (Stats SA) about the institution’s responsibility in collecting, analyzing, and disseminating official statistics to inform policy direction.See omnystudio.com/listener for privacy information.

Welcome to another episode of The Effective Statistician podcast! Today, I tackle the essential skill of negotiation—a tool we statisticians use every day, often without realizing it. We negotiate when we push for better study designs, adjust project timelines, or secure resources like training and conference budgets. Beyond work, we negotiate in our personal lives with family, friends, or vendors. In this episode, I share proven strategies to prepare for negotiations, uncover interests behind positions, and confidently advocate for yourself and your work. Mastering these skills empowers you to drive success and create a bigger impact as a statistician. Let's jump in!

By River Mitchell | Since graduating college and becoming a statistician, Marcellus Bowie has dreamed about being the statistician for the Hoophall Classic. Now, he's living out his dream for the fourth year in a row and using his platform from his business, Legit Stats, to help the younger generation find jobs and jumpstart their career.

Welcome to a brand-new year and an exciting new episode of The Effective Statistician! I'm thrilled to kick off 2025 by sharing some incredible news—I've written a book! After years of planning, false starts, and a lot of learning along the way, How to Be an Effective Statistician is finally complete and will officially launch on January 15th. In this episode, I take you behind the scenes of how the book came to life. I'll share why I wrote it, how it's structured, and the practical insights it offers for statisticians, data scientists, and other quantitative professionals. Whether you're looking to improve communication, manage challenges, or simply be more effective in your role, this book was designed with you in mind. I'll also give you all the details about the book launch party, where we'll celebrate, learn, and connect. Join me as I reflect on this journey and what it means for our community!

Stephen Grootes speaks to Pali Lehohla, former statistician-general of South Africa, to reflect on his career and share personal anecdotes about his relationship with money along the way.See omnystudio.com/listener for privacy information.

This is a preview of The Huddle Breakdown Interview available at www.huddlebreakdown.comWe are thrilled to welcome Sir David Spiegelhalter to The Huddle Breakdown in the second installment of ‘The Huddle Breakdown Interview'. He talks to Alan and James in a wide ranging conversation including the role of luck in football.Professor Sir David Spiegelhalter FRS OBE is the closest thing the world of statistics has to a national treasure. His new book, The Art of Uncertainty: Living with Chance, Ignorance, Risk and Luck is an engaging and informative guide to living with uncertainty in a world that makes it inevitable. He is Chair of the Winton Centre for Risk and Evidence Communication in the Centre for Mathematical Sciences at the University of Cambridge. His bestselling book, The Art of Statistics, has been published in 11 languages. His current roles are as Non-Executive Director, UK Statistics Authority; Mathematical Futures expert board of the Royal Society; Member of the Statistics Expert Group for the Infected Blood Inquiry, 2019 – 2024; and Advisor; NHS Maternity and Neonatal Outcomes Group. Hosted on Acast. See acast.com/privacy for more information.

This piece was recorded at the The 7th Collaborative Clinical Trials in Anaesthesiology Conference, Prato, Italy. The discussion centers around the roles and contributions of bio statisticians in clinical trials. Presented by Kate Leslie and Andy Cumpstey with their guests, Jessica Kasza, Professor of Biostatistics, Data Analytics/Modelling and Health Economics at Monash University and Elizabeth Ryan, Research Fellow at Monash University.

(00:00-19:59) The guys reiterate what a win will do for them on Sunday. Then, John has some more stats for the game. (19:59-31:49) Another person is harassing Jason Kelce. (31:49-39:21) John has some more interesting stats. Then, more texts.

Gabe of Libre Solutions Network and Mathew Crawford discuss all things bitcoin and how it seems the plan was to have Trump installed and push crypto. They comment on the role of the U.S. military, influencers, BlackRock, the potential future role of bitcoin erasing U.S. debt and functioning as a banking currency, digital gold, tulip mania, viewing the technology as neutral, bitcoin's future price, and more! Watch on BitChute / Brighteon / Rokfin / Rumble / Substack Geopolitics & Empire · Gabe (LSN) & Mathew Crawford: Bitcoin's Rise & It's Future Role...Tulip Mania or Digital Gold? #491 *Support Geopolitics & Empire! Donate https://geopoliticsandempire.com/donations Consult https://geopoliticsandempire.com/consultation Become a Member https://geopoliticsandempire.substack.com Become a Sponsor https://geopoliticsandempire.com/sponsors **Visit Our Affiliates & Sponsors! Above Phone https://abovephone.com/?above=geopolitics easyDNS (use code GEOPOLITICS for 15% off!) https://easydns.com Escape The Technocracy course (15% discount using link) https://escapethetechnocracy.com/geopolitics LegalShield https://hhrvojemoric.wearelegalshield.com Sociatates Civis (CitizenHR, CitizenIT, CitizenPL) https://societates-civis.com Wise Wolf Gold https://www.wolfpack.gold/?ref=geopolitics Gabe (Libre Solutions) Websites Libre Solutions Network https://libresolutions.network Libre Solutions Substack https://libresolutionsnetwork.substack.com About Gabe (Libre Solutions) Gabe is working to share and teach the tools and tactics needed to oppose digital tyranny by encouraging others to start their journey in building up their own digital autonomy. Mathew Crawford Websites Substack https://roundingtheearth.substack.com X https://x.com/EduEngineer About Mathew Crawford Mathew Crawford is an Educator, Entrepreneur, Statistician, Finance Specialist, and Founder of Rounding the Earth. *Podcast intro music is from the song "The Queens Jig" by "Musicke & Mirth" from their album "Music for Two Lyra Viols": http://musicke-mirth.de/en/recordings.html (available on iTunes or Amazon)

Gabe of Libre Solutions Network and Mathew Crawford discuss all things bitcoin and how it seems the plan was to have Trump installed and push crypto. They comment on the role of the U.S. military, influencers, BlackRock, the potential future role of bitcoin erasing U.S. debt and functioning as a banking currency, digital gold, tulip mania, viewing the technology as neutral, bitcoin's future price, and more! Watch on BitChute / Brighteon / Rokfin / Rumble / Substack Geopolitics & Empire · Gabe (LSN) & Mathew Crawford: Bitcoin's Rise & It's Future Role...Tulip Mania or Digital Gold? #491 *Support Geopolitics & Empire! Donate https://geopoliticsandempire.com/donations Consult https://geopoliticsandempire.com/consultation Become a Member https://geopoliticsandempire.substack.com Become a Sponsor https://geopoliticsandempire.com/sponsors **Visit Our Affiliates & Sponsors! Above Phone https://abovephone.com/?above=geopolitics easyDNS (use code GEOPOLITICS for 15% off!) https://easydns.com Escape The Technocracy course (15% discount using link) https://escapethetechnocracy.com/geopolitics LegalShield https://hhrvojemoric.wearelegalshield.com Sociatates Civis (CitizenHR, CitizenIT, CitizenPL) https://societates-civis.com Wise Wolf Gold https://www.wolfpack.gold/?ref=geopolitics Gabe (Libre Solutions) Websites Libre Solutions Network https://libresolutions.network Libre Solutions Substack https://libresolutionsnetwork.substack.com About Gabe (Libre Solutions) Gabe is working to share and teach the tools and tactics needed to oppose digital tyranny by encouraging others to start their journey in building up their own digital autonomy. Mathew Crawford Websites Substack https://roundingtheearth.substack.com X https://x.com/EduEngineer About Mathew Crawford Mathew Crawford is an Educator, Entrepreneur, Statistician, Finance Specialist, and Founder of Rounding the Earth. *Podcast intro music is from the song "The Queens Jig" by "Musicke & Mirth" from their album "Music for Two Lyra Viols": http://musicke-mirth.de/en/recordings.html (available on iTunes or Amazon)

It is World Championship season again! The FIDE World championship takes place from November 25 to December 13, in Singapore. As you probably know, GM Ding Liren will be defending his title against 18 year old wunderkind, GM Gukesh D. This is a unique matchup which features a fast-rising top 5 challenger, and a slumping World Champion who has fallen out of the top 20. Joining me to discuss it are three separate guests in the following order: Top trainer, author, and Ding Liren biographer, GM Davorin Kuljasevic shares his match thoughts. How does Ding's recent play differ from that of “peak Ding?”   Author of From Boy to Man to Challenger: The Fiercest Battles of Gukesh D ,  IM Cyrus Lakdawala on why he thinks Gukesh has a chance to be an all-time great.  Statistician, and co-founder of Chessgoals.com, NM Matt Jensen on the betting markets, the likelihood of a tiebreak, and one key statistical factor that favors Gukesh.  Chatting about the World Championship always gets me excited for the match, and this year is no exception. Timestamps for guests and topics discussed is below.  Thanks to our sponsors, Chessable.com.  If you sign up for Chessable Pro, please use the following link to help support Perpetual Chess: https://www.chessable.com/pro/?utm_source=affiliate&utm_medium=benjohnson&utm_campaign=pro 0:00- Intro- Intro and Match facts  0:06- GM Davorin Kuljasevic joins to discuss GM Ding Liren, as well as his general thoughts on the match.  0:12- What openings might we expect? Which seconds might he be working with?  Mentioned: GM Eugene Perelshteyn's Tweet: https://x.com/EugenePerel/status/1850321592678555941 World Championship bettings odds here: https://sports.bwin.com/en/sports/events/fide-world-championship-2024-15724987 23:00- Thanks to GM Kuljasevic for joining me, you can get his book on GM Ding Liren here 25:00- IM Cyrus Lakdawala joins to discuss GM D Gukesh Mentioned: From Boy to Man to Challenger: The Fiercest Battles of Gukesh D  35:00- Cyrus' match predictions 39:00- Gukesh opening predictions 49:00- Cyrus' upcoming projects  51:00- Statistician and NM Matt Jensen of Chessgoals.com joins to give a statistical preview to the match. 58:00- What is the expected draw rate for this match? What are the odds of a tiebreak?  1:08:00- What is new with Matt's website Chessgoals? Check out the Chcessgoals ourses here, Use the code Ben2024 to save 30%:  https://courses.chessgoals.com/collections/ Check out their podcast  “No Pawn Intended” on the Chessgoals YouTube Channel: https://www.youtube.com/@ChessGoals If you would like to help support Perpetual Chess via Patreon, you can do so here: https://www.patreon.com/c/perpetualchess Learn more about your ad choices. Visit podcastchoices.com/adchoices

Statistician says there's NOT A CHANCE that polls are as close as they sayRemember, remember 5th of Nov — treason & plots from BOTH sidesWhat I Saw at STOP THE STEAL 4 Years AgoElection Aftermath — Will Usual Suspects Start J6 Part DeuxVote if you wish, but don't DEVOTE yourself to party or candidate; WHAT is right, NOT WHO is rightThe 1916 Project - interview with author & filmmaker Seth Gruber The1916Project.comWill AI synthesize a New World Religion?  Bill Gates says we need it.  People from every religion are already training AIPrepping is huge — here's what people are doingIf you would like to support the show and our family please consider subscribing monthly here: SubscribeStar https://www.subscribestar.com/the-david-knight-showOr you can send a donation throughMail: David Knight POB 994 Kodak, TN 37764Zelle: @DavidKnightShow@protonmail.comCash App at: $davidknightshowBTC to: bc1qkuec29hkuye4xse9unh7nptvu3y9qmv24vanh7Money should have intrinsic value AND transactional privacy: Go to DavidKnight.gold for great deals on physical gold/silverFor 10% off Gerald Celente's prescient Trends Journal, go to TrendsJournal.com and enter the code KNIGHTBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-david-knight-show--2653468/support.

Statistician says there's NOT A CHANCE that polls are as close as they sayRemember, remember 5th of Nov — treason & plots from BOTH sidesWhat I Saw at STOP THE STEAL 4 Years AgoElection Aftermath — Will Usual Suspects Start J6 Part DeuxVote if you wish, but don't DEVOTE yourself to party or candidate; WHAT is right, NOT WHO is rightThe 1916 Project - interview with author & filmmaker Seth Gruber The1916Project.comWill AI synthesize a New World Religion?  Bill Gates says we need it.  People from every religion are already training AIPrepping is huge — here's what people are doingIf you would like to support the show and our family please consider subscribing monthly here: SubscribeStar https://www.subscribestar.com/the-david-knight-showOr you can send a donation throughMail: David Knight POB 994 Kodak, TN 37764Zelle: @DavidKnightShow@protonmail.comCash App at: $davidknightshowBTC to: bc1qkuec29hkuye4xse9unh7nptvu3y9qmv24vanh7Money should have intrinsic value AND transactional privacy: Go to DavidKnight.gold for great deals on physical gold/silverFor 10% off Gerald Celente's prescient Trends Journal, go to TrendsJournal.com and enter the code KNIGHTBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-real-david-knight-show--5282736/support.

49ers Forecast Show Hour with Larry Krueger on the Keys to Sunday's showdown with the Chiefs, Interview with the Statistician for the 49ers Radio broadcasts, Chris Babcock and much moreSee omnystudio.com/listener for privacy information.

Mathew Crawford discusses psychological mindwar and the British Empire's long-game at controlling the world. India, cults, and theosophy play important roles in London's global machinations. The networked international hierarchy of the globalists is compartmentalized and at times can seem contradictory as well as have real rivalries. He's looked at Kamala Harris' deep history and uncovered some remarkable connections. Their method of warfare can also be classified as induced schizophrenia and the culture of altered states they push should be considered as minor forms of schizophrenia. Watch on BitChute / Brighteon / Rokfin / Rumble / Substack Geopolitics & Empire · Mathew Crawford: Mindwar, British Empire, & Kamala Harris as Manchurian Candidate #465 *Support Geopolitics & Empire! Donate https://geopoliticsandempire.com/donations Consult https://geopoliticsandempire.com/consultation Become a Member https://geopoliticsandempire.substack.com Become a Sponsor https://geopoliticsandempire.com/sponsors **Visit Our Affiliates & Sponsors! Above Phone https://abovephone.com/?above=geopolitics easyDNS (use promo code GEOPOLITICS for 15% off!) https://easydns.com Expat Money Summit 2024 (use promo code EMPIRE for $100 off the VIP ticket!) https://2024.expatmoneysummit.com/?ac=8cDxEbJw LegalShield https://hhrvojemoric.wearelegalshield.com Wise Wolf Gold https://www.wolfpack.gold/?ref=geopolitics Websites Substack https://roundingtheearth.substack.com X https://x.com/EduEngineer Might Kamala Harris be a Manchurian Candidate? https://roundingtheearth.substack.com/p/might-kamala-harris-be-a-manchurian About Mathew Crawford Mathew Crawford is an Educator, Entrepreneur, Statistician, Finance Specialist, and Founder of Rounding the Earth. *Podcast intro music is from the song "The Queens Jig" by "Musicke & Mirth" from their album "Music for Two Lyra Viols": http://musicke-mirth.de/en/recordings.html (available on iTunes or Amazon)

Mathew Crawford discusses psychological mindwar and the British Empire's long-game at controlling the world. India, cults, and theosophy play important roles in London's global machinations. The networked international hierarchy of the globalists is compartmentalized and at times can seem contradictory as well as have real rivalries. He's looked at Kamala Harris' deep history and uncovered some remarkable connections. Their method of warfare can also be classified as induced schizophrenia and the culture of altered states they push should be considered as minor forms of schizophrenia. Watch on BitChute / Brighteon / Rokfin / Rumble / Substack Geopolitics & Empire · Mathew Crawford: Mindwar, British Empire, & Kamala Harris as Manchurian Candidate #465 *Support Geopolitics & Empire! Donate https://geopoliticsandempire.com/donations Consult https://geopoliticsandempire.com/consultation Become a Member https://geopoliticsandempire.substack.com Become a Sponsor https://geopoliticsandempire.com/sponsors **Visit Our Affiliates & Sponsors! Above Phone https://abovephone.com/?above=geopolitics easyDNS (use promo code GEOPOLITICS for 15% off!) https://easydns.com Expat Money Summit 2024 (use promo code EMPIRE for $100 off the VIP ticket!) https://2024.expatmoneysummit.com/?ac=8cDxEbJw LegalShield https://hhrvojemoric.wearelegalshield.com Wise Wolf Gold https://www.wolfpack.gold/?ref=geopolitics Websites Substack https://roundingtheearth.substack.com X https://x.com/EduEngineer Might Kamala Harris be a Manchurian Candidate? https://roundingtheearth.substack.com/p/might-kamala-harris-be-a-manchurian About Mathew Crawford Mathew Crawford is an Educator, Entrepreneur, Statistician, Finance Specialist, and Founder of Rounding the Earth. *Podcast intro music is from the song "The Queens Jig" by "Musicke & Mirth" from their album "Music for Two Lyra Viols": http://musicke-mirth.de/en/recordings.html (available on iTunes or Amazon)

On this episode of Good Nurse Bad Nurse, Tina explores the shocking case of Lucia de Berk, a nurse who was wrongfully convicted of multiple murders. We are able to see how confirmation bias plays a huge role in her conviction, demonstrating the dangers of making assumptions without questioning them. For our "Good Nurse" segment, Tina gives numerous examples of when statistics were used for good, and how they impacted our society in a positive way!   Join us on Patreon to get ad-free episodes, early access, and more exclusive content! Please support our show by supporting our sponsors below! Thanks to our sponsor Advantis Medical! Visit their website at https://www.advantismed.com/?utm_source=goodnursebadnurse&utm_medium=podcast&utm_campaign=spot1 to learn more about their travel nurse agency and why they're ranked #1 by nurses! Thank you to our sponsor Eko! Please visit them at https://ekohealth.com and use promo code GNBN for $50 off your purchase of the new Littmann Cardiology IV stethoscope with Eko

Comedian Gary Vider joins the show to discuss his podcast “#1 Dad” and his con-man father who inspired it. They also talk about how he got his start in stand-up and the comedians who helped him along in his early days. Next, Leah Knauer returns to read the news including stories about JLo filing for divorce from Ben Affleck, a colonoscopy doctor allegedly not hearing a patient screaming because he didn't have his hearing aid in, a new non-dating app that connects groups of people just for meals, and a survey that found Fresno to have the most chivalrous men in California. Finally, statistician and professional poker player Nate Silver makes his first ACS appearance to talk about his new book “On the Edge: The Art of Risking Everything.” They also talk about his site FiveThreeEight getting bought by Disney and the benefits of leaving the mainstream media in favor of Substack. Then they discuss what Nate's model predicts for the upcoming presidential election and his relationship with Sam Bankman-Fried. For more with Gary Vider: ● Listen to his podcast: #1 Dad ● INSTAGRAM: @garyvider ● TWITTER: @garyvider ● WEBSITE: http://garyvider.com For more with Nate Silver: ● Buy his new book: “On the Edge: The Art of Risking Everything” available everywhere. ● SUBSTACK: Silver Bulletin newsletter ● TWITTER: @natesilver538 Thank you for supporting our sponsors: ● http://SimpliSafe.com/Adam ● http://QualiaLife.com/Adam ● http://OReillyAuto.com/Adam ● Hims.com/ADAM

Tom Haberstroh and Travonne Edwards are coming home! Shaq gave Tom a new title after Adam Lefkoe presented The Big Aristotle with the facts of Tom's article. Jerv joins the show to rejoice in Philly's signing of Paul George, 76ers Positivity and Paper Championships. Chris Paul's next career move will be to feed Victor Wembanyama the rock, Tobias Harris has a self imposed exile in Detroit, Jonas Valancunias inexplicably chose pressure free Washington. Rudy Gobert gives his thoughts on the French Revolution taking place in the NBA. Patreon Exclusive: Is Bronny gonna enjoy playing with his Dad? Jayson Tatum already has plans for Deuce. Produced by Anthony Mayes & John Jervay COUNT THE DINGS MERCH STORE - Check it out here: https://bit.ly/CTDMERCH Sign up for Underdog Fantasy Promo Code DING: https://play.underdogfantasy.com/p-count-the-dings If you want to hear the full OG Pod, check out the Patreon! Join the Count The Dings Patreon for full, ad free episodes, extra Cinephobe content and more at www.patreon.com/CountTheDings Watch the OG Pod on YouTube: https://www.youtube.com/@CountTheDings Learn more about your ad choices. Visit megaphone.fm/adchoices

In this week's “Throwback Thursday / Where are they now?” segment, we hear from a statistician turned fantasy sports strategist, who leverages his knack for numbers to launch an analytics business. Side Hustle School features a new episode EVERY DAY, featuring detailed case studies of people who earn extra money without quitting their job. This year, the show includes free guided lessons and listener Q&A several days each week. Show notes: SideHustleSchool.com Email: team@sidehustleschool.com Be on the show: SideHustleSchool.com/questions Connect on Instagram: @193countries Visit Chris's main site: ChrisGuillebeau.com Read A Year of Mental Health: yearofmentalhealth.substack.com If you're enjoying the show, please pass it along! It's free and has been published every single day since January 1, 2017. We're also very grateful for your five-star ratings—it shows that people are listening and looking forward to new episodes.