Podcasts about David Spiegelhalter

David Spiegelhalter is one of the world's most important figures in statistics. He's an emeritus professor of statistics in the Centre for Mathematical Studies at the University of Cambridge and he's the author of The Art of Uncertainty: How to Navigate Chance, Ignorance, Risk and Luck. Spiegelhalter is committed to making mathematics more accessible, and he joins My Wildest Prediction to talk about probabilities, how to deal with uncertainty and artificial intelligence. My Wildest Prediction is a podcast series from Euronews Business where we dare to imagine the future with business and tech visionaries. Hosted on Acast. See acast.com/privacy for more information.

Certainty is an illusion—so how do we navigate an unpredictable world? Professor Sir David Spiegelhalter, aka Professor Risk, unpacks the art of uncertainty, revealing why randomness, luck, and probability shape everything. Are we ever truly in control? What separates risk from recklessness? And why do we trust data that's often wrong? Discover how lucky people think, why free will might be a myth, and how to make smarter decisions in a world ruled by chaos. If you've ever questioned the odds, this mind-expanding conversation will change the way you see risk, luck, and the future. _____________________________________________________________ TIMESTAMPS 0:00 And Joseph Jaffy is not famous today I'm joined by Professor Sir David Spiegelhalter 1:14 Teller says Joseph Jaffy not famous love you 4:00 So many paths to go down this concept of living in a VUCA world 12:33 What is it to be a lucky person? What truly is luck? 23:29 Why are we so excited about odds we have no chance in winning? 46:52 We can't admit we're uncertain because people won't trust us—totally untrue 1:06:23 You are Chuck Norris approved and we will be back #Uncertainty #RiskVsLuck #TheIllusionOfControl Learn more about your ad choices. Visit megaphone.fm/adchoices

Scientific Sense ® by Gill Eapen: Professor Sir David Spiegelhalter is Professor of Public Understanding of Risk at Faculty of Mathematics, University of Cambridge. His recent book is entitled The Art of Uncertainty: How to Navigate Chance, Ignorance, Risk and Luck Please subscribe to this channel:https://www.youtube.com/c/ScientificSense?sub_confirmation=1

Today's revolutionary idea is something a bit different: David talks to statistician David Spiegelhalter about how an eighteenth-century theory of probability emerged from relative obscurity in the twentieth century to reconfigure our understanding of the relationship between past, present and future. What was Thomas Bayes's original idea about doing probability in reverse: from effect to cause? What happened when this way of thinking passed through the vortex of the French Revolution? How has it come to lie behind recent innovations in political polling, AI, self-driving cars, medical research and so much more? Why does it remain controversial to this day? The latest edition of our free fortnightly newsletter is available: to get it in your inbox sign up now https://www.ppfideas.com/newsletter  Next time: 1848: The Liberal Revolution w/Chris Clark Past Present Future is part of the Airwave Podcast Network Learn more about your ad choices. Visit megaphone.fm/adchoices

There is a word you probably use that means something entirely different than what you think. In fact, it means the opposite of what you think. Yet, this opposite meaning has become so pervasive, even dictionaries now say that the wrong meaning is now okay. Listen and I will tell you what the word is and what it really means. https://www.jalopnik.com/dear-hollywood-please-knock-it-off-with-the-overdrive-5926885/ Artificial Intelligence can seem intimidating to some. Yet it is actually quite simple to use and it can do amazing things to make your life better. It can teach you a skill, plan your dinner, plan a trip, be a brainstorming partner and counsel you to help with a problem. These are just a few of the things you'll discover how to do from listening to my guest, Celia Quillan. She is an expert in artificial intelligence and has been featured in Time, The New York Post, The Wall Street Journal, and the Today show. She is the creator of the popular TikTok and Instagram channel @SmartWorkAI and she is author of the book, AI for Life: 100+ Ways to Use Artificial Intelligence to Make Your Life Easier, More Productive…and More Fun! (https://amzn.to/3QGCYy0) We often use phrases like, “There's a good chance…” or “It's likely that….” But without knowing HOW good a chance or HOW likely something is, the phrases don't mean much. To help get a true understanding of chance, probability and luck is David Spiegelhalter, emeritus professor of statistics at the University of Cambridge and author of the book The Art of Uncertainty: How to Navigate Chance, Ignorance, Risk and Luck (https://amzn.to/41sXdEu). You probably feel safe taking a shower in your own bathroom. But dangers are lurking – some you might never have thought of. Listen as I explain how to reduce the risk of taking a shower. https://www.menshealth.com/health/g19544438/shower-safety/ PLEASE SUPPORT OUR SPONSORS!!! FACTOR: Eat smart with Factor! Get 50% off at https://FactorMeals.com/something50off QUINCE: Indulge in affordable luxury! Go to https://Quince.com/sysk for free shipping on your order and 365-day returns. TIMELINE: Get 10% off your order of Mitopure!  Go to https://Timeline.com/SOMETHING SHOPIFY:  Nobody does selling better than Shopify! Sign up for a $1 per-month trial period at https://Shopify.com/sysk and upgrade your selling today! HERS: Hers is changing women's healthcare by providing access to GLP-1 weekly injections with the same active ingredient as Ozempic and Wegovy, as well as oral medication kits. Start your free online visit today at https://forhers.com/sysk INDEED: Get a $75 sponsored job credit to get your jobs more visibility at https://Indeed.com/SOMETHING right now! Learn more about your ad choices. Visit megaphone.fm/adchoices

How did Barack Obama, former American president know for sure whether Osama Bin Laden was in that compound in Abbottabad? Are football matches largely determined by luck? How can you measure coincidences? Sir David Spiegelhalter, the emeritus professor of statistics at the University of Cambridge explains it all. His new book, “The Art of Uncertainty: How to Navigate Chance, Ignorance, Risk and Luck” is a fascinating read even for those without any background in statistics and mathematics. Among many other things, the book is also a lesson in how to make complicated subjects interesting.

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.

David Spiegelhalter is Emeritus Professor of Statistics in the Statistical Laboratory, University of Cambridge and author of new book The Art of Uncertainty We live in chaotic times and David makes that world a little clearer with humour and clarity in this special interview with Alberto and Simon. The⁠ ⁠⁠⁠⁠⁠⁠⁠music this episode⁠⁠⁠⁠⁠,⁠ made with⁠⁠⁠⁠⁠⁠⁠⁠ ⁠TwoTone⁠⁠⁠⁠⁠⁠⁠⁠⁠, comes from David, and represents the death rates of the patients of murderer Dr Harold Shipman.

Nathan sits down with Emeritus Professor of Statistics at the University of Cambridge Sir David Spiegelhalter to discuss his new book "The Art of Uncertainty: How to Navigate Chance, Ignorance, Risk and Luck." Together, the pair covers nuances of probability, risk, and luck; as well as examine the way we understand the data surronding our everyday lives. David Spiegelhalter: Book: "The Art of Uncertainty: How to Navigate Chance, Ignorance, Risk and Luck" More S&P Global Content: The Daily Update S&P Global Look Forward Report Credits: Host/Author: Nathan Hunt Producer/Editor: Patrick Moroney Published With Assistance From: Kyle May, Kurt Burger, Camille McManus www.spglobal.com  

We live in a world where uncertainty is inevitable. How should we deal with what we don't know? And what role do chance, luck and coincidence play in our lives? Cambridge statistician and beloved broadcaster David Spiegelhalter has spent his career dissecting data in order to understand risks and assess the chances of what might happen in the future. In this episode of the podcast, recorded live in London with live examples with the audience, he guides us through the principles of probability, showing how it can help us think more analytically about everything from medical advice to pandemics and climate change forecasts, and explores how we can update our beliefs about the future in the face of constantly changing experience. Tune in to find why we can be so confident that two properly shuffled packs of cards have never been in the exact same order, what it means to be mathematically lucky, and how a classroom of people will result in a shared birthday in this essential guide to navigating uncertainty while also having the humility to admit what we do not know. To get an exclusive NordVPN deal, head to https://nordvpn.com/howtoacademy to get an extra 4 months on the 2-year plan. There's no risk with Nord's 30-day money-back guarantee. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Investing is an uncertain journey, where chance, risk, and luck are constant companions. But can understanding probability help us live with the unknown? We're joined by Sir David Spiegelhalter, Emeritus Professor of Statistics at the University of Cambridge and author of 'The Art of Uncertainty: How to Navigate Chance, Ignorance, Risk, and Luck'. And in today's Dumb Question of the Week, we ask: ‘Would you want to know when you will die?' --- Thank you to Trading 212 for sponsoring this episode. Claim free fractional shares worth up to ‎£⁠100. Just create and verify a Trading 212 Invest or Stocks ISA account, make a minimum deposit of £1, and use the promo code "RAMIN" within 10 days of signing up, or use the following link: trading212.com/join/RAMIN When investing, your capital is at risk and you may get back less than invested. Past performance doesn't guarantee future results. Pies & Autoinvest is an execution-only service. Not investment advice or portfolio management. Automatic investing refers to executing scheduled deposits. You are responsible for all investment and rebalancing decisions. Free shares can be fractional. Terms and fees apply. ---Get in touch

David Spiegelhalter, one of our favourite statisticians in the whole world, has a new book out. It's called The art of uncertainty: How to navigate chance, ignorance, risk and luck and published by Pelican Books. In this episode of Maths on the Move we talk to David about the book, touching on a huge range of topics — from double yolked eggs and the bay of pigs, to why it's useful to disagree and why uncertainty is personal. Enjoy! To find out more about some of the topics mentioned in this episode see, When being wrong is right — on the "tell me why I'm wrong" approach Struggling with chance — on the philosophy of probability Freedom and physics — on randomness and free will  

Consider this our call to arms for wine. Where we grapple heroically with the thorny issue of wine and health, calling out misinformation and over-reach, and learn that the truth is always complex, potentially positive - but often mis-represented. This makes us angry and frustrated. And you should feel the same too.You may also feel confused or jaded by this topic. Understandably so. But join us and we will hopefully clear things up AND imbue you with renewed vigour to fight the good fight. Because this is about things we all hold dear - freedom, fairness and our health and personal pleasure. For wine lovers, this is not a time to stay silent. Helping us shed light on this contentious topic are Christopher Snowdon from the Institute of Economic Affairs and Dr Laura Catena, former emergency physician in San Francisco, now head of respected Argentine winery Catena Zapata. Also cited are Tim Stockwell, Sir David Spiegelhalter, Kenneth Mukamal, Eric B Rimm and Edward Slingerland. Along the way we talk dogs, megaphones, the J-shaped curve, bacon sandwiches, zombie arguments and quantifying joy. We even find time to recommend some delicious wines. Thanks for tuning in. We love to hear from you so please do get in touch! Send us a voice message via Speakpipe. Or you can find contact info, together with all details from this episode including full wine recommendations, on our website: Show notes for Wine Blast S6 E3 - Life or Death? On Wine and HealthInstagram: @susieandpeter

David Spiegelhalter – The Art of Uncertainty: how to navigate chance, ignorance, risk and luck...with TRE's Hannah Murray

Statistician, Professor Sir David Spiegelhalter turns the pages of his book, The Art of Uncertainty: How to Navigate Chance, Ignorance, Risk and Luck, out now. Join Chris, Vassos and the team every morning from 6.30am for laughs with the listeners and the greatest guests. Listen on your smart speaker, just say: "Play Virgin Radio." Hosted on Acast. See acast.com/privacy for more information.

‘Professor Risk' David Spiegelhalter delves into the data and statistics to explore the forces of chance, ignorance and luck in The Art of Uncertainty. Whereas life is uncertain, he shows how far the circumstances of how, when and where you were born have an overriding influence on your future. But he warns against confusing the improbable with the impossible. The novelist Roddy Doyle returns to the fortunes of one of his iconic characters, Paula Spencer, in his new book, The Woman Behind The Door. Mother, grandmother, widow, addict and survivor Paula Spencer is finally laying the ghosts of the past to rest, but how much is passed on to the next generation?The historian Eliza Filby is interested in inheritance of a different kind – money and housing. In Inheritocracy: It's Time to Talk About the Bank of Mum and Dad, she explores the nature of privilege through her own family's experience. Filby's grandfather had the lucky fortune of winning a house in a card game and the family went on to become ‘working class accidental millionaires' who could pass on their fortune to later generations.Producer: Katy Hickman

Okay, it's time to finally answer the question: is drinking booze good or bad? Is there really a “J-curve”, such that it's bad to drink zero alcohol, good to drink a little, and then bad to drink any more than that? What exactly is the “safe level” of alcohol consumption, and why do the meta-analyses on this topic all seem to tell us entirely different things?In this episode of The Studies Show, Tom and Stuart get very badly intoxicated—with statistics.We're sponsored by Works in Progress magazine. There's no better place online to find essays on the topic of “Progress Studies”—the new field that digs deep into the data on how scientific and technological advances were made in the past, and tries to learn the lessons for the future. Check them out at worksinprogress.co.Show notes* Media reports say alcohol is good! Oh no wait, it's bad. Oh, sorry, it's actually good! No, wait, actually bad. And so on, ad infinitum* The three conflicting meta-analyses:* 2018 in The Lancet (“no safe level”)* 2022 in The Lancet (the J-curve returns)* 2023 in JAMA Network Open (using “occasional drinkers” as the comparison)* Some of the press coverage about the J-curve age differences* David Spiegelhalter's piece comparing the two Lancet meta-analyses* Tom's piece on the idea of “safe drinking”CreditsThe Studies Show is produced by Julian Mayers at Yada Yada Productions. We're very grateful to Sir David Spiegelhalter for talking to us about this episode (as ever, any errors are ours alone). This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thestudiesshowpod.com/subscribe

In this episode of the Data Malarkey podcast, your host Sam Knowles is joined by one of the world's finest data storytellers, David Spiegelhalter, the statistician and public communicator of his generation. Although he claims to have been retired for five years, the Emeritus Professor of Statistics from Cambridge University is working harder than ever.   Our conversation was recorded remotely, via the medium of Riverside.fm, on 6 March 2024.   Thanks to Joe Hickey for production support.   Podcast artwork by Shatter Media.   Voice over by Samantha Boffin.   If anyone can be said to have had “a good pandemic”, it was David. “At least I had something to do!” he quips, sharing how he quickly set up a studio at home and gave countless interviews about what the data meant and what we should do as a result.   While he believes that the Chief Scientific and Medical Officers of the U.K. National Health Service usually presented complex information simply and straightforwardly to a willing and receptive public – hungry for evidence of what they might choose to do and why – Government ministers (to the very top) and their Special Advisors (SPADs) had little clue.   Nothing gets David more irritated than wilful misuse of data, and several times during our lively discussion he vents considerable fury at peddlers of misinformation, under COVID and otherwise. We talk a lot about communicating risk (relative and absolute), particularly under uncertainty, with uncertainty the theme of David's imminent new book, The Art of Uncertainty (to be published by Penguin in September 2024).   Away from the stats lab, we learn how David applied his data-driven smarts to winning the inaugural (and to-date only) Loop World Championship; Loop is pool played on an elliptical table with only one pocket at one of the foci of the ellipse. He also took an evidence-based approach to qualifying for the second round of Winter Wipeout, recorded a dozen years and more ago in Argentina, where David adopted the persona of Professor Risk.   In addition to uncertainty, we also focus on trustworthiness. For David, those using data and statistics to communicate need to earn and constantly re-earn a reputation for being trustworthy. And just as no-one laughs at a comedian who says “I'm funny” at the start of his set, no-one trusts a person using data to communicate complex topics who says “Just trust me!”. Being seen as trustworthy is a consequence of being honest, competent, and reliable.   David introduces Sam and the audience to the skill of “pre-bunking”, and several times warns against building data-driven narratives that push emotional levers or buttons. Data storytellers should present the evidence simply and fairly and then allow the audience to draw their own conclusions. “Treat them as if they're intelligent, but also as if they don't know anything.”   EXTERNAL LINKS Cambridge University personal profile page https://www.statslab.cam.ac.uk/~david/ David on Wikipedia https://en.wikipedia.org/wiki/David_Spiegelhalter     To find out what kind of data storyteller you are, complete our data storytelling scorecard at https://data-storytelling.scoreapp.com. It takes just two minutes, and we'll send you your own personalised scorecard which tells you what kind of data storyteller you are.  

In any war, counting the number of people killed is challenging. So, too, is understanding how they died. In Gaza, where the still-rising death toll already includes 13,450 children, these figures can be obscured by biases, allegations—and the realities on the ground. In this week's episode, Lionel Barber and Alan Rusbridger are joined by leading statistician David Spiegelhalter to discuss how to shed light on casualty numbers in a war situation. Also this week, George Brock joins Alan and Lionel to discuss a small yet significant development in the future of local news. George is a professor of journalism at City, University of London and has previously worked at the Yorkshire Evening Press, Observer and Times, where he was managing editor and Saturday editor. George explains how the Guildford Dragon has secured charitable status, and whether this could be a possible model for local news across the country. Hosted on Acast. See acast.com/privacy for more information.

Professor Sir David Spiegelhalter was Winton Professor of the Public Understanding of Risk at the University of Cambridge until his recent retirement. A gifted broadcaster and communicator, David became familiar to many through his media discussions around statistics and risk during the Covid-19 pandemic. https://www.statslab.cam.ac.uk/~david/ 

When people are told a statistical claim, particularly about risk, the most important follow-up they can ask is about magnitude. How big of a number or impact is this? But many lack a basic understanding of statistics and how they fit into our world. It's not baked into the fundamentals of education. David Spiegelhalter is an emeritus professor of statistics at the University of Cambridge. His books like The Art of Statistics: How to Learn from Data and Covid By Numbers: Making Sense of the Pandemic with Data help contextualize the importance and impact of statistics in everyday life. He and Greg discuss the vital role of data literacy, how concepts like 'micro-mort' and 'micro-life' can measure risk, and the ramifications of faulty statistical interpretation during crises like COVID-19. *unSILOed Podcast is produced by University FM.*Episode Quotes:You can't talk about risk without talking about its magnitude04:31: To talk about risk without talking about magnitude, I think, is an abominable thing to do. It's manipulative. It's always manipulative. If someone's going to talk about risk, they are trying to worry you, and they're trying to manipulate your emotions. Most of the time, we talk about increased risk and the risk (delete the ‘of” )without having any idea what the magnitude is. And even if we do, it's quite difficult to know: is that a big number or not? So, I think that this is absolutely essential: whenever people are told something, a claim, they should ask, "How big is it? And is this really a big number? Is this really important?"Risk as analysis is very often dominated by risk as feeling21:22: Risk as analysis is very often dominated by risk as feeling, and you've got to have risk as feeling, I think, in there as well. But it's when one of them takes over. And I think the real problem with this is that if you just operate on risk as a feeling, it's so easy to be manipulated by people. "Oh, this is awful. This is awful." You've got to be really careful of this. And you think, "No, it's not bad," or understating what the risks of some things are. So I think that if you're very vulnerable, if you only operate on risk as a feeling, you're vulnerable to manipulation.How do you gain micro life?38:40: You can gain micro-lives by exercising and stuff like that. And so, but that's highly nonlinear. The benefits from the first 20 minutes of exercising are considerable. It's about 40 minutes. So it's about a micro-life and a half, or something like that. After that, it's about the past. So, if you exercise for half an hour, you live an extra half an hour. So you better enjoy exercising because that's the extra bit you're living. And it's like time, and I quite like this image that while you're exercising moderately, your aging stops. You're not aging that half hour.COVID's positive impact on public interest in data47:00: During COVID, it was amazing. The popular interest in statistics, data, and graphs—I was on the media all the time trying to explain stuff. And that's carried on. It's even good. Is there any good news about COVID? One of the small things I think might be the greater public tolerance for an interest in data and graphs and more subtle ideas being used.Show Links:Recommended Resources:Ronald FisherRon HowardPaul SlovicGuest Profile:Faculty Profile at University of CambridgeHis Work:The Art of Statistics: How to Learn from DataCovid By Numbers: Making Sense of the Pandemic with DataThe Norm Chronicles: Stories and Numbers About Danger and DeathSex by Numbers: What Statistics Can Tell Us About Sexual Behaviour 

In this episode we talk about the growth of data use in the media and the potential impact of misinformation on the public's trust in official statistics.   Navigating podcast host Miles Fletcher through this minefield is Prof Sir David Spiegelhalter, from the University of Cambridge; Ed Humpherson, Head of the Office for Statistics Regulation; and award-winning data journalist Simon Rogers.    Transcript    MILES FLETCHER  Welcome again to Statistically Speaking, the official podcast of the UK's Office for National Statistics, I'm Miles Fletcher. Now we've talked many times before in these podcasts about the rise of data and its impact on our everyday lives. It's all around us of course, and not least in the media we consume every day. But ‘what' or ‘who' to trust: mainstream media, public figures and national institutions like the ONS, or those random strangers bearing gifts of facts and figures in our social media feeds?  To help us step carefully through the minefields of misinformation and on, we hope, to the terra firma of reliable statistical communication, we have three interesting and distinguished voices, each with a different perspective. Professor Sir David Spiegelhalter is a well-known voice to UK listeners. He's chair of the Winton Centre for Risk Evidence Communication at the University of Cambridge and was a very prominent voice on the interpretation of public health data here during the COVID pandemic. Also, we have Ed Humpherson, Director General of regulation and head of the Office for Statistics Regulation (OSR), the official stats watchdog if you like, and later in this podcast, I'll be joined by award winning data journalist and writer Simon Rogers, who now works as data editor at Google.  Professor, you've been one of the most prominent voices these last few years – a fascinating few years, obviously, for statistics in which we were told quite frankly, this was a golden age for statistics and data. I mean, reflecting on your personal experience as a prominent public voice in that debate, when it comes to statistics and data, to be very general, how well informed are we now as a public, or indeed, how ill-informed on statistics?     DAVID SPIEGELHALTER  I think things have improved after COVID. You know, for a couple of years we saw nothing but numbers and graphs on the news and in the newspapers and everywhere, and that went down very well. People didn't object to that. In fact, they wanted more. And I think that has led to an increased profile for data journalism, and there's some brilliant ones out there. I'm just thinking of John Burn-Murdoch on the FT but lots of others as well, who do really good work. Of course, in the mainstream media there is still the problem of non-specialists getting hold of data and getting it wrong, and dreadful clickbait headlines. It is the sub editors that wreck it all just by sticking some headline on what might be a decent story to get the attention and which is quite often misleading. So that's a standard problem. In social media, yeah, during COVID and afterwards, there are people I follow who you might consider as - I wouldn't say amateurs at all, but they're not professional pundits or media people - who just do brilliant stuff, and who I've learned so much from. There are also some terrible people out there, widespread misinformation claims which are based on data and sound convincing because they have got numbers in them. And that, I mean, it's not a new problem, but now it is widespread, and it's really tricky to counter and deal with, but very important indeed.     MF  So the issue aside from - those of us who deal with the media have heard this a hundred times - “I don't write the headlines”, reporters will tell you when you challenge that misleading kind of headline. But would you say it's the mainstream media then, because they can be called out on what they report, who broadly get things right? And that the challenge is everything else - it's out there in the Wild West of social media?     DS  Yeah, mainstream media is not too bad, partly because, you know, we've got the BBC in this country, we've got regulations, and so it's not too bad. And social media, it's the Wild West. You know, there are people who really revel in using numbers and data to make inappropriate and misleading claims.     MF  Is there anything that can be done? Is it the government, or even those of us like the ONS who produce statistics, who should we be wading in more than we do? Should we be getting out there onto the social media platforms and putting people right?     DS  It's difficult I mean, I don't believe in sort of censorship. I don't think you can stop this at source at all. But just because people can say this, it doesn't give them a right for it to be broadcast wide, in a way and to be dumped into people's feeds. And so my main problem is with the recommendation algorithms of social media, where people will see things because it's getting clicks, and the right algorithm thinks persona will like it. And so we just get fed all this stuff. That is my real problem and the obscurity and the lack of accountability of recommendation algorithms right across social media is I think, a really shocking state of affairs. Of course, you know, we come on to this later, but we should be doing something about education, and actually sort of pre-empting some of the misunderstandings is something I feel very strongly about with my colleagues. You've got to get in there quick, and rather than being on the backfoot and just reacting to false claims that have been made, you've got to sort of realise how to take the initiative and to realise what misunderstandings, misinterpretations can be made, and get in there quickly to try to pre-empt them. But that of course comes down to the whole business of how ONS and others communicate their data.     MF  Because when you ask the public whether they trust them - and the UK statistics authority does this every two years - you ask the public if they trust ONS statistics, and a large proportion of them say they do. But of course, if they're not being presented with those statistics, then they're still going to end up being misled.     DS  Yeah, I mean, it's nice to get those responses back. But, you know...that's in terms of respondents and just asking a simple question, do you trust something or not? I think it's good to hear but we can't be complacent about that at all. I'm massively influenced by the approach of the philosopher, Baroness Onora O'Neill, who really makes a sharp distinction between organisations wanting to be trusted and revelling in being trusted, and she says that shouldn't be your objective to be trusted. Your objective should be to be trustworthy, to deserve trust, and then it might be offered up to you. And so the crucial thing is trustworthiness of the statistics system and in the communications, and that's what I love talking about, because I think it's absolutely important and it puts the responsibility really firmly back to the communicator to demonstrate trustworthiness.     MF  So doing more as stats producers to actually actively promote data and get people to come perhaps away from the social platforms, and to have their own websites that present data in an accessible way, in an understandable way, where people can get it for nothing without requiring an expensive subscription or something, as some of the best of the media outlets would require.     DS  The other thing I'd say is there's no point of being trustworthy if you're dull, as no one's going to look at it or take any notice, and other media aren't going to use it. So I think it's really worthwhile to invest, make a lot of effort to make what you're putting out there as attractive, as vivid and as grabbing as possible. The problem is that in trying to do that, I mean, that's what a lot of communicators and media people want to do, because of course they want people to read their stuff. But what that tends to do largely is make their stuff kind of opinionated and have a very strong line, essentially to persuade you to either do something or think something or buy something or vote something. So much communication has to do with persuading that I think it's just completely inappropriate. In this context, what we should be doing is informing people.    In a way we want to persuade them to take notice, so that's why you want to have really good quality communications, vivid, get good people out there. But in the end, they're just trying to inform people, and that's why I love working with ONS. I just think this is a really decent organisation whose job is just trying to raise the...to obviously provide official statistics...but in their communications, it's to try to raise the level of awareness raise the level of discussion, and by being part of a non -ministerial department, they're not there, the comms department, to make the minister look good, or to make anyone look good. It's just there to tell people how it is.    MF  Exactly. To put that data into context. Is this a big number or is this is a small number, right? Adjectives can sometimes be very unhelpful, but often the numbers don't speak for themselves, do they.    DS   Numbers never speak for themselves, we imbue them with meaning, which is a great quote as well from Nate Silver.    MF  And in doing that, of course, you have to walk the same line that the media do, in making them relevant and putting them into context, but not at the same time distorting them. There's been a big debate going on recently, of course, about revisions. And if you've listened to this podcast, which we'd always advise and consume other articles that the ONS has published, we've said a lot about the whole process of revising GDP, and the uncertainty that's built into those initial estimates, which although helpful, are going to be pretty broad. And then of course, when the picture changes dramatically, people are kind of entitled to say, oh hang on, you told us this was something different and the narrative has changed. The story has changed because of that uncertainty with the numbers, shouldn't you have done more to tell us about that uncertainty. That message can sometimes get lost, can't it?     DS  Yeah, it's terribly important. You've got to be upfront. We develop these five points on trustworthy communication and the first one was inform, not persuade. And the second is to be balanced and not to have a one-sided message to tell both sides of the story, winners and losers, positives and negatives. And then to admit uncertainty, to just say what you don't know. And in particular, in this case, “provisionality”, the fact that things may change in the future, is incredibly important to emphasise, and I think not part of a lot of discussion. Politicians find it kind of impossible to say I think, that things are provisional and to talk about quality of the evidence and limitations in the evidence, which you know, if you're only basing GDP on a limited returns to start with, on the monthly figures, then you need to be clear about that. And the other one is to pre-empt the misunderstandings, and again, that means sort of getting in there first to tell you this point, this may change. This is a provisional judgement, and you know, I think that that could be emphasised yet more times, yet more.     MF  And yet there's a risk in that though, of course the message gets lost and diluted and the...    DS  Oh no, it always gets trotted out - oh, we can't admit uncertainty. We can't tell both sides of story. We have to tell a message that is simple because people are too stupid to understand it otherwise, it's so insulting to the audience. I really feel a lot of media people do not respect their audience. They treat them as children - oh we've got to keep it simple, we mustn't give the nuances or the complexity. All right, if you're going to be boring and just put long paragraphs of caveats on everything, no one is going to read that or take any notice of them. But there are ways to communicate balance and uncertainty and limitations without being dull. And that's what actually media people should focus on. Instead of saying, oh, we can't do that. You should be able to do it. Good media, good storytelling should be able to have that nuance in. You know, that's the skill.     MF  You're absolutely right, you can't disagree with any of that, and yet, in communicating with the public, even as a statistics producer, you are limited somewhat by the public's ability to get used to certain content. I mean, for example, the Met Office recently, a couple of years back, started putting in ‘percentage of chance of rainfall', which is something that it hadn't done before. And some work on that revealed just how few people actually understood what they were saying in that, and what the chances were actually going to be of it raining when they went out for the afternoon's work.     DS  Absolute nonsense. That sorry, that's completely I mean, I completely rely on those percentages. My 90-year-old father used to understand those percentages. Because it's a novelty if you are going to ask people what they understand, they might say something wrong, such as, oh, that's the percentage of the area that it's going to rain in or something like that. No, it's the percentage of times it makes that claim that it's right. And those percentages have been used in America for years, they're completely part of routine forecast and I wouldn't say the American public is enormously better educated than the British public. So this is just reluctance and conservatism. It's like saying oh well people don't understand graphs. We can't put up line graphs on the news, people don't understand that. This is contempt for the public. And it just shows I think, a reluctance to make an effort to explain things. And people get used to stuff, once they've learned what a graph looks like, when they see it again, then they'll understand it. So you need to educate the public and not, you know, in a patronising way, it's just that, you know, otherwise you're just being misleading. If you just say, oh, you know, it'll rain or not rain you're just misleading them. If you just say it might rain, that's misleading. What does that mean? It can mean different things. I want a percentage and people do understand them, when they've got some experience of them.     MF  And what about certainty in estimates? Here is a reaction we add to the migration figures that ONS published earlier in the summer. Somebody tweeted back to say, well estimates, that's all very good but I want the actual figures. I want to know how many people have migrated.    DS  Yeah, I think actually, it's quite a reasonable question. Because, you know, you kind of think well can't you count them, we actually know who comes in and out of the country. In that case it's really quite a reasonable question to ask. I want to know why you can't count them. And in fact, of course ONS is moving towards counting them. It's moving away from the survey towards using administrative data to count them. So I think in that case, that's quite a good question to ask. Now in other situations, it's a stupid question. If you want to know if someone says, oh, I don't want an estimate of how many people you know, go and vote one way or do something or other, I want to know how many, well then you think don't be daft. We can't go and ask everybody this all the time. So that's a stupid question. So the point is that in certain contexts, asking whether something is an estimate or not, is reasonable. Sometimes it's not and that can be explained, I think, quite reasonably to people.     MF  And yet, we will still want to be entertained. We also want to have numbers to confirm our own prejudices.     DS  Yeah, people will always do that. But that's not what the ONS is for, to confirm people's prejudices. People are hopeless at estimating. How many, you know, migrants there are, how many people, what size ethnic minorities and things, we know if you ask people these numbers, they're pretty bad at it. But people are bad at estimating all numbers. So no, it's ONS's job to try to explain things and in a vivid way that people will be interested in, particularly when there's an argument about a topic going on, to present the evidence, not one side or the other, but that each side can use, and that's why I really feel that the ONS's migration team, you know, I have a lot of respect for them, when they're changing their format or consulting on it, they go to organization's on both sides. They go to Migration Watch and the Migration Observatory and talk to them about you know, can they understand what's going on, is this data helping them in their deliberations.     MF  Now, you mentioned earlier in the conversation, education, do we have a younger generation coming up who are more stats literate or does an awful lot more need to be done?     DS  A lot more needs to be done in terms of data education in schools. I'm actually part of a group at the Royal Society that is proposing a whole new programme called mathematics and data education, for that to be put together within a single framework, because a lot of this isn't particularly maths, and maths is not the right way or place to teach it. But it still should be an essential part of education, understanding numbers, understanding data, their limitations and their strengths and it uses some numeracy, uses some math but it's not part of maths. The problem has always been where does that fit in the syllabus because it doesn't, particularly at the moment. So that's something that every country is struggling with. We're not unique in that and, and I think it's actually essential that that happens. And when you know, the Prime Minister, I think quite reasonably says people should study mathematics until 18. I mean, I hope he doesn't mean mathematics in the sense of the algebra and the geometry that kids do, get forced to do essentially, for GCSE, and some of whom absolutely loathe it. And so, but that's not really the sort of mathematics that everyone needs. Everyone needs data literacy. Everyone needs that.    MF  Lies, damned lies and statistics is an old cliche, it's still robustly wheeled out in the media every time, offering some perceived reason to doubt what the statisticians have said. I mean looking ahead, how optimistic are you, do you think that one day we might finally see the end of all that?     DS  Well my eyes always go to heaven, and I just say for goodness sake. So I like it when it's used, because I say, do you really believe that? You know, do you really believe that, because if you do you're just rejecting evidence out of hand. And this is utter stupidity. And nobody could live like that. And it emphasises this idea somehow, among the more non-data-literate, it encourages them to think that numbers they hear either have to be sort of accepted as God given truths or rejected out of hand. And this is a terrible state to be in, the point is we should interpret any number we hear, any claim based on data, same as we'd interpret any other claim made by anybody about anything. We've got to judge it on its merits at the time and that includes do we trust the source? Do I understand how this is being explained to me? What am I not being told? And so why is this person telling me this? So all of that comes into interpreting numbers as well. We hear this all the time on programmes like More or Less, and so on. So I like it as a phrase because it is so utterly stupid, then so utterly, easily demolished, that it encourages, you know, a healthy debate.    MF  We're certainly not talking about good statistics, we're certainly not talking about quality statistics, properly used. And that, of course, is the role of the statistics watchdog as we're obliged to call him, or certainly as the media always call him, and that's our other guest, Ed Humpherson.     Ed, having listened to what the professor had to say there, from your perspective, how much misuse of statistics is there out there? What does your organisation, your office, do to try and combat that?      ED HUMPHERSON     Well, Miles the first thing to say is I wish I could give you a really juicy point of disagreement with David to set off some kind of sparky dialogue. Unfortunately, almost everything, if not everything that David said, I completely agree with - he said it more fluently and more directly than I would, but I think we are two fellow travellers on all of these issues.      In terms of the way we look at things at the Office for Statistics Regulation that I head up, we are a statistics watchdog. That's how we are reported. Most of our work is, so to speak, below the visible waterline: we do lots and lots of work assessing reviewing the production of statistics across the UK public sector. We require organisations like the ONS, but also many other government departments, to be demonstrating their trustworthiness; to explain their quality; and to deliver value. And a lot of that work just goes on, week in week out, year in year out to support and drive-up evidence base that's available to the British public. I think what you're referring to is that if we care about the value and the worth of statistics in public life, we can't just sort of sit behind the scenes and make sure there's a steady flow. We actually have to step up and defend statistics when they are being misused because it's very toxic, I think, to the public. Their confidence in statistics if they're subjected to rampant misuse or mis explanation of statistics, it's all very well having good statistics but if they go out into the world and they get garbled or misquoted, that I think is very destructive. So what we do is we either have members of the public raise cases with us when they see something and they're not they're not sure about it, or indeed we spot things ourselves and we will get in contact with the relevant department and want to understand why this thing has been said, whether it really is consistent with the underlying evidence, often it isn't, and then we make an intervention to correct the situation. And we are busy, right, there's a lot there's a lot of there's a lot of demand for work.     MF  Are instances of statistical misuse on the rise?     EH  We recently published our annual summary of what we call casework - that's handling the individual situations where people are concerned. And we revealed in that that we had our highest ever number of cases, 372, which might imply that, you know, things are getting worse. I'd really strongly caution against that interpretation. I think what that increase is telling you is two other things. One is, as we as the Office for Statistics Regulation, do our work, we are gradually growing our profile and more people are aware that they can come to us, that's the first thing this is telling you; and the second thing is that people care a lot more about statistics and data now, exactly as Sir David was saying that this raised profile during the pandemic. I don't think it's a sign that there's more misuse per se. I do think perhaps, the thing I would be willing to accept is, there's just a generally greater tendency for communication to be datafied. In other words, for communication to want to use data: it sounds authoritative, it sounds convincing. And I think that may be driving more instances of people saying well, a number has been used there, I want to really understand what that number is. So I would be slightly cautious about saying there is more misuse, but I would be confident in saying there's probably a greater desire to use data and therefore a greater awareness both of the opportunity to complain to us and of its importance.      MF  Underlying all of your work is compliance with the UK code of practice for statistics, a very important document, and one that we haven't actually mentioned in this podcast so far…      EH  Shame on you, Miles, shame on you.      MF  We're here to put that right, immediately. Tell us about what the code of practice is. What is it for? what does it do?      EH  So the Code of Practice is a statutory code and its purpose is to ensure that statistics serve the public good. And it does that through a very simple structure. It says that in any situation where an individual or an organisation is providing information to an audience, there are three things going on. There's the trustworthiness of the speaker, and the Code sets out lots of requirements on organisations as to how they can demonstrate they're trustworthiness. And it's exactly in line with what David was saying earlier and exactly in line with the thinking of Onora O'Neill – a set of commitments which demonstrate trustworthiness. Like a really simple commitment is to say, we will pre-announce at least four weeks in advance when the statistics are going to be released, and we will release them at the time that we say, so there is no risk that there's any political interference in when the news comes out. It comes out at the time that has been pre-announced. Very clear commitment, very tangible, evidence-based thing. It's a binary thing, right? You either do that or you do not. And if you do not: You're not being trustworthy. The second thing in any situation where people are exchanging information is the information itself. What's its quality? Where's this data from? How's it been compiled? What are its strengths and limitations? And the code has requirements on all of those areas. That is clarity of what the numbers are, what they mean, what they don't mean. And then thirdly, in that exchange of information, is the information of any use to the audience? It could be high, high quality, it could be very trustworthy, but it could, to use David's excellent phrase, it could just be “dull”. It could be irrelevant, it could not be important. And the value pillar is all about that. It's all about the user having relevant, insightful information on a question that they care about. That's, Miles, what the Code of Practice is: it's trustworthiness, it's quality and it's value. And those things we think are kind of pretty universal actually, which is why they don't just apply now to official statistics. We take them out and we apply them to all sorts of situations where Ministers and Departments are using numbers, we always want to ask those three questions. Is it trustworthy? Is it quality, is it value? That's the Code.      MF  And when they've satisfied your stringent requirements and been certified as good quality, there is of course a badge to tell the users that they have been.      EH  There's a badge - the badge means that we have accredited them as complying with that Code of Practice. It's called the National Statistics badge. The term is less important and what it means what it means is we have independently assessed that they comply in full with that Code.      MF  Most people would have heard, if they have heard of the OSR's work, they'll have seen it perhaps in the media. They'll have seen you as the so-called data watchdog, the statistics watchdog. It's never gently explained as it it's usually ‘slammed', ‘criticised', despite the extremely measured and calm language you use, but you're seen as being the body that takes politicians to task. Is that really what you do? It seems more often that you're sort of gently helping people to be right.      EH  That's exactly right. I mean, it's not unhelpful, frankly, that there's a degree of respect for the role and that when we do make statements, they are taken seriously and they're seen as significant, but we are not, absolutely not, trying to generate those headlines. We are absolutely not trying to intimidate or scare or, you know, browbeat people. Our role is very simple. Something has been said, which is not consistent with the underlying evidence. We want to make that clear publicly. And a lot of time what our intervention does actually is it strengthens the hand of the analysts in government departments so that their advice is taken more seriously at the point when things are being communicated. Now, as I say, it's not unwelcome sometimes that our interventions do get reported on. But I always try and make these interventions in a very constructive and measured way. Because the goal is not column inches. Absolutely not. The goal is the change in the information that's available to the public.      MF  You're in the business of correcting the record and not giving people a public shaming.      EH  Exactly, exactly. And even correcting the record actually, there's some quite interesting stuff about whether parliamentarians correct the record. And in some ways, it'd be great if parliamentarians corrected the record when they have been shown to have misstated with statistics. But actually, you could end up in a world where people correct the record and in a sort of tokenistic way, it's sort of, you know, buried in the depths of the Hansard parliamentary report. What we want is for people not to be misled, for people to not think that, for example, the number of people in employment is different from what it actually is. So actually, it's the outcome that really matters most; not so much the correction as are people left understanding what the numbers actually say.      MF  Surveys show - I should be careful using that phrase, you know - nonetheless, but including the UKSA survey, show that the public were much less inclined to trust in the words of the survey. Politicians use of statistics and indeed, Chris Bryant the Labour MP said that politicians who have been who've been found to have erred statistically should be forced to apologise to Parliament. Did you take that on board? Is there much in that?     EH  When he said that, he was actually directly quoting instances we've been involved with and he talks about our role very directly in that sense. Oh, yeah, absolutely. We support that. It will be really, really good. I think the point about the correction, Miles, is that it shows it's a manifestation of a culture that takes fidelity to the evidence, truthfulness to the evidence, faithfulness to the evidence, it takes that seriously, as I say, what I don't want to get into is a world where you know, corrections are sort of tokenistic and buried. I think the key thing is that it's part of an environment in which all actors in public debate realise it's in everybody's interests or evidence; data and statistics to be used fairly and appropriately and part of that is that if they've misspoken, they correct the record. From our experience, by and large, when we deal with these issues, the politicians concerned want to get it right. What they want to do is, they want to communicate their policy vision, their idea of the policy or what the, you know, the state of the country is. They want to communicate that, sure, that's their job as politicians, but they don't want to do so in a way that is demonstrably not consistent with the underlying evidence. And in almost all cases, they are… I wouldn't say they're grateful, but they're respectful of the need to get it right and respect the intervention. And very often the things that we encounter are a result of more of a cockup than a conspiracy really - something wasn't signed off by the right person in the right place and a particular number gets blown out of proportion, it gets ripped from its context, it becomes sort of weaponized; it's not really as a deliberate attempt to mislead. Now, there are probably some exceptions to that generally positive picture I'm giving. but overall it's not really in their interests for the story to be about how they misuse the numbers. That's not really a very good look for them. They'd much rather the stories be about what they're trying to persuade the public of, and staying on the right side of all of the principles we set out helps that to happen.      MF  Your remit runs across the relatively controlled world sort of government, Parliament and so forth. And I think the UK is quite unusual in having a body that does this in an independent sort of way. Do you think the public expects you to be active in other areas, we mentioned earlier, you know, the wilder shores of social media where it's not cockup theories you're going to be hearing there, it's conspiracy theories based on misuse of data. Is there any role that a statistics regulator could possibly take on in that arena?      EH  Absolutely. So I mentioned earlier that the way we often get triggered into this environment is when members of the public raised things with us. And I always think that's quite a solemn sort of responsibility. You know, you have a member of the public who's concerned about something and they care about it enough to contact us - use the “raise a concern” part of our website - so I always try and take it seriously. And sometimes they're complaining about something which isn't actually an official statistic. And in those circumstances, even if we say to them, “well, this isn't really an official statistic”, we will say, “but, applying our principles, this would be our judgement”. Because I think we owe it to those people who who've taken the time to care about a statistical usage, we owe it to take them seriously. And we have stepped in. Only recently we're looking at some claims about the impact of gambling, which are not from a government department, but from parts of the gambling industry. We also look at things from local government, who are not part of central government. So we do we do look at those things, Miles. It's a relatively small part of our work, but, as I say, our principles are universal and you've got to take seriously a situation in which a member of the public is concerned about a piece of evidence.     MF  Professor Spiegelhalter, what do you make of this regulatory function that the OSR pursues, are we unusual in the UK in having something along those lines?     DS  Ed probably knows better than I do, but I haven't heard of anybody else and I get asked about it when I'm travelling and talking to other people. I have no conflict of interest. I'm Non-Executive Director for the UK Stats Authority, and I sit on the regulation committee that oversees the way it works. So of course, I'm a huge supporter of what they do. And as described, it's a subtle role because it's not to do with performing, you know, and making a big song and dance and going grabbing all that attention but working away just to try to improve the standard of stats in this country. I think we're incredibly fortunate to have such a body and in fact, we know things are never perfect and there's always room for improvement of course, but I think we're very lucky to have our statistical system.     MF  A final thought from you...we're at a moment in time now where people are anticipating the widespread implementation of AI, artificial intelligence, large language models and all that sort of thing. Threat or opportunity for statistics, or both?    DS  Oh, my goodness me, it is very difficult to predict. I use GPT a lot in my work, you know, both for sort of research and making inquiries about stuff and also to help me do codings I'm not very good at. I haven't yet explored GPT-4's capacity for doing automated data analysis, but I want to, and actually, I'd welcome it. if it's good, if you can put some data in and it does stuff - that's great. However, I would love to see what guardrails are being put into it, to prevent it doing stupid misleading things. I hope that that does become an issue in the future, that if AI is automatically interpreting data for example, that it's actually got some idea of what it's doing. And I don't see that that's impossible. I mean, there were already a lot of guardrails in about sexist statements, racist statements, violent statements and so on. There's all sorts of protection already in there. Well, can't we have protection against grossly misleading statistical analysis?     MF  A future over the statistics watchdog perhaps?    DF  Quite possibly.    EH  Miles, I never turn down suggestions for doing new work.   MF  So we've heard how statistics are regulated in the UK, and covered the role of the media in communicating data accurately, and now to give some insight into what that might all look like from a journalist's perspective, it's time to introduce our next guest, all the way from California, award-winning journalist and data editor at Google, Simon Rogers. Simon, welcome to Statistically Speaking. Now, before you took up the role at Google you were actually at the forefront of something of a data journalism movement here in the UK. Responsible for launching and editing The Guardian's data blog, looking at where we are now and how things have come on since that period, to what extent do you reckon journalists can offer some kind of solution to online misinterpretation of information?    Simon Rogers   At a time when misinformation is pretty rampant, then you need people there who can make sense of the world and help you make sense of the world through data and facts and things that are true, as opposed to things that we feel might be right. And it's kind of like there is a battle between the heart and the head out there in the world right now. And there are the things that people feel might be right, but are completely wrong. And where, I think, Data Journalists can be the solution to solving that. Now, having said that, there are people as we know who will never believe something, and it doesn't matter. There are people for whom it literally doesn't matter, you can do all the fact checks that you want, and I think that is a bit of a shock for people, this realisation that sometimes it's just not enough, but I think honestly, the fact that there are more Data Journalists now than before...There was an EJC survey, the European Journalism Centre did a survey earlier this year about the state of data journalism. There are way more data journalists now than there were the last time they did the survey. It's becoming much more...it's just a part of being a reporter now. You don't have to necessarily be identified as a separate data journalist to work with data. So we're definitely living in a world where there are more people doing this really important work, but the need, I would say it has never been greater.     MF  How do you think data journalists then tend to see their role? Is it simply a mission to explain, or do some of them see it as their role to actually prove some theories and vindicate a viewpoint, or is it a mixture, are there different types of data journalists?     SR  I would say there were as many types of data journalists as there are types of journalists. And that's the thing about the field, there's no standard form of data journalism, which is one of the things that I love about it. That your output at the end of the day can be anything, it can be a podcast or it can be an article or a number or something on social media. And because of the kind of variety, and the fact I think, that unlike almost any other role in the newsroom, there really isn't like a standard pattern to becoming a data journalist. As a result of that, I think what you get are very different kind of motivations among very different kinds of people. I mean, for me, personally, the thing that interested me when I started working in the field was the idea of understanding and explaining. That is my childhood, with Richard Scarry books and Dorling Kindersley. You know, like trying to understand the world a little bit better. I do think sometimes people have theories. Sometimes people come in from very sophisticated statistical backgrounds. I mean, my background certainly wasn't that and I would say a lot of the work, the stats and the way that we use data isn't necessarily that complicated. It's often things like, you know, is this thing bigger than that thing? Has this thing grown? You know, where in the world is this thing, the biggest and so on. But you can tell amazing stories that way. And I think this motivation to use a skill, but there are still those people who get inured by maths in the same way that I did when I was at school, you know, but I think the motivation to try and make it clear with people that definitely seems to me to be a kind of a common thread among most of the data journalists that I've met.    MF  Do you think that journalists therefore, people going into journalism, and mentioning no names, as an occupation...used to be seen as a bit less numerous, perhaps whose skills tended to be in the verbal domain. Do you think therefore these days you've got to have at least a feel for data and statistics to be able to be credible as a journalist?     SR  I think it is becoming a basic skill for lots of journalists who wouldn't necessarily consider themselves data journalists. We always said eventually it is just journalism. And the reason is because the amount of sources now that are out there, I don't think you can tell a full story unless you take account of those. COVID's a great example of that, you know, here's a story that data journalists, I think, performed incredibly well. Someone like John Burn-Murdoch on the Financial Times say, where they've got a mission to explain what's going on and make it clear to people at a time when nothing was clear, we didn't really know what was going on down the road, never mind globally. So I think that is becoming a really important part being a journalist. I mean, I remember one of my first big data stories at the Guardian was around the release of the coins database – a big spending database from the government - and we had it on the list as a “data story” and people would chuckle, snigger a little bit of the idea that there'll be a story on the front page of the paper about data, which they felt to be weird, and I don't think people would be snickering or chuckling now about that. It's just normal. So my feeling is that if you're a reporter now, not being afraid of data and understanding the tools that are there to help you, I think that's a basic part of the role and it's being reflected in the way that journalism schools are working. I teach here one semester a year at the San Francisco Campus of Medill. There's an introduction to data journalism course and we get people coming in there from all kinds of backgrounds. Often half the class are just, they put their hands up if they're worried about math or scared of data, but somehow at the end of the course they are all making visualisations and telling data stories, so you know, those concerns can always be overcome.     MF  I suppose it's not that radical a development really if you think back, particularly from where we're sitting in the ONS. Of course, many of the biggest news stories outside of COVID have been data driven. think only of inflation for example, the cost of living has been a big running story in this country, and internationally of course, over the last couple of years. Ultimately, that's a data driven story. People are relying on the statisticians to tell them what the rate of inflation is, confirming of course what they're seeing every day in the shops and when they're spending money.     SR  Yeah, no, I agree. Absolutely. And half of the stories that are probably about data, people don't realise they're writing about data. However, I think there is a tendency, or there has been in the past, a tendency to just believe all data without questioning it, in the way that as a reporter, you would question a human source and make sure you understood what they were saying. If we gave one thing and that thing is that reporters would then come back to you guys and say ask an informed question about this data and dive into a little bit more, then I think we've gained a lot.     MF  So this is perhaps what good data journalists are bringing to the table, perhaps and ability to actually sort out the good data from the bad data, and actually, to use it appropriately to understand uncertainty and understand how the number on the page might not be providing the full picture.    SR   Absolutely. I think it's that combination of traditional journalistic skills and data that to me always make the strongest storytelling. When you see somebody, you know, who knows a story inside out like a health correspondent, who knows everything there is to know about health policy, and then they're telling a human story perhaps about somebody in that condition, and then they've got data to back it up - it's like the near and the far. This idea of the near view and the far view, and journalism being the thing that brings those two together. So there's the view from 30,000 feet that the data gives you and then the individual view that the more kind of qualitative interview that you get with somebody who is in a situation gives you. The two things together - that's incredibly powerful.    MF  And when choosing the data you use for a story I guess it's about making sound judgements – you know, basic questions like “is this a big number?”, “is this an important number?”     SR  Yeah, a billion pounds sounds like a lot of money, but they need to know how much is a billion pounds, is it more about a rounding error for the government.    MF  Yes, and you still see as well, outside of data journalism I stress, you still see news organisations making much of percentage increases or what looks like a significant increase in something that's pretty rare to start with.     SR  Yeah, it's all relative. Understanding what something means relatively, without having to give them a math lesson, I think is important.     MF  So this talk about supply, the availability of data journalism, where do people go to find good data journalism, perhaps without having to subscribe? You know, some of the publications that do it best are after all behind paywalls, where do we find the good stuff that's freely available?    SR  If I was looking from scratch for the best data journalism, I think there are lots of places you can find it without having to subscribe to every service. Obviously, you have now the traditional big organisations like the Guardian, and New York Times, and De Spiegel in Germany, there is a tonne of data journalism now happening in other countries around the world that I work on supporting the Sigma Data Journalism Awards. And over half of those entries come from small one or two people units, you know, practising their data journalism in countries in the world where it's a lot more difficult than it is to do it in the UK. For example, Texty in Ukraine, which is a Ukrainian data journalism site, really, and they're in the middle of a war zone right now and they're producing data journalism. In fact, Anatoly Barranco, their data editor, is literally in the army and on the frontline, but he's also producing data journalism and they produce incredible visualisations. They've used AI in interesting ways to analyse propaganda and social media posts and stuff. And the stuff happening everywhere is not just limited to those big partners behind paywalls. And what you do find also, often around big stories like what's happened with COVID, people will put their work outside of the paywall. But um, yeah, data is like an attraction. I think visualisation is an attraction for readers. I'm not surprised people try and monetize that, but there is enough going on out there in the world.    MF  And all that acknowledged, could the producers of statistics like the ONS, and system bodies around the world, could we be doing more to make sure that people using this data in this way have it in forms have it available to be interpreted? Is there more than we can do?     SR  I mean, there was the JC survey that I mentioned earlier, it's definitely worth checking out because one thing it shows is that 57% of data journalists say that getting access to data is still their biggest challenge. And then followed by kind of like lack of resources, time pressure, things like that. PDFs are still an issue out there in the world. There's two things to this for me, on one side it's like, how do I use the data, help me understand what I'm looking at. On the other side is that access, so you know, having more kind of API's and easy downloads, things that are not formatted to look pretty but formatted for use. Those kinds of things are still really important. I would say the ONS has made tremendous strides, certainly since I was working in the UK, on accessibility to data and that's a notable way, and I've seen the same thing with gov.us here in the States.    MF  Well it's good to hear the way the ONS has been moving in the right direction. Certainly I think we've been tough on PDFs.   SR Yes and to me it's noticeable. It's noticeable and you've obviously made a deliberate decision to do that, which is great. That makes the data more useful, right, and makes it more and more helpful for people.     MF  Yes, and at the other end of the chain, what about storing publishers and web platforms, particularly well you're at Google currently, but generally, what can these big platforms do to promote good data journalism and combat misinformation? I mean, big question there.    SR  Obviously, I work with Google Trends data, which is probably the world's biggest publicly available data set. I think a big company like Google has a responsibility to make this data public, and the fact that it is, you can download reusable datasets, is incredibly powerful. I'm very proud to work on that. I think that all companies have a responsibility to be transparent, especially when you have a unique data set. That didn't exist 20 years earlier, and it's there now and it can tell you something about how the world works. I mean, for instance, when we look at something like I mean, I've mentioned COVID before, but it's such a big event in our recent history. How people were searching around COVID is incredibly fascinating and it was important information to get out there. Especially at a time when the official data is always going to be behind what's actually happening out there. And is there a way you can use that data to predict stuff, predict where cases are going to come up... We work with this data every day and we're still just scratching the surface of what's possible with it.    MF   And when it comes to combating misinformation we stand, so we're told, on the threshold of another revolution from artificial intelligence, large language models, and so forth. How do you see that future? Is AI friend, foe, or both?     SR  I work for a company that is a significant player in the AI area, so I give you that background. But I think in the field of data, we've seen a lot of data users use AI to really help produce incredible work, where instead of having to read through a million documents, they can get the system to do it for them and pull out stories. Yeah, like any other tool, it can be anything but the potential to help journalists do their jobs better, and for good, I think is pretty high. I'm going to be optimistic and hope that that's the way things go.    MF  Looking optimistically to the future then, thank you very much Simon for joining us. And thanks also to my other guests, Professor Sir David Spiegelhalter and Ed Humpherson. Taking their advice on board then, when we hear or read about data through the news or experience it on social media, perhaps we should first always ask ourselves – do we trust the source? Good advice indeed.     You can subscribe to new episodes of this podcast on Spotify, Apple podcasts, and all the other major podcast platforms. You can also get more information, or ask us a question, by following the @ONSFocus on X, or Twitter, take your pick. I'm Miles Fletcher, from myself and our producer Steve Milne, thanks for listening.   ENDS 

James Tytko spoke with David Spiegelhalter to help solve listener John's musical mystery... Like this podcast? Please help us by supporting the Naked Scientists

On this episode of Angreement, Michelle and Katherine demand you throw them a party soon! They also angree about the youths and their feet, judge other people's coincidences, and learn more about the Gilbreths. “Gen Z Won't Let Anyone See Their Feet. Here's Why.” By Talia Ergas, Huffpost https://www.huffpost.com/entry/why-gen-z-wont-show-their-feet_l_64cd1b52e4b01796c06c0cc4#:~:text=The%20fear%20of%20having%20their,intentionally%20ugly%2Dcool%20style%20choice Georges Latour juggles devil sticks and pool cues: https://www.youtube.com/watch?v=kEAigCQiyms Clive Luther juggles devil sticks and tennis rackets: https://youtu.be/7xAjqxtUweU “A look inside Japan's obsession with bizarre mascots,” by James Datour, SBNation, https://www.sbnation.com/2020/3/18/21174767/japanese-mascots-yuru-chara Mondo Mascots Twitter account https://twitter.com/mondomascots Hunted Australia https://www.channel4.com/programmes/hunted-australia “3 moments that might convince you Edgar Allan Poe was a time traveler,” by Jake Offenhartz, Upworthy https://www.upworthy.com/3-moments-that-might-convince-you-edgar-allan-poe-was-a-time-traveler-rp2 “Are Coincidences Real?” by Paul Broks, The Guardian https://www.theguardian.com/world/2023/apr/13/are-coincidences-real “Coincidences and the Meaning of Life,” by Julie Beck, The Atlantic https://www.theatlantic.com/science/archive/2016/02/the-true-meaning-of-coincidences/463164/ “Cambridge Coincidences Collection,” by David Spiegelhalter https://understandinguncertainty.org/coincidences/index.php_page=7 “Behind the Picture: Picasso Draws With Light,” by Ben Cosgrove, Life Magazine, https://www.life.com/arts-entertainment/behind-the-picture-picasso-draws-with-light/

We talk to David Spiegelhalter, Emeritus Professor of Statistics at Cambridge University and prior Professor of the Public Understanding of Risk. Also known for appearances on Total Wipeout and Desert Island Discs!

If you've ever been lucky enough to meet David Spiegelhalter, or hear him talk in person or on TV or radio, you'll know he tells a great story. And the stories he told in his 2015 book Sex by numbers were fascinating and highly entertaining, as well giving us the tools to critically assess the statistics we read every day in the news. And sex is back in the news as the National Survey of Sexual Attitudes and Lifestyles that featured in his book is being conducted again this year. Who knows what stories will come out of the next survey? We were very happy to start 2023 with catching up with David (the first time in person since the pandemic!) at the Communicating mathematics for the public event that we were both speaking at in the Newton Gateway to Mathematics in Cambridge. We hope you enjoy this interview with him from 2015, where he gives us some of his favourite snippets from the book, and some easy ways you can think more critically about statistics. (You can also watch our interview as a video or read the associated article.)

Micromorts - units of risk equating to a one-in-a-million chance of death - can help answer these questions. Mathematician and statistician David Spiegelhalter has done the numbers and shares what he knows about your risk of death.

Um episódio em dia de jogo da seleção. Haja coração! Se você pudesse viajar no tempo, pra qual momento da sua vida você voltaria? Você contaria para um amigo(a) que ele(a) vive um relacionamento tóxico?Dicas do Felipe- The Influencer Bubble - How Money Works - https://youtu.be/t5yXuHJX2NkDicas do Rodolfo- A arte da estatística: Como aprender a partir de dados, por David Spiegelhalter - https://a.co/d/4pooXTe- Robert Downey, Sr., na Netflix - https://emprc.us/B9cIwVDicas da Bia- Toda luz que não podemos ver, de Anthony Doerr - https://a.co/d/1SGrhosDicas do Nelson- Harry & Meghan, na Netflix - https://emprc.us/hXr5AK- Versailles, na Netflix - https://emprc.us/JlyAoI

How can we make sense of what we're told about risk? We're bombarded with messages on subjects ranging from COVID to the economy from people that range from genuine experts to those with no expertise but strong opinions. On this episode, I'm speaking to Professor David Spiegelhalter.David is Chair of the Winton Centre for Risk and Evidence Communication within the Department of Pure Mathematics and Mathematical Statistics at Cambridge University. The Centre is dedicated to improving the way that quantitative evidence is used in society. Listeners in the UK will almost certainly have seen or heard David. Since the start of the pandemic he's been a regular fixture on TV and radio, helping to make sense of the things we're being told about the virus. In a world of self-appointed experts whose only qualification is from the University of YouTube and untrustworthy politicians telling us they're "following the science", he's been a voice of clarity and common sense. In our discussion, we explore what drives David's interest in statistics, why we like to see connections between things that might not actually be there, why the mantra of “following the science” is nonsensical and whether there is such a thing as coincidence. David also provides plenty of practical tips for communicating and interpreting messages about risk. As you might expect for someone who specialises in risk communication, David is really good at getting his message across in ways we can all understand. My huge thanks to long-time friend of the show Roger Miles, who helped to make this conversation possible.To find out more about David, visit his academic website: https://wintoncentre.maths.cam.ac.uk/about/people/professor-sir-david-spiegelhalter/or his personal website: https://www.statslab.cam.ac.uk/~david/You'll find his books in all good bookstores. For more information, visit:The Art of Statistics — https://www.penguin.co.uk/books/294857/the-art-of-statistics-by-spiegelhalter-david/9780241258767COVID by Numbers — https://www.penguin.co.uk/authors/126755/david-spiegelhalterFor video content, I recommend:Communicating statistics in the time of COVID — https://www.youtube.com/watch?v=JW9plVfanjoFalse Positives — https://www.youtube.com/watch?v=XmiEzi54lBIBe Prepared To Show Your Working — https://www.youtube.com/watch?v=E12_F4xeOHwIn our discussion, we also refer to the episode featuring Tim Harford on using Data to Make Smarter Decisions. You can hear that here: https://www.humanriskpodcast.com/tim-harford-on-using-data/

What story do the statistics tell about the pandemic? Sir David Spiegelhalter, the non-executive director the UK Statistics Authority, explores what lessons we've learned over the last two years.Once you've mastered the basics with Instant Genius, dive deeper with Instant Genius Extra, where you'll find longer, richer discussions about the most exciting ideas in the world of science and technology. Only available on Apple Podcasts.Produced by the team behind BBC Science Focus Magazine. Visit our website: sciencefocus.com See acast.com/privacy for privacy and opt-out information.

In this episode, Mark and Georgia spoke to Professor Sir David Spiegelhalter, who is currently Chair of the Winton Centre for Risk and Evidence Communication, based within the Department of Pure Mathematics and Mathematical Statistics at the University of Cambridge. Prior to this, he was the Winton Professor for the Public Understanding of Risk in the Statistical Laboratory within the same department. He completed his undergraduate degree in statistics at the University of Oxford, later moving to University College London to complete his MSc and PhD in mathematical statistics under the supervision of Sir Adrian Smith. His research interests include use of Bayesian methods in medical statistics, and the monitoring and comparing of clinical and public-health outcomes and their associated publication as performance indicators. Currently, he is working on improving the way in which risk and statistical evidence is taught and discussed in society. He has hosted and appeared on various TV and radio shows such as BBC Horizon and Desert Island Discs, and has also published several books. You can find Professor Spiegelhalter on Twitter @d_spiegel, or his personal home page: https://www.statslab.cam.ac.uk/~david/ (where you can find the video of him on Winter Wipeout!). The BlueSci Podcast is run by the Cambridge University Science Magazine. This episode was hosted by Georgia Nixon and Mark Grimes. Visit www.bluesci.co.uk to access our free magazine, and find out how to get involved. If you enjoyed this episode, please subscribe and leave a review or rating! we welcome your feedback and suggestions via email: podcast(at)bluesci.co.uk. You can also follow us on Twitter on @bluescipod or Instagram @bluescicam.

Support the show: https://www.buymeacoffee.com/datascienceharp Find David online: https://twitter.com/d_spiegel Read David's article "Will I live longer than my cat?": https://www.bbc.co.uk/news/magazine-19467491 Watch the video of this episode: https://youtu.be/pCWH97vBFmU Memorable Quotes from the show: [00:23:36] "...essentially what probability theory allows us to do is to make assumptions about how the world works, how the data is generated, and turn it and flip it around after we observe some data into statements about our uncertainty about underlying features of the world. We can do that, which of course is very explicit based on work indeed, where after processing data or uncertainty and it turns into uncertainty about the underlying quantities." Hightlights of the show: [00:01:29] Guest Introduction [00:03:08] Talk to us about how you first got interested in statistics and what was it that drew you to this field? [00:04:55] Why is it that it seems like mathematicians tend to dislike teaching statistics? [00:08:27] What is statistical science and what is it all about? [00:09:46] You talk about in your book, The Art of Statistics, how to handle problems and approach problems in statistics. You call the P, p, b, a C cycle. Tell us about that framework. [00:15:03] You mentioned in the book that statistics is to blame for the reproducibility and replication crises in science. Why? Why is that? [00:18:23] When we talk about induction and inductive inference, should the philosopher in us get worried at all about the problem of induction in statistics? [00:19:40] Tell our audience about the 'normal distribution'. [00:20:34] Do you have any examples of when inductive inference has failed in statistics that you could share with us? [00:22:15] Why do we need probability theory when we're doing statistics? [00:26:25] I think pouring into the Bayesian stuff is kind of taking a step back here, maybe first principles. But what is probability? How do we measure it? It seems like such a strange epistemological concept. [00:28:27] Can we say there's a at least some type of difference between epistemic probability and some physical or I believe you say aleatory? [00:30:03] Would there be a difference in the way that a philosopher or a statistician would interpret probability? [00:38:32] What's the Bayesian approach all about and why is it that courts in the UK are banning it or have banned it? [00:40:16] How is this (Bayesian approach) different from the frequentist approach to viewing probability? What's the central difference? [00:44:55] It seems like the prior distribution is something that makes base them so controversial. Why is that? [00:46:18] It seems like Bayes Theorem is the scientifically correct way to change your mind when you get new evidence, right? [00:48:18] David Deutsch mentioned lately about the Bayesian-ism, and he's having some qualms with Bayesian ism. He says that Bayesian-ism becomes controversial when you try to use it as a way to generate new ideas or judge one explanation against another. How do we reconcile that when we're faced with some epistemic. [00:49:51] About using it to help us in our everyday lives to make better decisions. How can we use Bayes in that context? [00:53:15] It is 100 years in the future. What do you want to be remembered for? Random Round [00:54:17] What do you believe that other people think is crazy? [00:55:02] What are you most curious about right now? [00:55:55] What are you currently reading? [00:58:33] What do you like most about your family? [00:58:53] What was your best birthday? Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: Support the show: https://www.buymeacoffee.com/datascienceharp YouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience Instagram: https://www.instagram.com/datascienceharp Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/datascienceharp

What does the future hold for statistics education? How can we improve public data literacy? Professor Sir David Spiegelhalter, PhD is Chair of the Winton Centre for Risk and Evidence Communication at University of Cambridge, dedicated to improving the way that quantitative evidence is used in society. He has been an applied statistician for over 40 decades and has been involved in several projects with important implications. His academic work has focused in Bayesian statistics, including being co-developer of BUGS and winBUGS, biomedical applications and science communication. He has won numerous awards for his work including being knighted in 2014. He is the author of a very popular book The Art of Statistics, Learning from Data. Our Data Science Zoominars feature interactive conversation with data science experts and a Q+A session moderated by Rafael A. Irizarry, PhD, Chair, Department of Data Science at Dana-Farber Cancer Institute.

Why do we, humans, communicate? And how? And isn't that a problem that to study communication we have to… communicate? Did you ever ask yourself that? Because  J.P. de Ruiter did — and does everyday. But he's got good reasons: JP is a cognitive scientist whose primary research focus is on the cognitive foundations of human communication. He aims to improve our understanding of how humans and artificial agents use language, gesture and other types of signals to effectively communicate with each other. Currently he has one of the two Bridge Professorship at Tufts University, and has been appointed in both the Computer Science and Psychology departments. In this episode, we'll look at why Bayes is helpful in dialogue research, what the future of the field looks like to JP, and how he uses PyMC in his own teaching. Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ (https://bababrinkman.com/) ! Thank you to my Patrons for making this episode possible! Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai and Steven Rowland. Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;) Links from the show: JP's page: https://sites.tufts.edu/hilab/people/ (https://sites.tufts.edu/hilab/people/) Projecting the End of a Speaker's Turn – A Cognitive Cornerstone of Conversation: https://www.researchgate.net/publication/236787756_Projecting_the_End_of_a_Speaker's_Turn_A_Cognitive_Cornerstone_of_Conversation (https://www.researchgate.net/publication/236787756_Projecting_the_End_of_a_Speaker's_Turn_A_Cognitive_Cornerstone_of_Conversation) Cognitive and social delays in the initiation of conversational repair: https://journals.uic.edu/ojs/index.php/dad/article/view/11388 (https://journals.uic.edu/ojs/index.php/dad/article/view/11388) Using uh and um in spontaneous speaking: http://www.columbia.edu/~rmk7/HC/HC_Readings/Clark_Fox.pdf (http://www.columbia.edu/~rmk7/HC/HC_Readings/Clark_Fox.pdf) Status of Frustrator as an Inhibitor of Horn-Honking Responses: https://www.tandfonline.com/doi/abs/10.1080/00224545.1968.9933615 (https://www.tandfonline.com/doi/abs/10.1080/00224545.1968.9933615) A Simplest Systematics for the Organization of Turn-Taking for Conversation: https://www.jstor.org/stable/412243 (https://www.jstor.org/stable/412243) Richard McElreath, Science Before Statistics – Intro to Causal Inference: https://www.youtube.com/watch?v=KNPYUVmY3NM (https://www.youtube.com/watch?v=KNPYUVmY3NM) The Prosecutor's fallacy: https://en.wikipedia.org/wiki/Prosecutor%27s_fallacy (https://en.wikipedia.org/wiki/Prosecutor%27s_fallacy) The Monty Hall problem: https://en.wikipedia.org/wiki/Monty_Hall_problem (https://en.wikipedia.org/wiki/Monty_Hall_problem) LBS #50, Ta(l)king Risks & Embracing Uncertainty, with David Spiegelhalter:...

The topic of inflation has focused on the Federal Reserve's response, but how are companies responding? (0:30) Emily Flippen and Maria Gallagher analyze Walmart's aggressive push to hire more full-time truckers, as well as: - Meta Platforms working on a currency for the metaverse - Twitter's incoming director: Elon Musk - Berkshire-Hathaway's new stake in HP - Rite Aid's tenuous future - Coca-Cola's latest flavor innovations (19:00) John Ourand from the Sports Business Journal discusses Tiger Woods' impact on The Masters' ratings, Apple and Amazon striking deals with Major League Baseball, and why ESPN has one of the best TV deals for live sports. (33:30) Maria and Emily recommend three books (The Ascent of Money by Niall Ferguson, The Art of Statistics: How to Learn from Data by David Spiegelhalter, Principles for Dealing with the Changing World Order: Why Nations Succeed and Fail by Ray Dalio) and share two stocks on their radar: Airbnb and Etsy. Got a question about stocks?  Call our voicemail: 703-254-1445 Stocks discussed: WMT, COST, AMZN, TGT, FB, TWTR, TSLA, BRK, HP, BBY, RAD, KO, T, PARA, AAPL, CMCSA, AMZN, DIS, ABNB, ETSY Host: Chris Hill Guests: Emily Flippen, Maria Gallagher, John Ourand Engineers: Steve Broido, Rick Engdahl

Today, 23 March 2022, marks two years since the UK locked down for the first time in the COVID-19 pandemic.  We relaunch the Plus podcast by looking back to where our pandemic coverage all began, by revisiting our podcast from April 2020. Back in March and April 2020 one thing was on everybody's mind: the novel coronavirus - now better known as COVID-19. In this podcast we spoke to two people who have become very familiar to many of us over the last two years.  We reported on our first COVID-19 conversation with Julia Gog, an epidemiologist who has been informing the Science Advisory Group for Emergencies (SAGE).  Julia is now a close collaborator with us here at Plus as part of the JUNIPER modelling consortium (as we'll find out in the next podcast).  We also spoke with David Spiegelhalter, Chair of the Winton Centre for Risk and Evidence Communication, who is now a familiar figure through his frequent appearance on radio, TV and in print giving clear and calm explanations about the numbers behind the pandemic.  David told us about how to communicate science during a crisis.  And, at the end of the podcast, we had a go at explaining the maths of herd immunity in one minute. To find out more about the topics covered in this podcast see: Communicating the coronavirus crisis How can maths fight a pandemic? A call to action on COVID-19 Taking the pandemic temperature And you can find out much more in all our other coverage of the COVID-19 pandemic The music in this podcast comes from the band eusa. The track is called Now we are all SoB's.

People's perception of risk can vary greatly from person to person, making it challenging for healthcare professionals to communicate benefits and harms of medicines in a balanced fashion. Alexandra Freeman from the Winton Centre for Risk and Evidence Communication discusses how to give patients the information they need to decide what's best for them.Tune in to find out:Why people perceive risks so differentlyWhy medical communicators should strive to inform rather than persuadeHow to communicate in a trustworthy fashionWant to know more?There is no right way to communicate evidence to patients, but there are a few things you can do to avoid getting it wrong.Conventional communication techniques are good for persuading people – but when the aim is to inform, the principles of evidence communication should be applied instead.Graphics can help people translate abstract numbers into contextualised risks they can relate to, like these visuals that illustrate the risk of blood clots with the AstraZeneca COVID-19 vaccine.These evidence-based guidelines can help professional communicators illustrate the personalised risk of dying from COVID-19.The Winton Centre offers plenty of resources on risk and evidence communication, including free e-learning courses for healthcare professionals, the Risky Talk podcast with statistician David Spiegelhalter, and the RealRisk tool to help healthcare professionals and communicators extract the right statistics from academic papers.For more on communicating benefits and harms in pharmacovigilance, revisit this Drug Safety Matters episode on vaccine safety communication.Join the conversation on social mediaFollow us on Twitter, Facebook or LinkedIn and share your thoughts about the show with the hashtag #DrugSafetyMatters.Got a story to share?We're always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!About UMCRead more about Uppsala Monitoring Centre and how we work to advance medicines safety.

In dieser Episode ist wieder Dr. Lukas Lang zu Gast. Wir sprechen über Data Science und Machine Learninig (auch »artificial intelligence« genannt). Das ist ein Themenbereich, der sehr viel Potential für unsere Zukunft hat, aber wie alle diese Themenbereiche auch eine Menge an Gefahren, Herausforderungen und Hypes generiert. Lukas ist ein perfekter Gesprächspartner für dieses Thema, weil er sowohl in der Spitzenforschung tätig war als auch in der industriellen Praxis mit diesen Themen beschäftigt ist. Diese Mischung scheint mir bei komplexen technischen Fragestellungen und Problemen sehr nützlich zu sein. Lukas hat nach seinem Studium der Informatik eine Promotion im Spezialgebiet Computational Science gemacht. Anschließend war er mehrere Jahre in der universitären Forschung im Bereich der mathematischen Bild- und Datenanalyse tätig, zuletzt an der Universität Cambridge. Seine Arbeit hat Anwendungen in der medizinischen Bildgebung, in der Molekular- und Zellbiologie, und in der Computer Vision. Derzeit leitet er den Geschäftsbereich »Data Science and AI« eines Spin-Offs des internationalen Industriekonzerns Voestalpine. Sein Team arbeitet an der Umsetzung von Daten-Projekten in der Erzeugung und Verarbeitung von Spezialmetallen, und am Aufbau eines globalen Data Science Programms für die Produktionsstandorte. Wir haben dieses umfangreiche Thema in zwei Episoden aufgeteilt: In der ersten Episode beginnen wir das Thema Data Science einzuführen, auch anhand einiger Beispiele, beginnend mit historischen Beispielen sowie Anwendungsfällen der heutigen Zeit. Wir spannen dabei den Bogen von Tycho Brahe und Florence Nightingale bis zu modernen Sprachassistenten und Entscheidungsunterstützung im Militär und zivilen Bereich. Dann gibt Lukas einen Überblick über wesentliche Prinzipien und Begriffe, die in diesem Zusammenhang immer wieder auftreten, wie Datascience, die Rolle der klassischen Statistik, Modellierung, Visualisierung, EDA, AI, KI, machine learning, multivariate statistik, Datenqualität und vieles mehr.  Wir sprechen dann über die These die seit einiger Zeit im Raum steht, dass man dank Daten und »AI« ja keine Modelle, keine Theorie mehr benötigt — The End of Theory —, sondern einfach aus Daten lernt und das wäre hinreichend für die wissenschaftliche Betrachtung der Welt. Wir diskutieren dann Möglichkeiten, Geschäftsmodelle und Grenzen von Machine Learning und Data Science. Wer trifft heute überhaupt Entscheidungen und was ist die Rolle und Funktion eines Data Scientists? Sollten Menschen immer das letzt Wort bei wesentlichen Entscheidungen haben? Ist das überhaupt (noch) realistisch? Welche Rolle spielen regulatorische Maßnahmen wie das aktuelle EU-Framework? In der zweiten Episode werden wir darauf aufbauend die Frage stellen, wie viel der aktuellen Behauptungen in diesem Feld Realität und wie viel Hype ist. Was können wir in der Zukunft zu erwarten — sowohl im positiven wie auch im negativen? Was sind dominierende Forschungsfragen und wo Grenzen liegen, unerwartete Effekte auftreten, und welche ethischen Fragen durch diese neuen Möglichkeiten zu diskutieren. xkcd Cartoon Konkret gibt es das Spannungsfeld zwischen Datensparsamkeit und der Idee alles zu sammeln, weil wir das irgendwie in der Zukunft für uns nutzen können. Aber will der Data Scientists überhaupt in Daten untergehen? Führen mehr Daten zu besseren Entscheidungen? Wir diskutieren wieder anhand konkreter Beispiele für gute und problematische Anwendungen wie predictiver Policing, Mapping und »KI« für militärische Dronenpiloten. Welche individuelle Verantwortung leiten wir daraus für Techniker ab? Wie geht Lukas selbst mit diesen Herausforderungen um?  Referenzen Lukas Lang Persönliche Webseite von Lukas Andere Episoden Episode 40: Software Nachhaltigkeit, ein Gespräch mit Philipp Reisinger Episode 37: Probleme und Lösungen Episode 32: Überleben in der Datenflut – oder: warum das Buch wichtiger ist als je zuvor Episode 31: Software in der modernen Gesellschaft – Gespräch mit Tom Konrad Episode 25:Entscheiden unter Unsicherheit Episode 19: Offene Systeme – Teil 1 und Episode 20, Teil 2 Episode 6: Messen, was messbar ist? Fachliche Referenzen Adhikari, DeNero, Jordan, Interleaving Computational and Inferential Thinking: Data Science for Undergraduates at Berkeley Melanie Mitchell, Artificial Intelligence: A Guide for Thinking Humans (2020) Michael I. Jordan, The revolution hasn't happened yet Hannah Fry, What data can't do Peter Coy, Goodhart's Law Rules the Modern World. Here Are Nine Examples  Roberts et al., Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans  Antun et al., On instabilities of deep learning in image reconstruction and the potential costs of AI Use of AI in breast cancer detection: 94% of AI systems evaluated in these studies were less accurate than a single radiologist, and all were less accurate than consensus of two or more radiologists Lukas Lang, What is Data Science? Seth Stephens-Davidowitz, Everybody Lies Evgeny Morozov, To Save Everything, Click here (2014) Meredith Broussard, Artificial Unintelligence (2018) Cathy O‘Neill, Weapons of Maths destruction (2017) Richard David Precht, Künstliche Intelligenz und der Sinn des Lebens (2020) Jerry Z Muller, The Tyrrany of Metrics (2018) Joseph Weizenbaum, Computermacht und Gesellschaft (2001) Margaret Heffernan, Uncharted: How to Map the Future (2021) Edward Snowden, Permanent Record (2019) Shoshanna Zuboff, Surveillance Capitalism (2019) Hartmut Rosa, Unverfügbarkeit (2020) Duncan J Watts, Everything is obvious, once you know the answer (2011) Gerd Gigerenzer, Klick: Wie wir in einer digitalen Welt die Kontrolle behalten und die richtigen Entscheidungen treffen - Vom Autor des Bestsellers »Bauchentscheidungen« (2021) Byung-Chul Han, Im Schwarm, Ansichten des Digitalen (2015) Marinanne Bellotti, A.I. is solving the wrong problem Hannah Fry, Hello World: How to be Human in the Age of Algorithms (2018) Hannah Fry, What Statistics Can and Can't Tell Us About Ourselves, The New Yorker (2019) David Spiegelhalter, The Art of Statistics: Learning from Statistics (2020) James, Witten, Hastie & Tibshirani. Introduction to Statistical Learning (2021) The end of theory: The data deluge makes the scientific method obsolete. Wired 6/2008 Rutherford and Fry on Living with AI: The Biggest Event in Human History Deep Mind, The Podcast David Donoho, 50 Years of Data Science, Journal of Computational and Graphical Statistics (2017) Stuart Russel and Peter Norving, Artificial Intelligence, A Modern Approach, Berkely Textbook (2021) Michael Roberts et al, Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans, Nature Machine Intelligence (2021) Neil Thompson, Deep Learning's Diminishing Returns, The Cost of Improvement Is Becoming Unsustainable, IEEE Spectrum (2021)

Professor Sir David Spiegelhalter specialises in medical statistics. He is the Chair of the Winton Centre for Risk and Evidence Communication at Cambridge University, and one of the most frequently cited experts in his field. During the Covid 19 pandemic, he has made regular appearances as a broadcaster and newspaper commentator, analysing and explaining complex data for a general audience. David was born in Barnstable, the youngest of three children. After studying maths at Oxford University and University College London, he spent a year teaching at the University of Berkeley, California before returning to the UK. He has also worked in the field of computer-aided diagnosis. His expertise was called upon in the Bristol Royal Infirmary Inquiry and the Harold Shipman Inquiry. He was knighted in 2014 for his services to medical statistics. Presenter Lauren Laverne Producer Sarah Taylor

Boris Johnson offers a 'heartfelt apology' after for attending a lockdown gathering in the Downing Street garden. Ben Lake, Plaid Cymru MP for Ceredigion tells Bloomberg Westminster's Yuan Potts that if he himself had broken the rules he'd have to resign. But he says the Prime Minister has weathered similar storms in the past. Plus: Can we trust the Covid numbers? Star statistician David Spiegelhalter on omicron, hospitalizations and why the data on deaths should be taken with a 'huge pinch of salt.' See omnystudio.com/listener for privacy information.

David Spiegelhalter is an expert on medical statistics. He was the president of the Royal Statistical Society and is Chair of the Winton Centre for Risk and Evidence communication. He is also a World Champion, in a version of pool called Loop and hosts his own podcast, Risky Talk. David has a new book out (with Anthony Masters), COVID by Numbers, which is an excellent book on COVID statistics. This follows his previous bestseller, the Art of Statistics. David discusses what was most surprising and misunderstood about COVID statistics. David emphasises how numbers can be emotional and weaponised and what we can do to protect ourselves. We chat about what thinking about risk and techniques we should teach children and think about in every day life. Ideas such as baseline risk and absolute vs relative risk. We think about unintended consequences, the agency challenges of regulators and how to think of a range of risk. David explains fat tails and extreme values and that, for instance, AI risk is an extreme existential risk but perhaps over rated. I learn about the “Rose Paradox” and “Cromwell's law”, in statistics. The Rose paradox suggests policy might be useful at a general population level but not at an individual basis. For instance, government messages about drinking less and things like that can be rational at the population level and yet it's also rational for individuals to take no notice of it. Cromwell's law implies many life events are not 0% or 100% and you should take that into account in decision making. Or, in plain English, you should always imagine there's something you haven't thought of. We discuss the risks of alcohol and touch on air pollution and cholesterol (statin drugs), and how to think about medical statistics. David explains the attraction and beauty of stained glass art. David ends with life advice about enjoying life and taking (good, well-managed) risks in order to have a fulfilling life. Transcript and video are available here.

In this episode, Geoff Allen speaks with Ilan Goodman about the intersection of philosophy and science. They discuss: Ilan's background as a philosopher, podcaster and actor; scientific philosophy versus ‘pure philosophy'; Patricia Churchland's views on philosophy and neuroscience; philosophy as a mapping exercise; scientific realism and mind-independent truths; the distinctions between philosophy and science; causality at the level of quantum particles; communicating science to the public; the motivations for podcasting; the current state of intellectual discourse; the challenges of communicating coronavirus information to the public; and other topics. Ilan Goodman is a podcaster, producer and communicator of science. He works as a producer for science-focused podcasts, including CrowdScience (BBC World Service), The Curious Cases of Rutherford and Fry (BBC R4) and Azeem Azhar's Exponential View. In a past role, he worked at the Winton Centre for Risk and Evidence Communication, working as a producer for Risky Talk with David Spiegelhalter. Ilan is the host and producer of NOUS the podcast, which explores questions about the mind through philosophy, psychology and neuroscience. Ilan is also an actor, and he has appeared extensively on stage, in TV shows and in feature films. Ilan holds an MSc in History and Philosophy of Science from UCL, and this common ground formed the basis for much of our conversation. Ilan holds Bachelor degrees in Experimental Psychology & Philosophy (University of Oxford) and Acting (Royal Academy of Dramatic Art). *** Follow Extrapolator on social media for all the latest news: instagram.com/extrapolatorpod facebook.com/extrapolatorpod linkedin.com/company/extrapolator

Several European nations have suspended the use of the AstraZeneca vaccine after a Danish woman died from blood clots following her first shot. However, doctors and scientists maintain there is no link and the vaccine is in fact completely safe. Professor of Global Health at UCL Dr. Anthony Costello and statistician David Spiegelhalter join Christiane Amanpour to discuss the impact of this decision. Then turning to the shadow pandemic of violence against women, the kidnap and murder of 33-year-old British woman Sarah Everard has shocked the nation and sparked a conversation about sexual assault, harassment and women’s safety. Mandu Reid, the leader of the Women's Equality Party, and Jackson Katz, an anti-violence educator, explain why it’s so important we reframe the conversation and hold men accountable. Our Michel Martin speaks to Connecticut Congresswoman Jahana about her 10 year experience as a teacher and the unprecedented challenges that teachers have faced over the past year.To learn more about how CNN protects listener privacy, visit cnn.com/privacy

How do we live with risk? How do we quantify it? How do we talk about it? Can we understand uncertainty better? David has made this his life's work.David Spiegelhalter is Winton Professor of the Public Understanding of Risk at the University of Cambridge and Senior Scientist in the MRC Biostatistics Unit.His background is in medical statistics, particularly the use of Bayesian methods in clinical trials, health technology assessment and drug safety.He led the statistical team in the Bristol Royal Infirmary Inquiry and also gave evidence to the Shipman Inquiry.  In his post, he leads a small team which attempts to improve the way in which the quantitative aspects of risk and uncertainty are discussed in society.He works closely with the Millennium Mathematics Project in trying to bring risk and uncertainty into education.He gives many presentations to schools and others, advises organisations on risk communication, and is a regular newspaper columnist on current risk issues.—Recorded live at the global event in Cardigan, west Wales in 2010.Watch David's full talk here: www.thedolectures.com/talks/david-spiegelhalter-if-you-can-calculate-risk-you-can-make-better-judgments

These are uncertain times. The British Prime Minister Theresa May has survived a vote of confidence in her leadership, but the future of her Brexit deal remains unknown. In the US, Donald Trump faces a hostile Congress and multiple legal threats to his presidency. Meanwhile the IPCC says the entire planet must urgently address the existential challenge of climate change, yet the path forward remains littered with obstacles.What is the best way to weather all this uncertainty? In a programme first aired in 2016, Manuela Saragosa gets advice from David Tuckett, professor and director of the Centre for the Study of Decision-Making Uncertainty at University College London. Plus, David Spiegelhalter, Winton professor for the Public Understanding of Risk in the Statistical Laboratory, at the University of Cambridge, explains the difference between risk and uncertainty. Lt Col Steven Gventer of the US Army tells us how soldiers are trained to deal with uncertainty in war. And, Will Borrell, founder and owner of Vestal Vodka and the owner of the Ladies & Gents bar in London, recalls how his customers reacted on the evening after the UK voted to leave the European Union. (Picture: British Prime Minister Theresa May at the opening day of the G20 Summit in Argentina; Credit: Amilcar Orfali/Getty Images)

Do e-cigarettes make quitting smoking more difficult? Research last month claimed to show that e-cigarettes harm your chances of quitting smoking. The paper got coverage world-wide but it also came in for unusually fierce criticism from academics who spend their lives trying to help people quit. It's been described as 'grossly misleading' and 'not scientific'. We look at what is wrong with the paper and ask if it should have been published in the first place. A campaign of dodgy statistics Are American presidential hopefuls getting away with statistical murder? We speak to Angie Drobnic, Editor of the US fact-checking website Politifact, about the numbers politicians are using - which are not just misleading, but wrong. Will missing a week of school affect your GCSE results? Recently education minister Nick Gibb said that missing a week of school could affect a pupil's GCSE grades by a quarter. We examine the evidence and explore one of the first rules of More or Less – 'correlation is not causation'. We interview Stephen Gorard, Professor of Education at Durham University. What are the chances that a father and two of his children share the same birthday? A loyal listener got in touch to find out how rare an occurrence this is. Professor David Spiegelhalter from the University of Cambridge explains the probabilities involved.Presenter: Tim Harford Producer: Charlotte McDonald

With the publication of the widest survey of sexual behaviour since the Kinsey Report, Matthew Sweet picks apart the data with its author, David Spiegelhalter, and New Generation Thinker, Fern Riddell, author of The Victorian Guide to Sex. Nick Broomfield discusses his latest documentary, Tales of the Grim Sleeper, about a serial killer in LA which exposes the deep divide still evident in America today. Plus, Queen Mary's Matt Rubery on the fascinating history of the audio book.

All pupils at infant schools in England are to get free school lunches from next September, but does the evidence prove free dinners improve results? 'I accept every time I get in my car, there's a 20% chance I could die' - it's a line from the Formula 1 hit film, Rush, but was it really true for 1970s racing drivers? The government wants shops to start charging for plastic bags and the Deputy Prime Minister Nick Clegg says a plastic bag takes 1,000 years to degrade, but More or Less finds the environmental facts about plastic bags are much less certain than that statistic suggests. And do the health benefits of cycling outweigh the risk of injury? Professor David Spiegelhalter goes through the numbers.

July has seen train crashes in Canada, Pakistan, France, Spain and Switzerland. Inside Science asks if this is a trend or just a coincidence. Professor David Spiegelhalter, an expert in the public perception of risk, explains whether there is such a thing as a 'crash season'. Microbiologists working on the Mary Rose in Portsmouth have discovered a new type of metal-eating bacteria which is damaging the ship's wooden timbers. Reporter Gaia Vince goes behind the scenes at Portsmouth's Historic Dockyards to find out how conservation scientists have saved the ship. Last week Manchester hosted the 2013 International Congress of History of Science Technology and Medicine, the biggest ever meeting of historians of science from around the world. The keynote speech was given by Prof Hasok Chang of the University of Cambridge, urging his colleagues to put "Science back in History of Science". Inside Science asked him if there should also be more history in the practice of science... Finally, Dr Marek Kukula Public Astronomer at the Greenwich Observatory shows us his instrument - the 28inch refracting telescope which historians at the time likened to a Spanish onion, or the Taj Mahal.

Tim Harford on income inequality in the UK, and elsewhere. He speaks to Professor Sir Tony Atkinson of Oxford University; Stewart Lansley, author of 'The Cost of Inequality'; and Professor Donald Boudreaux of George Mason University. Also, David Spiegelhalter, the Winton Professor of the Public Understanding of Risk at Cambridge University explains why he took on what could be his riskiest venture to date - appearing on BBC One's Winter Wipeout. Plus, the magic of maths with magician and Stanford maths professor Persi Diaconis.