Miodrag Lovrić is an enormously energetic statistician and educator. He persuaded 700 scholars from 110 countries to contribute to the massive four-volume second edition of the International Encyclopedia of Statistical Science (Springer, 2025).
Now he is close to completing Statistical Thinking for the AI Era, which comprises Vol. 1: Foundations and Inference, and Vol. 2: Advanced Methods–each with about 15 chapters and 520 pages. The chapters I have seen indicate it’s a terrific textbook.
Miodrag has lived and worked on at least four continents and takes a wholeheartedly global focus in both those major works. The textbook starts with Forewords from many continents. Miodrag generously invited me to contribute a foreword from Australia–which appears below. As you see, I slipped in mention that I’m an Antarctic tragic, even if it’s a big claim that I speak also from that seventh continent!
Foreword From Australia and Antarctica
I’ve been an Antarctic tragic since I was a boy. I’ve participated in citizen science during two voyages south. I’ve observed glacial retreat and penguin colonies dying. I read about giant datasets analysed by AI-assisted statistical modelling of changes to sea ice, weather, and much else including terrifying tipping points that will determine catastrophic changes our children must endure.
I’ve given a well-received statistics talk to researchers at the Institute for Marine and Antarctic Studies in a building on the wharf where Antarctic supply ships berth. All around us were whale skulls, ancient sledges, and other memorabilia. I feel I can claim at least a little justification for offering this piece on behalf of the seventh continent, as well as speaking from Australia.
Statistics is about communication and therefore the province of psychologists. Researchers publish representations of their results and readers, ranging from researchers to politicians, policymakers, and ordinary people, draw conclusions from those representations. However, vast industries misrepresent research results as they seek to persuade us to support autocrats, eat unhealthy food, buy things we don’t want that trash the planet, and burn fossil fuels to destroy the chance our children will inherit a liveable world.
My students and I studied people’s misconceptions of basic statistical concepts. That was statistical cognition, which has now developed hugely into metascience—a wonderfully interdisciplinary field that investigates how research is done, and should be done to be more trustworthy. For example, it studies how paper mills use AI tools to generate fake manuscripts that desperate researchers buy then submit to journals. Quality journals use AI tools and much editorial expertise to try to weed out fakes before these criminally pollute the research literature—much of it on life-and-death issues.
Around 2014 Open Science emerged: replication is central; this requires meta-analysis to synthesise results; this requires point and interval estimates, and statistical significance is irrelevant and often damaging. My statistics textbooks advocate estimation and meta-analysis. The intro book, with Bob Calin-Jageman, integrates estimation and Open Science all through. The second edition (Routledge, 2024) includes Bob’s wonderful open source software that’s ideal for beginners and researchers aiming for good estimation and Open Science practices. At thenewstatistics.com is more information, also the significance roulette simulation, which dramatizes a highly misleading feature of p values that few researchers appreciate. The good news is that these new approaches are found accessible by students and are a delight to teach.
I conclude that everyone should have a basic understanding of evidence and statistics. This book is broad in scope, well-informed, and future-focussed. For example, it explains traditional, Bayesian, and randomisation frameworks for estimation, any of which can support Open Science. As I read, I’m often applauding the approaches Professor Lovrić has chosen. With this book he is making an enormous contribution to statistics education around the world.
Geoff