What do you think?
Rate this book
How to Expect the Unexpected: The Science of Making Predictions―and the Art of Knowing When Not To
A “vivid, wide-ranging, and delightful guide” (bestselling author Tim Harford) for understanding how and why predictions go wrong, with practical tips to give you a better chance of getting them right
How can you be 100 percent sure you will win a bet? Why did so many Pompeians stay put while Mount Vesuvius was erupting? Are you more likely to work in a kitchen if your last name is Baker? Ever since the dawn of human civilization, we have been trying to make predictions about what the world has in store for us. For just as long, we have been getting it wrong. In How to Expect the Unexpected, mathematician Kit Yates uncovers the surprising science that undergirds our predictions—and how we can use it to our advantage. From religious oracles to weather forecasters, and from politicians to economists, we are subjected to poor predictions all the time. Synthesizing results from math, biology, psychology, sociology, medicine, economic theory, and physics, Yates provides tools for readers to understand uncertainty and to recognize the cognitive biases that make accurate predictions so hard to come by. This book will teach you how and why predictions go wrong, help you to spot phony forecasts, and give you a better chance of getting your own predictions correct.
How can you be 100 percent sure you will win a bet? Why did so many Pompeians stay put while Mount Vesuvius was erupting? Are you more likely to work in a kitchen if your last name is Baker? Ever since the dawn of human civilization, we have been trying to make predictions about what the world has in store for us. For just as long, we have been getting it wrong. In How to Expect the Unexpected, mathematician Kit Yates uncovers the surprising science that undergirds our predictions—and how we can use it to our advantage. From religious oracles to weather forecasters, and from politicians to economists, we are subjected to poor predictions all the time. Synthesizing results from math, biology, psychology, sociology, medicine, economic theory, and physics, Yates provides tools for readers to understand uncertainty and to recognize the cognitive biases that make accurate predictions so hard to come by. This book will teach you how and why predictions go wrong, help you to spot phony forecasts, and give you a better chance of getting your own predictions correct.
448 pages, Hardcover
First published October 31, 2023
50 people are currently reading
682 people want to read
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
Displaying 1 - 26 of 26 reviews
August 2, 2023
The topic here is one everyone is interested in - getting a better handle on the future, and it's an interesting read. Arguably Kit Yates' title is a touch misleading. This isn't a 'how to' book - after reading it, you won't be any better at doing anything, but you may be less likely to make some popular errors.
My background is in Operational Research, which includes a lot on forecasting and mathematical prediction, so I was slightly disappointed that this isn't really covered here. Instead it gives us mostly ways that we instinctively get predictions wrong, so it's arguably more a psychology book that a mathematical one. There have been quite a few others that tread the path of uncovering our biases, for example with a mathematical approach in Jordan Ellenberg's How Not to be Wrong and with a more psychological twist in Richard Nesbitt's Mindware. But Yates has a particular focus on our tendency to assume linearity - that things will broadly continue the way they always have. By bringing in plenty of examples where this isn't the case - it's very often true in reality - including chaotic systems, he gives us a fresh viewpoint.
For me, the best chapter was 'reading between the lines', where Yates focuses most directly on non-linearity and really unpacks what's happening in some real world examples. And there were plenty of others with interesting examples and observations in other chapters - but I did have a few issues.
Occasionally Yates makes a statement that is hard to back up. Some of this, as is often the case with academics dipping a toe into popular science, was on historical matters - we are told 'It was will into the Middle Ages before the spherical view of the world became the predominant theory.' This just isn't true. I think he is also wrong about the millennium bug, calling it a self-defeating impact from predictions. The idea is that because of all the effort that was put in, there were few big problems, so people thought it was overhyped. I was consulting for the IT department of a global company at the time, and the reality was far more nuanced - the analysis was that it genuinely was overhyped, in that far too much was spent on checking non-critical systems that can have failed relatively painlessly, where a more effective approach would have been only to check mission- or safety-critical systems and leave the rest to fail and be fixed if necessary.
On other occasions, Yates provides a lack of explanation. For example, he introduces Benford's law, without telling us why it occurs. Some of the material was a little dull - I was particularly disappointed with the chapter on game theory, which failed to capture the intriguing nature of the subject and didn't explain enough for the reader to get their head around what was going on. Bearing in mind a lot of the book is based on psychological research, I was really surprised there was no mention of the replication crisis (surely in itself demonstrating a glaring lack of ability to predict the future) - I would be surprised if some of the studies he cites haven't failed to be capable of reproduction, or weren't based on far too small a sample to be meaningful. At the very least, this should be discussed in a book based on such studies.
The linearity bias isn't the only one that Yates covers, though most of the ones mentioned tie into it. As is always the case with books like this, it proved very interesting to read about, but I very rapidly forgot what all the biases are (again), and found it difficult to think of practical applications of what I've read. It's fine if you are a business or government wanting to deal with uncertainty (though even there, the book isn't a practical guide), but I think it's very unlikely to make much difference to the way we go about making predictions about the future in our everyday lives, beyond 'don't bother'.
Overall, this is an interesting topic and Yates presents a novel approach and does a good job of getting the reader to appreciate the dangers of relying on linearity. The book does have a few issues, but is still well worth a read.
My background is in Operational Research, which includes a lot on forecasting and mathematical prediction, so I was slightly disappointed that this isn't really covered here. Instead it gives us mostly ways that we instinctively get predictions wrong, so it's arguably more a psychology book that a mathematical one. There have been quite a few others that tread the path of uncovering our biases, for example with a mathematical approach in Jordan Ellenberg's How Not to be Wrong and with a more psychological twist in Richard Nesbitt's Mindware. But Yates has a particular focus on our tendency to assume linearity - that things will broadly continue the way they always have. By bringing in plenty of examples where this isn't the case - it's very often true in reality - including chaotic systems, he gives us a fresh viewpoint.
For me, the best chapter was 'reading between the lines', where Yates focuses most directly on non-linearity and really unpacks what's happening in some real world examples. And there were plenty of others with interesting examples and observations in other chapters - but I did have a few issues.
Occasionally Yates makes a statement that is hard to back up. Some of this, as is often the case with academics dipping a toe into popular science, was on historical matters - we are told 'It was will into the Middle Ages before the spherical view of the world became the predominant theory.' This just isn't true. I think he is also wrong about the millennium bug, calling it a self-defeating impact from predictions. The idea is that because of all the effort that was put in, there were few big problems, so people thought it was overhyped. I was consulting for the IT department of a global company at the time, and the reality was far more nuanced - the analysis was that it genuinely was overhyped, in that far too much was spent on checking non-critical systems that can have failed relatively painlessly, where a more effective approach would have been only to check mission- or safety-critical systems and leave the rest to fail and be fixed if necessary.
On other occasions, Yates provides a lack of explanation. For example, he introduces Benford's law, without telling us why it occurs. Some of the material was a little dull - I was particularly disappointed with the chapter on game theory, which failed to capture the intriguing nature of the subject and didn't explain enough for the reader to get their head around what was going on. Bearing in mind a lot of the book is based on psychological research, I was really surprised there was no mention of the replication crisis (surely in itself demonstrating a glaring lack of ability to predict the future) - I would be surprised if some of the studies he cites haven't failed to be capable of reproduction, or weren't based on far too small a sample to be meaningful. At the very least, this should be discussed in a book based on such studies.
The linearity bias isn't the only one that Yates covers, though most of the ones mentioned tie into it. As is always the case with books like this, it proved very interesting to read about, but I very rapidly forgot what all the biases are (again), and found it difficult to think of practical applications of what I've read. It's fine if you are a business or government wanting to deal with uncertainty (though even there, the book isn't a practical guide), but I think it's very unlikely to make much difference to the way we go about making predictions about the future in our everyday lives, beyond 'don't bother'.
Overall, this is an interesting topic and Yates presents a novel approach and does a good job of getting the reader to appreciate the dangers of relying on linearity. The book does have a few issues, but is still well worth a read.
March 11, 2024
Kit Yates' book - How to Expect the Unexpected - describes the many ways in which humans can misinterpret the world around them. Early chapters cover cognitive effects that lead us to view mystics as having real powers; later chapters look at why we are so bad at spotting exponential growth or assigning causality to observations where there is none. It is an enjoyable read in what could be called the 'easy academia' genre: books for not only budding amateurs or rusty-degree holders bolster eager to reinforce their knowledge but also suitably-engaged newcomers. The book doesn't tread much new ground and can read more like a 'watch out this if you don't want to be misled' list than a coherently explored thesis. But it is well worth a read for anyone interested in improving their reasoning and avoiding the myriad pitfalls we all fall into when interpreting the deluge of facts and information we are caught up in on a daily basis.
The first thought I had when beginning this book was that it is a (slightly) more mathematical variation on Daniel Kahneman's tome Thinking Fast and Slow. That book explored the "heuristics [that] are essential to getting anything done but lead us into avoidable bias; how we make choices, often with far too much confidence and far too little regard for luck" before closing on a more philosophical look at the good life as lensed through Kahneman's two-tier system of thought (see my review here). The author attempted to build a psychological system of fast/slow thinking to help us understand our biases, which naturally led into discussions of Bayes' theorem and other elements of statistical reasoning that do not come naturally but must be invoked to avoid the errors of our quicker thinking sides.
To give the classic example, ask almost anyone whether Steve the quiet, shy, 5ft nothing, list-loving introvert is more likely to be a farmer or librarian and most will say the former. This totally ignores the background frequency of librarians (many times lower than farmers) and, at the same time through the representativeness heuristic, assumes all farmers fit a certain mould. It is classic spurious reasoning and - mutatis mutandis - can be seen in all sorts of bogus reasoning across media and politics. In Kahneman, this curiosity is introduced from the perspective of thinking fast and slow, pulled apart via Bayes' theorem and then the overall edifice is drawn together to make wider claims about human thought.
Is it all entirely convincing? Maybe not. But Thinking Fast and Slow at least said something beyond the interesting quirks it described. It is much less clear what precisely Yates is aiming for or what conclusion his book ultimately draws. It is both an interesting, informative discussion of the many ways in which humans can miss their own biases or failures of statistical reasoning to draw totally wrong conclusions; but it is at the same time also at risk of becoming a glossary of biases and counterintuitive curios, without a clear guiding thread to pull the reader through.
Which is not to say it is not worth reading - hence the four stars. The examples are interesting and, in the main, more easy to follow than in Kahneman. To take just two examples from the chapter on how cold readers utilise our natural biases to make it appear they have supernatural powers, Yates describes the Forer Effect. This is where the recipient of a vague personality assessment interprets it as if it were extremely personal and unique. This can be attained by stringing together Barnum Statements that are designed to sound specific but apply to almost anyone. The overall Forer concept neatly captures the allure of star sign descriptions.
One my favourite such techniques that Yates introduces is the wonderfully named 'punctuated rainbow ruse'. A rainbow ruse is offering two contradictory statements and letting the person select the correct one, so making the cold reader appear extraordinarily prescient. For instance, you could refer to someone as naturally extroverted but at times wary and reserved. A listener will often agree with some aspect of this - or see both sides of themselves in this all-encompassing statement. A punctuated rainbow ruse involves more confidently saying the first half - 'you are outgoing' - then slightly pausing for clues. The reader then either delivers the second half of the ruse to save face if the person clearly does not identify as outgoing. Or, if the sitter immediately agrees with the first half, the cold reader will have scored an impressive 'direct hit' in their reading, rather than relying on less impressive Barnum statements exclusively.
The book is replete with these sorts of examples and it makes for entertaining reading. It can, however, get a bit 'death by jargon' at times, with the reader being a bit drowned in terminology. I am a categorical thinker so enjoyed this but even I found it tricky to keep afloat amidst the Pollyanna Effects, apophenia, p-hacking and flipism. Without a clear guiding thread, the book risks becoming a glossary rather than a popular science book making a clear point. The 'Dodging Snowballs' chapter suffers from this, for example. It begins with the notion of positive / negative feedback before moving to people whose names sound like their jobs (aptronyms), placebo / nocebo effect, the experimenter effect, mass hysteria and fake news. There are links - but I found several times I was struggling to see how chapters had ended up where they did.
Which is not to say the author has not been smart with the structure of the book in other ways even if it 'tedious links' around a bit. Yates was particularly smart to begin with the cold-reading chapter. This, I believe, could ease the less mathematically inclined into the book before chapter 2 hits with the more statistical thinking. The author does not delve into the mathematics but does introduce the reader to important concepts to guard against mistaken thinking, such as the 'multiple comparisons fallacy' in which if you make enough statistical comparisons at a a given confidence level you will eventually spot a 'statistically significant result'; or the related confirmation bias in which stories are only read or reported on in the media that align with a prevailing view.
Probably more than anything else, such results should be taught in schools to stop all sorts of bogus claims gaining common currency. It 'explains', for example, in the context of our global news system, why we see shockingly unlikely events happening surprisingly regularly. From the individual who has survived three plane crashes to omniscient animals predicting successive football matches in a row. There are millions of airline passengers a year and some plane crashes; by the law of large numbers someone will have survived a surely-meaningful number of crashes. While basic statistics means that if enough animals make predictions on matches, one will get them right and they will be the ones we hear about.
I've verbosely spelled this and other examples out because of how vital they are and because they are the book's greatest strength. To the best of my knowledge, our education system is not teaching children about the cognitive shortcuts that lead us into error, or the features of large numbers or linear reasoning that we need to understand to draw inferences better. If digested, this book offers a necessary counter to this lack of training at school which translates to a problem writ large on the country. It is probably less of a coherent work than Kanhneman's tome, but to its credit it is much shorter, avoids the philosophical machinations that bog down that previous work and is much easier to read.
Perhaps the book's biggest challenge - which is not Yates' to fix - is that it won't be read by many people who need to read it. It will appeal to, and be easily read by, people like me. People with a grounding in maths who probably already think in a similar manner to Yates and who are fairly analytical and critical. Or those who understand the value such things. It will not be read by those prone to believing mediums or susceptible to conspiracy theories, whose education did not guard them against specious logic and cognitive shortcuts to intuitive but wrong answers. I haven't marked the book down for this but, as the book more and more discussed hysteria and anti-vax sentiment, it was hard not to conclude that this was a valuable book that could not drive the change it propounds.
The first thought I had when beginning this book was that it is a (slightly) more mathematical variation on Daniel Kahneman's tome Thinking Fast and Slow. That book explored the "heuristics [that] are essential to getting anything done but lead us into avoidable bias; how we make choices, often with far too much confidence and far too little regard for luck" before closing on a more philosophical look at the good life as lensed through Kahneman's two-tier system of thought (see my review here). The author attempted to build a psychological system of fast/slow thinking to help us understand our biases, which naturally led into discussions of Bayes' theorem and other elements of statistical reasoning that do not come naturally but must be invoked to avoid the errors of our quicker thinking sides.
To give the classic example, ask almost anyone whether Steve the quiet, shy, 5ft nothing, list-loving introvert is more likely to be a farmer or librarian and most will say the former. This totally ignores the background frequency of librarians (many times lower than farmers) and, at the same time through the representativeness heuristic, assumes all farmers fit a certain mould. It is classic spurious reasoning and - mutatis mutandis - can be seen in all sorts of bogus reasoning across media and politics. In Kahneman, this curiosity is introduced from the perspective of thinking fast and slow, pulled apart via Bayes' theorem and then the overall edifice is drawn together to make wider claims about human thought.
Is it all entirely convincing? Maybe not. But Thinking Fast and Slow at least said something beyond the interesting quirks it described. It is much less clear what precisely Yates is aiming for or what conclusion his book ultimately draws. It is both an interesting, informative discussion of the many ways in which humans can miss their own biases or failures of statistical reasoning to draw totally wrong conclusions; but it is at the same time also at risk of becoming a glossary of biases and counterintuitive curios, without a clear guiding thread to pull the reader through.
Which is not to say it is not worth reading - hence the four stars. The examples are interesting and, in the main, more easy to follow than in Kahneman. To take just two examples from the chapter on how cold readers utilise our natural biases to make it appear they have supernatural powers, Yates describes the Forer Effect. This is where the recipient of a vague personality assessment interprets it as if it were extremely personal and unique. This can be attained by stringing together Barnum Statements that are designed to sound specific but apply to almost anyone. The overall Forer concept neatly captures the allure of star sign descriptions.
One my favourite such techniques that Yates introduces is the wonderfully named 'punctuated rainbow ruse'. A rainbow ruse is offering two contradictory statements and letting the person select the correct one, so making the cold reader appear extraordinarily prescient. For instance, you could refer to someone as naturally extroverted but at times wary and reserved. A listener will often agree with some aspect of this - or see both sides of themselves in this all-encompassing statement. A punctuated rainbow ruse involves more confidently saying the first half - 'you are outgoing' - then slightly pausing for clues. The reader then either delivers the second half of the ruse to save face if the person clearly does not identify as outgoing. Or, if the sitter immediately agrees with the first half, the cold reader will have scored an impressive 'direct hit' in their reading, rather than relying on less impressive Barnum statements exclusively.
The book is replete with these sorts of examples and it makes for entertaining reading. It can, however, get a bit 'death by jargon' at times, with the reader being a bit drowned in terminology. I am a categorical thinker so enjoyed this but even I found it tricky to keep afloat amidst the Pollyanna Effects, apophenia, p-hacking and flipism. Without a clear guiding thread, the book risks becoming a glossary rather than a popular science book making a clear point. The 'Dodging Snowballs' chapter suffers from this, for example. It begins with the notion of positive / negative feedback before moving to people whose names sound like their jobs (aptronyms), placebo / nocebo effect, the experimenter effect, mass hysteria and fake news. There are links - but I found several times I was struggling to see how chapters had ended up where they did.
Which is not to say the author has not been smart with the structure of the book in other ways even if it 'tedious links' around a bit. Yates was particularly smart to begin with the cold-reading chapter. This, I believe, could ease the less mathematically inclined into the book before chapter 2 hits with the more statistical thinking. The author does not delve into the mathematics but does introduce the reader to important concepts to guard against mistaken thinking, such as the 'multiple comparisons fallacy' in which if you make enough statistical comparisons at a a given confidence level you will eventually spot a 'statistically significant result'; or the related confirmation bias in which stories are only read or reported on in the media that align with a prevailing view.
Probably more than anything else, such results should be taught in schools to stop all sorts of bogus claims gaining common currency. It 'explains', for example, in the context of our global news system, why we see shockingly unlikely events happening surprisingly regularly. From the individual who has survived three plane crashes to omniscient animals predicting successive football matches in a row. There are millions of airline passengers a year and some plane crashes; by the law of large numbers someone will have survived a surely-meaningful number of crashes. While basic statistics means that if enough animals make predictions on matches, one will get them right and they will be the ones we hear about.
I've verbosely spelled this and other examples out because of how vital they are and because they are the book's greatest strength. To the best of my knowledge, our education system is not teaching children about the cognitive shortcuts that lead us into error, or the features of large numbers or linear reasoning that we need to understand to draw inferences better. If digested, this book offers a necessary counter to this lack of training at school which translates to a problem writ large on the country. It is probably less of a coherent work than Kanhneman's tome, but to its credit it is much shorter, avoids the philosophical machinations that bog down that previous work and is much easier to read.
Perhaps the book's biggest challenge - which is not Yates' to fix - is that it won't be read by many people who need to read it. It will appeal to, and be easily read by, people like me. People with a grounding in maths who probably already think in a similar manner to Yates and who are fairly analytical and critical. Or those who understand the value such things. It will not be read by those prone to believing mediums or susceptible to conspiracy theories, whose education did not guard them against specious logic and cognitive shortcuts to intuitive but wrong answers. I haven't marked the book down for this but, as the book more and more discussed hysteria and anti-vax sentiment, it was hard not to conclude that this was a valuable book that could not drive the change it propounds.
July 12, 2023
This was an excellent read (listen on Audible) and I’d highly recommend to anyone with an interest in applying logic and mathematics to modelling and prediction. Very accessible for non experts but with enough depth and interesting real world examples to keep those with more knowledge and experience interested. Have read a few similar books to this one but felt I took more away from this one than any other, likely owing to the authors direct experience in the field.
May 10, 2024
An interesting book on how cognitive biases and traps often condition or trick us into making false predictions, with the goal of empowering us with knowledge of these common traps so we hopefully don't fall into them. Yates covers a lot of diverse topics here (how we can be manipulated by mediums, psychics and World-Cup-predicting-octopi; why experiments which start without a hypothesis and measure dozens of variables hoping to validate a post hoc hypothesis from the swarm of generated data are misguided; how presuming that past events predict future performance on a linear basis can be wrong) so it's hard to classify the book into one cohesive topic or recommend it as a definitive volume on any one of these topics. However, I would recommend this book to someone who's looking for a general primer any of these topics as a way to get started.
Further reading:
Rigor Mortis: How Sloppy Science Creates Worthless Cures, Crushes Hope, and Wastes Billions by Richard Harris - more in-depth discussion on statistical analysis and how correlation doesn't equal causation
The Confidence Game: Why We Fall for It . . . Every Time by Maria Konnikova - about con artists
Predictably Irrational: The Hidden Forces That Shape Our Decisions by Dan Ariely - more on cognitive biases
Further reading:
Rigor Mortis: How Sloppy Science Creates Worthless Cures, Crushes Hope, and Wastes Billions by Richard Harris - more in-depth discussion on statistical analysis and how correlation doesn't equal causation
The Confidence Game: Why We Fall for It . . . Every Time by Maria Konnikova - about con artists
Predictably Irrational: The Hidden Forces That Shape Our Decisions by Dan Ariely - more on cognitive biases
December 14, 2023
I enjoyed the Game Theory chapter, but I can't say I came away from this book with any new information. It was at best a mathematical proof(-ish) of things that are widely known or common sense. Some of the tidbits were interesting, and I appreciate all the research that went into the book. The problem is that there was so much boring detail that anyone who would benefit from this knowledge would put the book down before completion.
June 12, 2025
This book is premised on various established notions in mathematical universe such as Power Law and Benford's Law that I read before from Alex Bellos' book: Alex Through the Looking - Glass and The Law of Large Number, Randomness, Probability and Chaos Theory I read from 2 Math professors and popular authors: Ian Stewart and Steven Strogatz. The author focuses on discussing these math notions under the context of our contemporary everyday life and their intellectual benefits to us all in what we do. The name of the book is therefore a bit misleading. It should have been Making Smart Predictions or some such but it may be a bland name.
This book can help build practical appreciation for readers in recognizing that some (seriously and really!) down-to-earth math notions are essential to us because they are practically useful in helping us understand some aspects of our modern life and environment and how to smartly navigate them with proper thinking that helps prevent us from troubles and helps us make prudent decisions of our own. All math ideas discussed here are therefore precious nuggets of beneficial knowledge for modern living.
This book is a praiseworthy addition to the author's first book, both of which do the humanity a great learning favor. Readers (especially young ones) who have not read his first book should not miss it: The Math of Life and Death: 7 Mathematical Principles That Shape Our Lives. The title may sound a bit scary but the contents are enlighteningly intriguing and useful, especially for readers who never read books from Alex Bellos, Ian Stewart and Steven Strogatz before.
This book can help build practical appreciation for readers in recognizing that some (seriously and really!) down-to-earth math notions are essential to us because they are practically useful in helping us understand some aspects of our modern life and environment and how to smartly navigate them with proper thinking that helps prevent us from troubles and helps us make prudent decisions of our own. All math ideas discussed here are therefore precious nuggets of beneficial knowledge for modern living.
This book is a praiseworthy addition to the author's first book, both of which do the humanity a great learning favor. Readers (especially young ones) who have not read his first book should not miss it: The Math of Life and Death: 7 Mathematical Principles That Shape Our Lives. The title may sound a bit scary but the contents are enlighteningly intriguing and useful, especially for readers who never read books from Alex Bellos, Ian Stewart and Steven Strogatz before.
May 5, 2025
This book is so bizarre and amusing to me. I am not lying that I just marvel how backwards the whole situation is. The author is very good at identifying various tools/things/patterns/phenomena which skew our perception about the world and things that influence our behavior and perception, BUT YET for the whole book, the author hones in how covd vax is "safe and effective" (multiple times) and how forcing people to stay at home [saved countless lives].
It is just fascinating that the author, while being well educated and versed in failures of our thinking and perception, still is fully brainwashed to the most stupid hoax perpetuated by pcr (not a real test of anything other than conductivity), moronic pseudo-scientists and profit seeking companies and individuals.
Like literally, outlining multiple laws, that skew reporting, data gathering and identifying bad incentives (like Goodheart's law and etc.) still cannot comprehend in his brain, that all data gathered during the pandemic (especially using pcr pseudo conductivity test) is just a pile of crap. Thus all the "measures" were just completely stupid.
I am truly amazed.
It is just fascinating that the author, while being well educated and versed in failures of our thinking and perception, still is fully brainwashed to the most stupid hoax perpetuated by pcr (not a real test of anything other than conductivity), moronic pseudo-scientists and profit seeking companies and individuals.
Like literally, outlining multiple laws, that skew reporting, data gathering and identifying bad incentives (like Goodheart's law and etc.) still cannot comprehend in his brain, that all data gathered during the pandemic (especially using pcr pseudo conductivity test) is just a pile of crap. Thus all the "measures" were just completely stupid.
I am truly amazed.
May 18, 2025
75%
75, it's not really about predicting the unpredictable here, but about you saying mathematical probability to predict advance. So this whole thing about predicting the unpredictable. I mean, If we're gonna throw out a bunch of weird things like and then just go back and delete them from our logs so that we have perfect prediction because that's what you It's going to do. That's funny. It's funny idea. I should do that for psychological reasoning, uh, then? Yeah, you can definitely unexpect the unexpectedly. Expect the unexpected.
Otherwise, it really wasn't that deep, um, talking about places demon? Yeah, but then they, we say, all the same. You can't simultaneously know the location of an object and the velocity that it's traveling yet. Like, yeah, you can. It's just Advanced physics modeling. Yeah, we get closer all the time.
75, it's not really about predicting the unpredictable here, but about you saying mathematical probability to predict advance. So this whole thing about predicting the unpredictable. I mean, If we're gonna throw out a bunch of weird things like and then just go back and delete them from our logs so that we have perfect prediction because that's what you It's going to do. That's funny. It's funny idea. I should do that for psychological reasoning, uh, then? Yeah, you can definitely unexpect the unexpectedly. Expect the unexpected.
Otherwise, it really wasn't that deep, um, talking about places demon? Yeah, but then they, we say, all the same. You can't simultaneously know the location of an object and the velocity that it's traveling yet. Like, yeah, you can. It's just Advanced physics modeling. Yeah, we get closer all the time.
January 13, 2024
The writing is excellent in that it moves along nicely and is sort of a broad story telling survey of a wide variety of different ways to think about ... thinking. It is however basically a survey, so nothing is covered in detail, instead it introduces a few biases and some different types of math and statistical thinking which are useful, but any of which would require much more study to really apply.
If you aren't familiar with the space, it is a really good way to start thinking about this type of problem. If you've already spent here, you may get a couple of cute stories to help future communications around these topics, but you probably won't learn any new skills.
If you aren't familiar with the space, it is a really good way to start thinking about this type of problem. If you've already spent here, you may get a couple of cute stories to help future communications around these topics, but you probably won't learn any new skills.
January 15, 2024
I enjoyed this book. The author covers a wide range of “forecasting” from the techniques of fortune tellers to those of modern day weather professionals. While I found the entire book worth my time, much of the material has been covered by others. The “gem” chapter, to me, is the final one where chaos mathematics is introduced. The coverage of how even minute changes in the initial state of a complex model can result in significantly different predictions was fascinating and I believe the author’s biggest contribution.
October 26, 2024
Some of the math seems wrong.
P. 96: "imagine drawing cards from a deck of fifty-two at random and just putting them back. How many times do you think you would need to repeat this before the chance of having drawn the same card twice becomes more than 50%. The answer is just nine".
Please someone explain this to me so I understand what I am not getting and can continue reading, and remove this bad rating.
This is how chatgpt looks at it and it makes much more sense.
1-(51/52) power of n > 0.5
P. 96: "imagine drawing cards from a deck of fifty-two at random and just putting them back. How many times do you think you would need to repeat this before the chance of having drawn the same card twice becomes more than 50%. The answer is just nine".
Please someone explain this to me so I understand what I am not getting and can continue reading, and remove this bad rating.
This is how chatgpt looks at it and it makes much more sense.
1-(51/52) power of n > 0.5
November 30, 2024
The title of "How to Expect the Unexpected" does a great job of drawing readers in, but unfortunately, the content fails to deliver. The book doesn’t offer any new or practical ways to handle the unexpected and, instead, is filled with complex mathematical dilemmas that often left me lost. Expectations are a common issue that deserve better treatment if the author truly intended to provide helpful insights. Ironically, the book ends up proving its title right—I didn’t expect to feel so frustrated and nauseated while waiting for tangible solutions. Unexpected, indeed!
January 2, 2024
I like these kind of books; they explain what is really a tricky kind of thing in a way that feels chatty and comprehensible.
I always tell my kids compound interest is magic, but sometimes people forget that it applies to debts as well.
He jokes that he did not expect the global pandemic that delayed the publication by two years as he home schooled his kids… lol
4 stars
So far this year, my library saved me A$4192.08
I always tell my kids compound interest is magic, but sometimes people forget that it applies to debts as well.
He jokes that he did not expect the global pandemic that delayed the publication by two years as he home schooled his kids… lol
4 stars
So far this year, my library saved me A$4192.08
February 16, 2024
The take-home message is simple in a baysian way, be willing to challenge your biases as you collect new information. If you are only looking for the answer to the titular question, then stop here. However, the other theme: that the small details and the process in which you collect and interpret data can drastically change the results, is full of gold nuggets worth weighing and considering.
November 26, 2024
Could not finish the book. I kept waiting for the actual guidance and there was a lot of discussion of probabilities and the mathematics behind probability. I love math, don’t get me wrong. This was just a boring application of it. I’m sure there are other books that are more to the point. If anything, the title was misleading.
December 15, 2023
Honestly I find the book is too lengthy for simple concept, but it shows how the level of detail the author would like to present for the reader.
Not bad overall but it is just not suitable for me :)
Not bad overall but it is just not suitable for me :)
January 10, 2024
I really enjoyed listening to the audiobook on Audible narrated by Kit himself. I thought I knew a lot about probability and statistics, but this text opened my eyes to some interesting new concepts such as Bayes theorem and Benfords law. It also has a good section on game theory and Chaos theory. I'm very tempted to check out his other books now. Recommended!
July 31, 2023
Excellent
November 16, 2023
I learned more practical applications for probability methods and real world examples that made this book worth reading.
January 11, 2024
A fascinating book that gives the reader an insight into the science behind predictions.
January 18, 2025
Very mediocre hoped to learn a bit more, second half wasn’t great.
June 6, 2025
A bit unfocused and pop-sciencey, but had some interesting parts and overall I enjoyed it.
November 12, 2023
It's really good but I've read a few similar books so found it hard to enjoy it as there was little new over what other popular books about how to use maths and logic cover.
October 7, 2023
This book is Not Entirely Worthless... still it comes dangerously close. The author is definitely familiar with logic, probability and statistics and the Many Well-Known Stories about holes in logic and lack of familiarity with Probability Statistics and non-linear growth and relationship (e.g. reciprocal) which affect peoples judgements. But the book is called, How to Expect the Unexpected... It doesn't address how to do this... except laud modern maths (sic) models. Instead it tells us mostly we Can't Expect the Unexpected (which can be said in one sentence). Also, a science book should Never mention Politics and confuse Science with Policy But this book does throughout. Why? Likely because it wanted to be a NYT Best Seller. So the Author is Just Another Sellout. Kit Yates, I looked up your PhD Thesis because I seriously wondered after the mid-point if you'd ever really had an original thought. I don't know statistical biology well-enough to tell, if you do! That's a bad come away from one of your well-proofread books. I won't be back to read your other book(s).
Displaying 1 - 26 of 26 reviews