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3.84 AVERAGE
3.84 AVERAGE
This book is a well-written, common sense account of probability for the layperson. I found it entertaining and it reminded me of past statistics courses - things I had forgotten I had even learned. However, it's not really what I was expecting. I expected more focus on how we misjudge probability in our every day lives, but that discussion felt ancillary to the discussion of the history of statistics in the book. I don't want to make it sound boring, because it wasn't, and the last third of the book was much more in the realm of what I was expecting. I really enjoyed reading about the fund manager stuff in particular. It confirmed my suspicion that most of the financial professionals don't really perform any better than if they were guessing at random. This is just the same as "futurists" or any sort of job that involves prediction. I would have loved it if he would have expanded this discussion into other professions (like futurists) because that would have been very interesting. All in all though, a good read, and I thought that the metaphor of the drunkard's walk was great...I just wished it had been described earlier in the book, but I guess he was building up to it!
إن الكثير مما يحدث لنا - النجاح في وظائفنا ، واستثماراتنا ، وقراراتنا الحياتية ، الرئيسية والثانوية - هو نتيجة عوامل عشوائية بقدر ما هو نتيجة للمهارة والاستعداد والعمل الجاد. لذا فإن الحقيقة التي ندركها ليست انعكاسًا مباشرًا للأشخاص أو الظروف التي تكمن وراءها ، ولكنها بدلاً من ذلك صورة مشوشة بسبب التأثيرات العشوائية لقوى خارجية غير متوقعة أو متقلبة. هذا لا يعني أن الكفاءة ليست مهمة - إنها أحد العوامل التي تزيد من فرص النجاح - ولكن العلاقة بين الإجراءات والنتائج ليست مباشرة كما نود أن نصدق. وبالتالي ، ليس من السهل فهم ماضينا ، ولا يسهل التنبؤ بمستقبلنا ، وفي كلا الحالتين نستفيد من النظر إلى ما وراء التفسيرات السطحية.
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Leonard Mlodinow
The Drunkard's Walk
Translated By #Maher_Razouk
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Leonard Mlodinow
The Drunkard's Walk
Translated By #Maher_Razouk
I was surprised by how maths-y this book was (I shouldn't have been, really). And then, I was surprised by how much I was enjoying it despite that. Some amusing writing, relatively easy to follow, but definitely needs your full attention. I wouldn't be surprised if a lot of people picked this up and put it straight back down again, because if you didn't really enjoy high school maths, this probably won't appeal.
I feel like I've read this book before... but relative to others with similar topics, this one just doesn't compare.
Really enjoyed this book. It's a great history lesson for all those in epidemiology, biostatistics, statistics, and anyone that is struggling to understand why things happen the way they do. Only con of the book was the author's random inane jokes...
funny
informative
inspiring
fast-paced
informative
reflective
medium-paced
3.5
This book has probability theory, statistics, examples of how they're applied and lots of calculations. I liked that I got to know a bit more about the history of how these concepts that I studied in school came to me, I was not so interested in reading about how to do those calculations. I'm also not interested in betting/gambling or sports and the examples in this book were a lot about those. I would've liked to read much more about real-world applications outside of these. I did appreciate the extensive references and the presence of an index. My favorite part was the conclusion that gives a hopeful outlook when you look at how much chance affects our lives and how much of our lives are just under the illusion of our control.
This book has probability theory, statistics, examples of how they're applied and lots of calculations. I liked that I got to know a bit more about the history of how these concepts that I studied in school came to me, I was not so interested in reading about how to do those calculations. I'm also not interested in betting/gambling or sports and the examples in this book were a lot about those. I would've liked to read much more about real-world applications outside of these. I did appreciate the extensive references and the presence of an index. My favorite part was the conclusion that gives a hopeful outlook when you look at how much chance affects our lives and how much of our lives are just under the illusion of our control.
This one is a book about probability and how it affects our everyday lives and not just the lives of imaginary textbook people. It does a pretty good job, too. The Monty Hall problem shows up, as does a question about offspring: If you know a woman has two children and one of them is a girl, what is the likelihood that both of her children are girls?
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A great quick read on how subtle but important math can be in our daily lives, and how hard randomness is to properly assess.
I think my favorite topic in the book was the 1968 conviction of Malcolm Collins, based on probabilities that a pair of suspects would fit all attributes given as less than one in a million. The probability was flawed, because the math professor that the prosecutors used ignored that separate events have to be independent to calculate probabilities the way he did, but more importantly, even assuming the calculation was correct, in a metro area of ~7 million, a one in a million chance means there's only about a 1 in 7 chance the guy you picked up is the one who committed the crime.
My second favorite is that the author himself got a positive result on a medical test that has a false positive rate of 1 in 1,000, so his doctor told him there was a 999 in 1,000 chance he would die soon. The doctor didn't understand that because the author was in a low-risk group, and because the low-risk group was so much larger than the high-risk group, that a given positive result excluding any other information amounted to about a 1-in-11 chance the author actually had the disease (and, yay, the author didn't have the disease).
Learn Bayes' Theorem, people. It sounds so simple as to be too obvious to bother retaining, but people get it wrong all the time.
I think my favorite topic in the book was the 1968 conviction of Malcolm Collins, based on probabilities that a pair of suspects would fit all attributes given as less than one in a million. The probability was flawed, because the math professor that the prosecutors used ignored that separate events have to be independent to calculate probabilities the way he did, but more importantly, even assuming the calculation was correct, in a metro area of ~7 million, a one in a million chance means there's only about a 1 in 7 chance the guy you picked up is the one who committed the crime.
My second favorite is that the author himself got a positive result on a medical test that has a false positive rate of 1 in 1,000, so his doctor told him there was a 999 in 1,000 chance he would die soon. The doctor didn't understand that because the author was in a low-risk group, and because the low-risk group was so much larger than the high-risk group, that a given positive result excluding any other information amounted to about a 1-in-11 chance the author actually had the disease (and, yay, the author didn't have the disease).
Learn Bayes' Theorem, people. It sounds so simple as to be too obvious to bother retaining, but people get it wrong all the time.