Psychology and Biology nerd. Chemistry enthusiast. Fan of good research-based science books, comedies and crime.
Nate gives a great view of how big data can and should work (or not). I particularly liked that while some case studies had clear central messages, he avoided reductionism and reapplied lessons from other chapters.
This is a must read for any one interested in being correct about the world we live in.
The way the book ties together so many different threads with a single consistent hypothesis is praiseworthy
I now understand part of the reason I kept seeing Bayes' Theorem referenced everywhere when this book was released.
I like how the focus is on confronting priors. not eliminating them, but seeing the effect they have on your view. I confess I will be doing some basic calculations in the coming days.
this does make the second book read by Chamberland that basically slams you with data. much more approachable than Gig Calories, Bad Calories.
Not for everyone and some of the chapters were a bit long, but still a great book dealing our ability to see into the future.
Silver accomplishes a difficult task - statistics are not only understandable they are fascinating. Silver applies his grasp of statistics to real world problems and provides insights into how to navigate financial markets, public threats such weather forecasting or predicting terrorist strikes. The prose is readily accessible and free of jargon. This is a real treat.
Silver’s ability to dissect each of the scenarios into relevant and comprehensible examples shed light into how easily we can become entrapped and misguided by information.Chamberlain's reading is very well done and conveys the book's messages powerfully.
the world is much more complicated than you think. I was surprised with the open perspective of this book, it challenged me to reevaluate some of my own preconceived ideas. I don't think the author get everything right, but his thinking is on the right track. definitely recommend this book for anyone who wants to think more critically about their predictions.
Interesting book about forecasting, statistics, and why simply having more data is not going to result in better predictions. The writing is mostly entertaining and accessible to all, but if you're interested in the details there's enough there that I was able to correctly answer 3/4 questions on a Bayesian theory test a friend coincidentally posted on Facebook while I was reading this book.
The direction seems a little scattered though, it's more like a series of case studies or vignettes without a clear and cohesive direction. The most important information in the book (in my opinion) is Bayesian theory and how we can and should use it to keep our forecasts realistic; yet it isn't mentioned till over half way into the book and then isn't consistently emphasized through till the end. The rest of the book is examples of predictions gone right or wrong and examinations why; interesting but a little disjointed seeming at times. Still, very interesting read and worth picking up.