695 of 724 people found the following review helpful
Much-needed insight to understand and improve predictive science,
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This review is from: The Signal and the Noise: Why So Many Predictions Fail - But Some Don't (Hardcover)
This is the best general-readership book on applied statistics that I've read. Short review: if you're interested in science, economics, or prediction: read it. It's full of interesting cases, builds intuition, and is a readable example of Bayesian thinking.
Longer review: I'm an applied business researcher and that means my job is to deliver quality forecasts: to make them, persuade people of them, and live by the results they bring. Silver's new book offers a wealth of insight for many different audiences. It will help you to develop intuition for the kinds of predictions that are possible, that are not so possible, where they may go wrong, and how to avoid some common pitfalls.
The core concept is this: prediction is a vital part of science, of business, of politics, of pretty much everything we do. But we're not very good at it, and fall prey to cognitive biases and other systemic problems such as information overload that make things worse. However, we are simultaneously learning more about how such things occur and that knowledge can be used to make predictions better -- and to improve our models in science, politics, business, medicine, and so many other areas.
The book presents real-world experience and critical reflection on what happens to research in social contexts. Data-driven models with inadequate theory can lead to terrible inferences. For example, on p. 162: "What happens in systems with noisy data and underdeveloped theory - like earthquake prediction and parts of economic and political science - is a two-step process. First, people start to mistake the noise for a signal. Second, this noise pollutes journals, blogs, and news accounts with false alarms, undermining good science and setting back our ability to understand how the system really works." This is the kind of insight that every good practitioner acquires through hard-won battles, and continues to wrestle every day both in doing work and in communicating it to others.
It is both readable and technically accurate: it presents just enough model details yet avoids being formula-heavy. Statisticians will be able to reproduce models similar to the ones he discusses, but general readers will not be left out: the material is clear and applicable. Scholars of all stripes will appreciate the copious notes and citations, 56 pages of notes and another 20 pages of index, which detail the many sources. It is also important to note that this is perhaps the best general readership book from a Bayesian perspective -- a viewpoint that is overdue for readable exposition.
The models cover a diversity of areas from baseball to politics, from earthquakes to finance, from climate science to chess. Of course this makes the book fascinating to generalists, geeks, and breadth thinkers, but perhaps more importantly, I think it serves well to develop reusable intuition across domains. And, for those of us who practice such things professionally, to bring stories and examples that we can tell and use to illustrate concepts with the people we inform.
There are three audiences who might not appreciate the book as much. First are students looking for a how-to book. Silver provides a lot of pointers and examples, but does not get into nuts and bolts details or supply foundational technical instruction. That requires coursework in research methods and and statistics. Second, his approach to doing multiple models and interpreting them humbly will not satisfy those who promote a naive, gee-whiz, "look how great these new methods are" approach to research. But then, that's not a problem; it's a good thing. The third non-fitting audience will be experts who desire depth in one of the book's many topic areas; it's not a technical treatise for them and I can confidently predict grumbling in some quarters. Overall, those three audiences are small, which happily leaves the rest of us to enjoy the book.
What would make it better? As a pro, I'd like a little more depth (of course). It emphasizes games a little too much for my taste. And a clearer prescriptive framework could be nice (but also could be a problem for reasons he illustrates). But those are minor points; it hits its target better than any other such book I know.
Conclusion: if you're interested in scientific or statistical forecasting, either as a professional or layperson, or if you simply enjoy general science books, get it. Cheers!
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Showing 1-10 of 17 posts in this discussion
Initial post: Sep 27, 2012 7:52:46 AM PDT
Ed Morgan says:
I'm a big fan of Nate's 538 -- I'm "literally" obsessed with it during this pre-election time -- and really look forward to this. Thanks for your clear review and for bringing your expertise.
In reply to an earlier post on Sep 27, 2012 7:59:17 AM PDT
Thank you for the note! I also enjoy 538 greatly, and the approach in this book is very much the same (as one would expect): to use multiple imperfect models and aggregate them while being keenly aware of uncertainty. Best!
Posted on Sep 27, 2012 7:59:55 AM PDT
Last edited by the author on Dec 8, 2012 6:38:07 AM PST
A. D. Thibeault says:
Great review. A full executive summary of the book, and a podcast discussion of Silver's treatment of Bayes theorem is available at newbooksinbrief.com.
Posted on Sep 29, 2012 6:19:45 AM PDT
R. B. Meek says:
Excellent review. As another huge fan of 538, this book has been on my "get list" since I first caught wind of it. Thank you for affirming what I already suspected. Mr. Silver can write a book as well as he writes a blog. I have just begun the book and it already promises to be all you say. Thank you.
In reply to an earlier post on Sep 29, 2012 8:44:18 AM PDT
Thank you for the note -- I'm glad to hear you're enjoying it. This also reminds me that I should review some of the other stats (pro and popular) that I've read lately. Best wishes.
Posted on Sep 29, 2012 11:11:03 AM PDT
[Deleted by the author on Oct 4, 2012 9:09:05 AM PDT]
Posted on Oct 3, 2012 8:06:16 AM PDT
Paul Frandano says:
Useful review, Sitting. That said, it might have been several percentage points more helpful if you had cited a few examples from the "any other such book" list you've read. You left that an empty set, which naturally begs the question.
In reply to an earlier post on Oct 3, 2012 9:41:21 AM PDT
Thanks for the note, and actually I'd agree although I think we may be reading with different purposes. My evaluation of "accuracy" is limited to only his discussion of how he presents the models that he presents -- not whether they're really the right models. There are a lot of places where I would use different models, different inputs, or have different interpretations, but the key is that his general approach has the right philosophy IMHO. Cheers!
In reply to an earlier post on Oct 3, 2012 9:46:16 AM PDT
Paul: LOL, I agree. I started to include some other books but the review was getting too long already. I'll try to post more of them later, but meanwhile some of the books I had in mind as "stats for general readers" were Salsburg's The Lady Tasting Tea, and McGrayne's The Theory That Would Not Die, and going father back, books like Gleick's Chaos (yes, I know, not really stats, but general science with minimal technical content). That's the comparative genre, I think. All of those are interesting but not really as useful for understanding the broad technical principles as Silver's.
In reply to an earlier post on Oct 3, 2012 10:57:52 AM PDT
Paul Frandano says:
Thanks, Sitting, for a quick, considerate, and good-natured reply or two. I know that Salsburg and the Gleick, but not the McGrayne, and I'm a Baseball Prospectus kinda guy, as well as a fellow with a genuine stake in understanding "Our Statistical World (and the Bets We Unconsciously Make, Every Single Day, as We Play Those Everyday Odds)" - a book I wish I had the power to write - so I do believe I'll be picking up the Silver book.