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The Signal and the Noise: Why So Many…
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The Signal and the Noise: Why So Many Predictions Fail — but Some… (edição: 2012)

de Nate Silver

MembrosResenhasPopularidadeAvaliação médiaMenções
2,590734,270 (3.86)32
Silver built an innovative system for predicting baseball performance, predicted the 2008 election within a hair's breadth, and became a national sensation as a blogger. Drawing on his own groundbreaking work, Silver examines the world of prediction.
Membro:crypto29
Título:The Signal and the Noise: Why So Many Predictions Fail — but Some Don't
Autores:Nate Silver
Informação:Penguin Press HC, The (2012), Edition: 1, Hardcover, 544 pages
Coleções:Sua biblioteca
Avaliação:
Etiquetas:non-fiction, bayesian, economics, forecasting, mathematics, politics, probability, science, statistics

Detalhes da Obra

The Signal and the Noise: Why So Many Predictions Fail — but Some Don't de Nate Silver

Adicionado recentemente porbiblioteca privada, ffunm00, futureman, C.Pickarski, Jacquesct, vesca, LapsusCalami, KarenRennich
Bibliotecas HistóricasTim Spalding
  1. 20
    Thinking, Fast and Slow de Daniel Kahneman (BenTreat)
    BenTreat: Integrates some of the analytical techniques Silver describes with common irrational patterns of decision-making; Kahneman's book explains how to use some of Silver's techniques (and other tools) to avoid making decisions which are not in one's own best interest.… (mais)
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» Veja também 32 menções

Mostrando 1-5 de 73 (seguinte | mostrar todas)
Too much baseball. But still great. ( )
  marzagao | Jun 1, 2021 |
The first 3 chapters of this one were my favorites. ( )
  adamfortuna | May 28, 2021 |
As the most famous "data journalist" out there, Nate Silver is the poster child of the recent trend of using data analytics to enhance traditional journalism (not to be confused with writing whatever you want and then throwing a graph on it). While he made his name in baseball and politics, and he does discuss those subjects in detail, in this book he also sets his sights a little higher: not just how to tell good predictions from bad predictions, but how to actually make predictions, weigh evidence, and even change your mind. The first part of the book discusses examples of prediction in sports, economics, and more offbeat areas like earthquakes and natural disasters, while the second is a guided tour through Bayesian statistics, the current best model we have for making inferences about uncertain events and updating our guesses in light of new information. Agree or disagree with him, the alternative to the bad use of statistics is not no use of statistics, and Silver's approach to modeling is about the best there is out there in terms of humility and rigor. Even should you violently disagree with him, he quite helpfully gives you plenty of conceptual tools to go and improve on his work on your own. Sometimes the chapters feel breezy, but given how low the general level of statistical literacy is out there, in comparison this is a masterclass in clear, concise, and useful thinking. And, in a slightly meta reflection upon finishing the book, given his relative success in acknowledging the uncertainty in the 2016 election forecasts (his model gave Trump 30% chance to win on the eve of the election vs 1% at places like the New York Times) I feel I can trust him more, which essentially proves his point. It's always neat when a book does that. ( )
  aaronarnold | May 11, 2021 |
Enjoyable, but not really very insightful or educational. 4/5 for entertainment, 3/5 for education. A kind of tortured introduction to Bayes vs. Frequentists (without actually describing Frequentists very well). Lots of examples from crap like baseball which I really don't care about. I'd personally recommend a book like Freakonomics and a brief intro to statistics, instead.

(Audible audiobook) ( )
  octal | Jan 1, 2021 |
Let me say first, what I say about most non-fiction books I read: There was too much set-up and not enough conclusion. Nate Silver did a masterful job talking about fundamental predictability (found between the signal and noise) in many diverse industries and world events. But he offered little in the way of "what's next" and how we could all get better at predicting.

BUT.

What a great book! As I read through it, I gained an appreciation for just how smart Nate is, and why he was able to make such accurate forecasts at FiveThirtyEight. The guy understands system dynamics, probability, and what makes for good data ... in a lot of very different fields, in depth. And he understands and articulated well that there is still a lot of art to predictions - data models often need to be merged with experienced intuition.

There were many points in the book where he surprised me with his analysis and insight. For example: local weather forecasters over-predict rain because there is a strong incentive to get sunny days right more than rainy days (no one wants their picnic rained on, but a sunny day instead of rain is a happy bonus). But the national weather forecasts folks are spot on. He had some charts detailing the exact discrepancies.

And when he introduced Bayes's Theorem (a new concept for me), I was amazed at how well it could mathematically explain the probabilities we all felt on 9/11 after the first plane hit. We started with a low probability .. "Could this be a terrorist attack? No. It must have been an accident." But after the second plane hit we all rapidly switched to a very high probability .. "There is no doubt this was a planned attack."

He discussed the housing crash, climate change, earthquake predictions, basketball and baseball modeling, the stock market, poker, the logic process of the Deep Blue chess program, and more. In each case, he helps you see where the signal was in the model, where the noise was, and how it either succeeded or failed in making accurate predictions.

Anyone interested in logic, data analysis, or data-based predictions will very much enjoy this book. Great read. ( )
  pedstrom | Dec 22, 2020 |
Mostrando 1-5 de 73 (seguinte | mostrar todas)
The first thing to note about The Signal and the Noise is that it is modest – not lacking in confidence or pointlessly self-effacing, but calm and honest about the limits to what the author or anyone else can know about what is going to happen next. Across a wide range of subjects about which people make professional predictions – the housing market, the stock market, elections, baseball, the weather, earthquakes, terrorist attacks – Silver argues for a sharper recognition of "the difference between what we know and what we think we know" and recommends a strategy for closing the gap.
adicionado por eereed | editarGuardian, Ruth Scurr (Nov 9, 2012)
 
What Silver is doing here is playing the role of public statistician — bringing simple but powerful empirical methods to bear on a controversial policy question, and making the results accessible to anyone with a high-school level of numeracy. The exercise is not so different in spirit from the way public intellectuals like John Kenneth Galbraith once shaped discussions of economic policy and public figures like Walter Cronkite helped sway opinion on the Vietnam War. Except that their authority was based to varying degrees on their establishment credentials, whereas Silver’s derives from his data savvy in the age of the stats nerd.
adicionado por eereed | editarNew York Times, Noam Scheiber (Nov 2, 2012)
 
A friend who was a pioneer in the computer games business used to marvel at how her company handled its projections of costs and revenue. “We performed exhaustive calculations, analyses and revisions,” she would tell me. “And we somehow always ended with numbers that justified our hiring the people and producing the games we had wanted to all along.” Those forecasts rarely proved accurate, but as long as the games were reasonably profitable, she said, you’d keep your job and get to create more unfounded projections for the next endeavor.......
adicionado por marq | editarNew York Times, LEONARD MLODINOW (Oct 23, 2012)
 
In the course of this entertaining popularization of a subject that scares many people off, the signal of Silver’s own thesis tends to get a bit lost in the noise of storytelling. The asides and digressions are sometimes delightful, as in a chapter about the author’s brief adventures as a professional poker player, and sometimes annoying, as in some half-baked musings on the politics of climate change. But they distract from Silver’s core point: For all that modern technology has enhanced our computational abilities, there are still an awful lot of ways for predictions to go wrong thanks to bad incentives and bad methods.
adicionado por eereed | editarSlate, Matthew Yglesias (Oct 5, 2012)
 
Mr. Silver reminds us that we live in an era of "Big Data," with "2.5 quintillion bytes" generated each day. But he strongly disagrees with the view that the sheer volume of data will make predicting easier. "Numbers don't speak for themselves," he notes. In fact, we imbue numbers with meaning, depending on our approach. We often find patterns that are simply random noise, and many of our predictions fail: "Unless we become aware of the biases we introduce, the returns to additional information may be minimal—or diminishing." The trick is to extract the correct signal from the noisy data. "The signal is the truth," Mr. Silver writes. "The noise is the distraction."
 

» Adicionar outros autores (12 possíveis)

Nome do autorFunçãoTipo de autorObra?Status
Nate Silverautor principaltodas as ediçõescalculado
Chamberlain, MikeNarradorautor secundárioalgumas ediçõesconfirmado
Dewey, AmandaDesignerautor secundárioalgumas ediçõesconfirmado
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Introduction

This is a book about information, technology, and scientific progress.
1
A CATASTROPHIC FAILURE
OF PREDICTION


It was October 23, 2008.
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Silver built an innovative system for predicting baseball performance, predicted the 2008 election within a hair's breadth, and became a national sensation as a blogger. Drawing on his own groundbreaking work, Silver examines the world of prediction.

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