Reddit reviews The Signal and the Noise: Why So Many Predictions Fail--but Some Don't

And mocking him for being "wrong" just belies a complete lack of understanding of statistics and probability. I mean, if he had her penned at 70% (as the post up above indicated), then he's forecasting a 30% chance she'll lose. 30% is a pretty significant chance - not unlikely at all!

The problem is that, emotionally, people would rather somebody forecast something with certainty (even if the prediction is completely divorced from reality) than have someone use all the information we have, as well as we can use it, and arrive at the conclusion that "we really don't know for sure".

Nate Silver's book talks about this quite a bit and I'd definitely recommend it to anyone interested in data analysis and/or political punditry.

Calibration exercises may not be common outside the rationality sphere, but they certainly didn't start within the rationality sphere either. AFAIK, Philip Tetlock started the trend in Expert Political Judgement when he ran a series of experiments comparing expert predictions to random university undergrads. The experts outperformed, but they could further be divided into foxes and hedgehogs - those who know many things, and those who are quite certain of one thing.

Anyway, Silver quotes Tetlock in his book The Signal and the Noise, so I'm guessing that's where he got the idea.

This is why statistics are so easy to lie with. No one puts the basic effort into understanding what they mean.

Plenty of teams have overcome low odds. There's plenty of variance in sports and the weaknesses of 538s model for direct-game prediction is well known (for ex: the fact CARMELO measures players instead of teams as a unit, that it uses data from the regular season despite large post-season statistical gaps, etc).

The later into the playoffs the more accurate it will get. But it's still a small amount of data but despite all of that it's still one of the best we've got.

FWIW, if you care, 538's creator wrote a good book about bayesian statistics and prediction modelling: https://www.amazon.com/Signal-Noise-Many-Predictions-Fail-but/dp/0143125087/

Ditto over here. I just started reading and I know I'm going to have to come back later. Nate Silver writes some interesting words in the preface of "The Signal and The Noise" that relate to this topic. He states that what we're experiencing now has analogues in history - the most salient example being that the advent of the printing press was eventually responsible for newspapers that catered to every viewpoint or belief structure. The Internet has just made the rabbit hole deeper and more lifelike while requiring considerably less imagination to continue on.

>Except an election is not random probability

No one is claiming that, they use predictive probability I just gave an example that's simpler.

>His method for calculating the chances was straight up wrong.

Do you have a source that backs that up?


Please read this

And this

And if you don't understand what those are saying, please read this

*edits

> I feel like a lot of people here have this pervasive need to look down on people who made bad calls.

I mention people's predictions not to "look down" on them, but because I'm interested in predictions, especially when they contrast and discussing that.

I especially became interested in that after reading Nate Silver's book (the statistician who started FiveThirtyEight) The Signal and the Noise: Why So Many Predictions Fail. Nate discusses at one point that people should keep better track of predictions, because otherwise you end up mostly remembering when someone was right and forgetting the times they were wrong (or vice versa). Some people become overconfident in their prediction abilities.

If you haven’t read Malcolm Gladwell’s books those are good; he reads his own audiobooks and I like his speaking style. He also has a podcast called revisionist history that I really like.

Tetlock’s superforecasting is a bit long-winded but good; it’s a lay-person’s book on his research for IARPA (intelligence research) to improve intelligence assessments. His intro mentions Kahneman and Duckworth’s grit. I haven’t read it yet, but Nate Silver’s signal and the noise is in a similar vein to Tetlock’s book and is also recommended by IARPA.

Jonathan Haidt’s The Righteous Mind was really eye-opening to me to understand the differences in the way that liberals and conservatives (both in the political and cultural sense) view the world around them and how that affects social cohesion. He has a few TED talks if you’d like to get an idea of his research. Related, if you’re interested in an application of Kahneman’s research in politics, the Rationalizing Voter was a good book.

As a “be a better person” book, I really liked 7 habits of highly effective people by Stephen Covey (recommend it on audiobook). Particularly, unlike other business-style self-help about positive thinking and manipulating people—this book really makes you examine your core values, what’s truly important to you and gives you some tools to help refocus your efforts in those directions. Though, as I’m typing this I’m thinking about the time I’m spending on reddit and not reading the book I’ve been meaning to all night =p

Signal and the Noise by Nate Silver

makes me think about what ML/AI/DS can do and can not do. What should I be focusing on...

The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists

Good inspirations from many data scientists

You should take a look at Nate Silver's book "The Signal and the Noise: Why So Many Predictions Fail--but Some Don't".

It explains exactly what you're talking about in intense detail.

NWS is one of the sources for raw data, but they have their own forecast algorithms. Nate Silver did a lengthy piece on weather forecasting in The Signal and the Noise, which discussed how for profit forecasting services tend to skew the forecast depending on their advertisers since it's a game of probability.

How about Nate Silver's The Signal and the Noise or Sharon Bertsch McGrayne's The Theory that Would not die? Both good books about statistics/data science.

https://www.amazon.com/Signal-Noise-Many-Predictions-Fail-but/dp/0143125087/ref=sr_1_1?ie=UTF8&qid=1538957790&sr=8-1&keywords=the+signal+and+the+noise

https://www.amazon.com/dp/B0050QB3EQ/ref=dp-kindle-redirect?_encoding=UTF8&btkr=1

He is a statistician and analyst who wrote a pretty good and very accessible book called The Signal and the Noise

Or this one.

"All models are wrong, but some are useful." - George Box

From my experience, it generally appears to be that it's always possible to have more information from which to source; however, barriers such as cost and time often prohibit you from being able to do so.

I think there will always be a better statistical model out there; I don't see anything wrong with updating your model over time as you come across more information or more effective techniques to do the analysis you would want.

I think these two sources should interest you. (1) (2)

Also Nate Silver's Signal and the Noise might be worth a read. It's not a technical manual, but it will give you some things to think about regarding the use of models in a wide variety of fields.

Hope that helps!

The Signal and the Noise by Nate Silver?

The Signal and the Noise by Nate Silver.

It's a great introduction to how probability and statistics can be used to model real events in the future, and why many of those predictions don't work very well at all. The book is more about the logic and principles of predictions than the math behind them, so if statistics intimidate you but forecasting fascinates you, this is the book you'll want to read.

>such as you ultimately study things to help yourself, but really you're helping someone else who will pay you so you can help yourself

I love to learn about about laminar flow, the Germans who got lost in Death Valley (seriously read this, and his post on the hunt for downed airplanes), how ISIS formed, and an understanding of how people interpret data not because it will pay me, but because it makes me a smarter person. Your own intellect is not most beneficial to you because of what words you can make come out of your mouth, but by letting you decipher the words that come out of other people's mouths. The best thing about knowledge is that it gives you perspective.

>The real question, however, is will that make you attractive to us ladies? That's still the big question. Getting in shape will. Playing board games "for the challenge" with nerdy dudes who talk about pseudo philosophical BS they read on the internet? Not so much.

There in lies your problem. You're wanting to form yourself to make yourself attractive, where as long as you're a top 10 then you can do whatever you want. You honestly think a guy who is in great shape, well read, and works hard but happens to like board games or D&D can't get a girl?. Sure, if that's your only hobby it will be harder, but everyone should have multiple hobbies that they're passionate about. Women love confidence and passion. Confidence is just a different word for truly loving and believing in yourself regardless of what other people think. Passion is just another word for what you really really like.

If you don't love yourself, why would anyone else?

De acord!

Depinde și ce înțelegi prin imaginație, în unele aplicații un soft poate fi mai inventiv decât noi. Citeam în The Signal and the Noise că la unele turnee de șah ăia care trișează sunt prinși tocmai pentru că fac niște mutări atât de inventive și ieșite din comun încât ridică semne de întrebare că niciun om nu poate gândi atât de mult în perspectivă, deci trebuie să fie "ajutat" de un AI.

La ce te referi prin imaginație? Care ar fi aplicațiile imaginației noastre prin care ne-am păstra totuși un avantaj față de AI? Sunt sincer curios.

The Signal and the Noise, by Nate Silver. (Goodreads, Amazon)

Speaks about the way we make predictions in various fields, such as the economy, politics, weather, stock market, sports (which teams win championships, which players will become successful) and others.

He's the chief editor of the news website fivethirtyeight. Although its America-centric, the quality of the reporting there is peerless because its entirely backed up by data, rather than opinion.