The Signal and the Noise Why So Many Predictions Fail – but Some Don’t Nate Silver 544p

 

It is a pretty interesting book especially becus it covers some areas of science not usually covered in popsci (geology, meteorology), and i learned a lot. it is also clearly written and easy to read, which speeds up reading speeds, making the 450ish pages rather quickly to devour. From a learning perspectiv this is awesome as it allows for faster learning. it shud also be mentioned that it has a lot of very useful illustrations which i shared on my social networks while reading it.

 

“Fortunately, Dustin is really cocky, because if he was the kind of person

who was intimidated—if he had listened to those people—it would have ruined

him. He didn’t listen to people. He continued to dig in and swing from his heels

and eventually things turned around for him.”

Pedroia has what John Sanders calls a “major league memory”—which is to

say a short one. He isn’t troubled by a slump, because he is damned sure that

he’s playing the game the right way, and in the long run, that’s what matters.

Indeed, he has very little tolerance for anything that distracts him from doing

his job. This doesn’t make him the most generous human being, but it is ex­

actly what he needs in order to play second base for the Boston Red Sox, and

that’s the only thing that Pedroia cares about.

“Our weaknesses and our strengths are always very intimately connected,”

James said. “Pedroia made strengths out of things that would be weaknesses for

other players.”

 

This sounds like low agreeableness to me. I wonder if Big Five can predict baseball success?

 

 

The statistical reality of accuracy isn’t necessarily the governing paradigm

when it comes to commercial weather forecasting. It’s more the perception of

accuracy that adds value in the eyes of the consumer.

For instance, the for-profit weather forecasters rarely predict exactly a

50 percent chance of rain, which might seem wishy-washy and indecisive to

consumers.41 Instead, they’ll flip a coin and round up to 60, or down to 40, even

though this makes the forecasts both less accurate and less honest.42

 

Floehr also uncovered a more flagrant example of fudging the numbers,

something that may be the worst-kept secret in the weather industry. Most com­

mercial weather forecasts are biased, and probably deliberately so. In particu­

lar, they are biased toward forecasting more precipitation than will actually

occur43—what meteorologists call a “wet bias.” The further you get from the

government’s original data, and the more consumer facing the forecasts, the

worse this bias becomes. Forecasts “add value” by subtracting accuracy.

 

thats interesting. never heard of this.

 

 

This logic is a little circular. TV weathermen say they aren’t bothering to

make accurate forecasts because they figure the public won’t believe them any­

way. But the public shouldn t believe them, because the forecasts aren’t accurate.

This becomes a more serious problem when there is something urgent—

something like Hurricane Katrina. Lots of Americans get their weather infor­

mation from local sources49 rather than directly from the Hurricane Center, so

they will still be relying on the goofball on Channel 7 to provide them with

accurate information. If there is a mutual distrust between the weather fore­

caster and the public, the public may not listen when they need to most.

 

Nicely illustrating for importance of honesty in reporting data, even on local TV.

 

 

In fact, the actual value for GDP fell outside the economists’ prediction

interval six times in eighteen years, or fully one-third of the time. Another

study,18 which ran these numbers back to the beginnings of the Survey of Pro­

fessional Forecasters in 1968, found even worse results: the actual figure for

GDP fell outside the prediction interval almost h a l f the time. There is almost

no chance19 that the economists have simply been unlucky; they fundamentally

overstate the reliability of their predictions.

 

In reality, when a group of economists give you their GDP forecast, the

true 90 percent prediction interval—based on how these forecasts have actually

performed20 and not on how accurate the economists claim them to be—spans

about 6.4 points of GDP (equivalent to a margin of error of plus or minus 3.2

percent).*

 

When you hear on the news that GDP will grow by 2.5 percent next year,

that means it could quite easily grow at a spectacular rate of 5.7 percent instead.

Or it could fall by 0.7 percent—a fairly serious recession. Economists haven’t

been able to do any better than that, and there isn’t much evidence that their

forecasts are improving. The old joke about economists’ having called nine out

of the last six recessions correctly has some truth to it; one actual statistic is that

in the 1990s, economists predicted only 2 of the 60 recessions around the world

a year ahead of time.21

 

and this is why we cant have nice things, i mean macroeconomics

 

 

I have no idea whether I was really a good player at the very outset. But the

bar set by the competition was low, and my statistical background gave me an

advantage. Poker is sometimes perceived to be a highly psychological game, a

battle of wills in which opponents seek to make perfect reads on one another by

staring into one another’s souls, looking for “tells” that reliably betray the con­

tents of the other hands. There is a little bit of this in poker, especially at the

higher limits, but not nearly as much as you’d think. (The psychological factors

in poker come mostly in the form of self-discipline.) Instead, poker is an incred­

ibly mathematical game that depends on making probabilistic judgments amid

uncertainty, the same skills that are important in any type of prediction.

 

The obvious idea is to program computers to play poker for u online. If they play against bad humans, they shud bring in a steady flow of cash for almost free.

 

 

 

“Fortunately, Dustin is really cocky, because if he was the kind of person

who was intimidated—if he had listened to those people—it would have ruined

him. He didn’t listen to people. He continued to dig in and swing from his heels

and eventually things turned around for him.”

Pedroia has what John Sanders calls a “major league memory”—which is to

say a short one. He isn’t troubled by a slump, because he is damned sure that

he’s playing the game the right way, and in the long run, that’s what matters.

Indeed, he has very little tolerance for anything that distracts him from doing

his job. This doesn’t make him the most generous human being, but it is ex­

actly what he needs in order to play second base for the Boston Red Sox, and

that’s the only thing that Pedroia cares about.

“Our weaknesses and our strengths are always very intimately connected,”

James said. “Pedroia made strengths out of things that would be weaknesses for

other players.”

This sounds like low agreeableness to me. I wonder if Big Five can predict baseball success?

The statistical reality of accuracy isn’t necessarily the governing paradigm

when it comes to commercial weather forecasting. It’s more the perception of

accuracy that adds value in the eyes of the consumer.

For instance, the for-profit weather forecasters rarely predict exactly a

50 percent chance of rain, which might seem wishy-washy and indecisive to

consumers.41 Instead, they’ll flip a coin and round up to 60, or down to 40, even

though this makes the forecasts both less accurate and less honest.42

Floehr also uncovered a more flagrant example of fudging the numbers,

something that may be the worst-kept secret in the weather industry. Most com­

mercial weather forecasts are biased, and probably deliberately so. In particu­

lar, they are biased toward forecasting more precipitation than will actually

occur43—what meteorologists call a “wet bias.” The further you get from the

government’s original data, and the more consumer facing the forecasts, the

worse this bias becomes. Forecasts “add value” by subtracting accuracy.

thats interesting. never heard of this.

This logic is a little circular. TV weathermen say they aren’t bothering to

make accurate forecasts because they figure the public won’t believe them any­

way. But the public shouldn t believe them, because the forecasts aren’t accurate.

This becomes a more serious problem when there is something urgent—

something like Hurricane Katrina. Lots of Americans get their weather infor­

mation from local sources49 rather than directly from the Hurricane Center, so

they will still be relying on the goofball on Channel 7 to provide them with

accurate information. If there is a mutual distrust between the weather fore­

caster and the public, the public may not listen when they need to most.

Nicely illustrating for importance of honesty in reporting data, even on local TV.

In fact, the actual value for GDP fell outside the economists’ prediction

interval six times in eighteen years, or fully one-third of the time. Another

study,18 which ran these numbers back to the beginnings of the Survey of Pro­

fessional Forecasters in 1968, found even worse results: the actual figure for

GDP fell outside the prediction interval almost h a l f the time. There is almost

no chance19 that the economists have simply been unlucky; they fundamentally

overstate the reliability of their predictions.

In reality, when a group of economists give you their GDP forecast, the

true 90 percent prediction interval—based on how these forecasts have actually

performed20 and not on how accurate the economists claim them to be—spans

about 6.4 points of GDP (equivalent to a margin of error of plus or minus 3.2

percent).*

When you hear on the news that GDP will grow by 2.5 percent next year,

that means it could quite easily grow at a spectacular rate of 5.7 percent instead.

Or it could fall by 0.7 percent—a fairly serious recession. Economists haven’t

been able to do any better than that, and there isn’t much evidence that their

forecasts are improving. The old joke about economists’ having called nine out

of the last six recessions correctly has some truth to it; one actual statistic is that

in the 1990s, economists predicted only 2 of the 60 recessions around the world

a year ahead of time.21

and this is why we cant have nice things, i mean macroeconomics

I have no idea whether I was really a good player at the very outset. But the

bar set by the competition was low, and my statistical background gave me an

advantage. Poker is sometimes perceived to be a highly psychological game, a

battle of wills in which opponents seek to make perfect reads on one another by

staring into one another’s souls, looking for “tells” that reliably betray the con­

tents of the other hands. There is a little bit of this in poker, especially at the

higher limits, but not nearly as much as you’d think. (The psychological factors

in poker come mostly in the form of self-discipline.) Instead, poker is an incred­

ibly mathematical game that depends on making probabilistic judgments amid

uncertainty, the same skills that are important in any type of prediction.

The obvious idea is to program computers to play poker for u online. If they play against bad humans, they shud bring in a steady flow of cash for almost free.

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