Nate Silver Quotes

Distinguishing the signal from the noise requires both scientific knowledge and self-knowledge: the serenity to accept the things we cannot predict, the courage to predict the things we can, and the wisdom to know the difference.

The signal is the truth. The noise is what distracts us from the truth.

In America, everyone is entitled to an opinion, and it is certainly useful to have a few when a pollster shows up. But these are opinions of a quite different roder from eighteenth- or nineteenth-century opinions. It is probably more accurate to call them emotions rather than opinions, which would account for the fact that they change from week to week, as the pollsters tell us. What is happening here is that television is altering the meaning of 'being informed' by creating a species of information that might properly be called disinformation. I am using this world almost in the precise sense in which it is used by spies in the CIA or KGB. Disinformation does not mean false information. It means misleading information--misplace, irrelevant, fragmented or superficial information--information that creates the illusion of knowing something but which in fact leads one away from knowing. In saying this, I do not mean to imply that television news deliberately aims to deprive Americans of a coherent, contextual understanding of their world. I mean to say that when news is packaged as entertainment, that is the inevitable result. And in saying that the television news show entertains but does not inform, I am saying something far more serious than that we are being deprived of authentic information. I am saying we are losing our sense of what it means to be well informed. Ignorance is always correctable. But what shall we do if we take ignorance to be knowledge?

The irony is that by being less focused on your results, you may achieve better ones.

Nate handed it over the expert, and Waschbär examined it in turn, finally concluding with a low whistle. "No, not a diamond. It's a star." "A star?" the fairies chorused. "Yer cryin' th' stars from yer eyes." Nate's hands on the reins tightened as he added, "Fer Ariel.

We need to stop, and admit it: we have a prediction problem. We love to predict things—and we aren’t very good at it.

Most of you will have heard the maxim "correlation does not imply causation." Just because two variables have a statistical relationship with each other does not mean that one is responsible for the other. For instance, ice cream sales and forest fires are correlated because both occur more often in the summer heat. But there is no causation; you don't light a patch of the Montana brush on fire when you buy a pint of Haagan-Dazs.

Voting, we might even say, is the next to last refuge of the politically impotent. The last refuge is, of course, giving your opinion to a pollster, who will get a version of it through a desiccated question, and then will submerge it in a Niagara of similar opinions, and convert them into--what else?--another piece of news. Thus we have here a great loop of impotence: The news elicits from you a variety of opinions about which you can do nothing except to offer them as more news, about which you can do nothing.

One of the pervasive risks that we face in the information age, as I wrote in the introduction, is that even if the amount of knowledge in the world is increasing, the gap between what we know and what we think we know may be widening.

Data Is Useless Without Context

If political scientists couldn’t predict the downfall of the Soviet Union—perhaps the most important event in the latter half of the twentieth century—then what exactly were they good for?

Shakespeare's plays often turn on the idea of fate, as much drama does. What makes them so tragic is the gap between what his characters might like to accomplish and what fate provides them.

Wherever there is human judgment there is the potential for bias.

Money doesn't make you happy... but it sure doesn't make you said either!" - Nate Temple

The story the data tells us is often the one we’d like to hear, and we usually make sure that it has a happy ending.

Under Bayes' theorem, no theory is perfect. Rather, it is a work in progress, always subject to further refinement and testing.

When catastrophe strikes, we look for a signal in the noise - anything that might explain the chaos that we see all around us and bring order to the world again.

The more interviews that an expert had done with the press, Tetlock found, the worse his predictions tended to be.

Economy is not baseball, where the game is always played by the same rules.

As the statistician George E. P. Box wrote, "All models are wrong, but some models are useful." What he meant by that is that all models are simplifications of the universe, as they must necessarily be. As another mathematician said, "The best model of a cat is a cat." ... The key is in remembering that a model is a tool to help us understand the complexities of the universe, and never a substitute for the universe itself.

It was a gift of mine. Annoying people to immeasurable levels of hatred.

I spotted a shiny item on the ground, and like all girls, squirrels, and toddlers, I made a move to grab it.

Finding patterns is easy in any kind of data-rich environment; that’s what mediocre gamblers do. The key is in determining whether the patterns represent noise or signal.

The numbers have no way of speaking for themselves. We speak for them. We imbue them with meaning. — Nate Silver, The Signal and the Noise1

Good innovators typically think very big and they think very small. New ideas are sometimes found in the most granular details of a problem where few others bother to look. And they are sometimes found when you are doing your most abstract and philosophical thinking, considering why the world is the way that it is and whether there might be an alternative to the dominant paradigm. Rarely can they be found in the temperate latitudes between they two spaces, where we spend 99 percent of our lives.

Who needs theory when you have so much information? But this is categorically the wrong attitude to take toward forecasting, especially in a field like economics where the data is so noisy.

There are 1,013,900 words in the English language, and none of them accurately portray how badly I want to hit you over the head with a chair. A heavy Amish chair.” Gavin…

In science, progress is possible. In fact, if one believes in Bayes' theorem, scientific progress is inevitable as predictions are made and as beliefs are tested and refined.

If there is a mutual distrust between the weather forecaster and the public, the public may not listen when they need to most.

When they play against each other, the game usually comes down to who can find his opponent’s blind spots first.

For Popper, a hypothesis was not scientific unless it was falsifiable—meaning that it could be tested in the real world by means of a prediction.

Absolutely nothing useful is realized when one person who holds that there is a 0 percent probability of something argues against another person who holds that the probability is 100 percent.

When we advance more confident claims and they fail to come to fruition, this constitutes much more powerful evidence against our hypothesis. We can't really blame anyone for losing faith when this occurs

We face danger whenever information growth outpaces our understanding of how to process it. The last forty years of human history imply that it can still take a long time to translate information into useful knowledge, and that if we are not careful, we may take a step back in the meantime.

What makes him successful is the way that he analyzes information. He is not just hunting for patterns. Instead, Bob combines his knowledge of statistics with his knowledge of basketball in order to identify meaningful relationships in the data.

But forecasters often resist considering these out-of-sample problems. When we expand our sample to include events further apart from us in time and space, it often means that we will encounter cases in which the relationships we are studying did not hold up as well as we are accustomed to. The model will seem to be less powerful. It will look less impressive in a PowerPoint presentation (or a journal article or a blog post). We will be forced to acknowledge that we know less about the world than we thought we did. Our personal and professional incentives almost always discourage us from doing this.

It’s never good when you blindly follow some belief system or group of people without consciously deducing whether what they do is right or wrong. The number one test is to wonder what would happen if you openly, but respectfully, questioned your commander’s decision when you think it’s wrong. If the answer in any way resembles punishment, pain, or ridicule, rather than an explanation, you are probably not working for the good guys.” Gavin

We are undoubtedly living with many delusions that we do not even realize.

Nothing is written in stone.” “The Ten Commandments are. And I think we both know that neither of us would pull off an A on that grading scale.

You stupid thundercunt. I’m a Beast Master. I own shifters. And you are going to pay very dearly for that oversight.

What a well-designed forecasting system can do is sort out which statistics are relatively more susceptible to luck; batting average, for instance, is more erratic than home runs.

Human beings have an extraordinary capacity to ignore risks that threaten their livelihood, as though this will make them go away.

After adjusting for inflation, a $10,000 investment made in a home in 1896 would be worth just $10,600 in 1996.

As John Maynard Keynes said, “The market can stay irrational longer than you can stay solvent.

A forecaster who says he doesn’t care about the science is like the cook who says he doesn’t care about food.

The litmus test for whether you are a competent forecaster is if more information makes your predictions better.

The leap is into the Bayesian way of thinking about prediction and probability.

All models are wrong, but some models are useful.”90 What he meant by that is that all models are simplifications of the universe, as they must necessarily be.

How can we apply our judgment to the data—without succumbing to our biases?

We may, without even realizing it, work backward to generate persuasive-sounding theories that rationalize them, and these will often fool our friends and colleagues as well as ourselves.

If you compare the number of children who are diagnosed as autistic64 to the frequency with which the term autism has been used in American newspapers,65 you’ll find that there is an almost perfect one-to-one correspondence (figure 7-4), with both having increased markedly in recent years.

The key to making a good forecast, as we observed in chapter 2, is not in limiting yourself to quantitative information. Rather, it’s having a good process for weighing the information appropriately.

On Rachel's show for November 7, 2012: Ohio really did go to President Obama last night. and he really did win. And he really was born in Hawaii. And he really is legitimately President of the United States, again. And the Bureau of Labor statistics did not make up a fake unemployment rate last month. And the congressional research service really can find no evidence that cutting taxes on rich people grows the economy. And the polls were not screwed to over-sample Democrats. And Nate Silver was not making up fake projections about the election to make conservatives feel bad; Nate Silver was doing math. And climate change is real. And rape really does cause pregnancy, sometimes. And evolution is a thing. And Benghazi was an attack on us, it was not a scandal by us. And nobody is taking away anyone's guns. And taxes have not gone up. And the deficit is dropping, actually. And Saddam Hussein did not have weapons of mass destruction. And the moon landing was real. And FEMA is not building concentration camps. And you and election observers are not taking over Texas. And moderate reforms of the regulations on the insurance industry and the financial services industry in this country are not the same thing as communism. Listen, last night was a good night for liberals and for democrats for very obvious reasons, but it was also, possibly, a good night for this country as a whole. Because in this country, we have a two-party system in government. And the idea is supposed to be that the two sides both come up with ways to confront and fix the real problems facing our country. They both propose possible solutions to our real problems. And we debate between those possible solutions. And by the process of debate, we pick the best idea. That competition between good ideas from both sides about real problems in the real country should result in our country having better choices, better options, than if only one side is really working on the hard stuff. And if the Republican Party and the conservative movement and the conservative media is stuck in a vacuum-sealed door-locked spin cycle of telling each other what makes them feel good and denying the factual, lived truth of the world, then we are all deprived as a nation of the constructive debate about competing feasible ideas about real problems. Last night the Republicans got shellacked, and they had no idea it was coming. And we saw them in real time, in real humiliating time, not believe it, even as it was happening to them. And unless they are going to secede, they are going to have to pop the factual bubble they have been so happy living inside if they do not want to get shellacked again, and that will be a painful process for them, but it will be good for the whole country, left, right, and center. You guys, we're counting on you. Wake up. There are real problems in the world. There are real, knowable facts in the world. Let's accept those and talk about how we might approach our problems differently. Let's move on from there. If the Republican Party and the conservative movement and conservative media are forced to do that by the humiliation they were dealt last night, we will all be better off as a nation. And in that spirit, congratulations, everyone!

Paperback and buckram books decorated the floor, torn open to leave loose pages lying about like the useless guts of an eviscerated animal. Fury smoldered deep down inside my stomach, as if I were looking at heaps of dead children lying about the room instead. Books were my children. It was sacrilegious. Hundreds

War is wild, but one must know when to be wild, and when to keep calm. Too cold, and you become calloused, brittle, and breakable. Too hot, and your emotions rule you, stealing your control. But to be strategically ruthless? Analytically wild? In a General, these qualities are priceless. For everything else, there’s soldiers,

A forecaster should almost never ignore data, especially when she is studying rare events like recessions or presidential elections, about which there isn’t very much data to begin with. Ignoring data is often a tip-off that the forecaster is overconfident, or is overfitting her model—that she is interested in showing off rather than trying to be accurate.

Making predictions based on our beliefs is the best (and perhaps even the only) way to test ourselves. If objectivity is the concern for a greater truth beyond our personal circumstances, and prediction is the best way to examine how closely aligned our personal perceptions are with that greater truth, the most objective among us are those who make the most accurate predictions.

The alchemy that the ratings agencies performed was to spin uncertainty into what looked and felt like risk. They took highly novel securities, subject to an enormous amount of systemic uncertainty, and claimed the ability to quantify just how risky they were. Not only that, but of all possible conclusions, they came to the astounding one that these investments were almost risk-free.

Quite a lot of evidence suggests that aggregate or group forecasts are more accurate than individual ones, often somewhere between 15 and 20 percent more accurate depending on the discipline. That doesn’t necessarily mean the group forecasts are good. (We’ll

The most calamitous failures of prediction usually have a lot in common. We focus on those signals that tell a story about the world as we would like it to be, not how it really is. We ignore the risks that are hardest to measure, even when they pose the greatest threats to our well-being. We make approximations and assumptions about the world that are much cruder than we realize. We abhor uncertainty, even when it is an irreducible part of the problem we are trying to solve.

Love is precious, and shouldn’t be wasted on every passing whim, or it will mean nothing by the time you truly wish to share it with someone who matters.” I said softly. Silvers, Shayne (2012-10-08). Obsidian Son: A Novel In The Nate Temple Supernatural Thriller Series (The Temple Chronicles Book 1) (p. 194). Argento Publishing. Kindle Edition.

I was a putrid stain on the great, pristine cog of bureaucracy that is the United States government.

skidded to my knees, spun, and unleashed a wallop of purple power, angled up at the sky. I had practiced this one all by myself, and was proud of it.

My tinfoil hat grew taller.

Fucking cats and shiny things. No matter how bloodthirsty the feline, they see a shiny and lose their shit.

Emotions could be secretly transferred via physical contact, so that the enemy—women—could never learn how deep and vulnerable we truly were.

He jumped to his feet a few moments later, his eye a little wild around the edges, and his fur was sticking straight out, making him look like a mutant Pomeranian.

I nestled myself into this cozy cleavage of carnage

And, kids, that’s what happens when you open Pandora’s Box, I thought to myself.

Bayes’s theorem predicts that the Bayesians will win.

Nobody saw it coming. When you can’t state your innocence, proclaim your ignorance: this is often the first line of defense when there is a failed forecast.

Not only does political coverage often lose the signal—it frequently accentuates the noise.

even if the amount of knowledge in the world is increasing, the gap between what we know and what we think we know may be widening.

The forecasters later told researchers that they were afraid the public might lose confidence in the forecast if they had conveyed any uncertainty in the outlook.

Technology is beneficial as a labor-saving device, but we should not expect machines to do our thinking for us.

But Mann, who blogs along with Schmidt at RealClimate.org, sees himself as engaged in trench warfare against groups like the Heartland Institute. “We’re

When you find yourself knee-deep in shit, don’t sit there complaining about it. Start walking.” She

So we should have some sympathy for economic forecasters.50 It’s hard enough to know where the economy is going. But it’s much, much harder if you don’t know where it is to begin with.

In science, dubious forecasts are more likely to be exposed—and the truth is more likely to prevail. In politics, a domain in which the truth enjoys no privileged status, it’s anybody’s guess.

Johannes Gutenberg’s invention in 1440 made information available to the masses, and the explosion of ideas it produced had unintended consequences and unpredictable effects. It was a spark for the Industrial Revolution in 1775,1 a tipping point in which civilization suddenly went from having made almost no scientific or economic progress for most of its existence to the exponential rates of growth and change that are familiar to us today. It set in motion the events that would produce the European Enlightenment and the founding of the American Republic. But the printing press would first produce something else: hundreds of years of holy war. As mankind came to believe it could predict its fate and choose its destiny, the bloodiest epoch in human history followed.2

John P. Ioannidis published a controversial paper titled “Why Most Published Research Findings Are False.”39 The paper studied positive findings documented in peer-reviewed journals: descriptions of successful predictions of medical hypotheses carried out in laboratory experiments. It concluded that most of these findings were likely to fail when applied in the real world. Bayer Laboratories recently confirmed Ioannidis’s hypothesis. They could not replicate about two-thirds of the positive findings claimed in medical journals when they attempted the experiments themselves.40

Sir Muffle Paws darted between my legs before hurling himself up onto a side-table that held a ritzy silver saucer with fresh cream. I rolled my eyes at the opulence granted to the filthy feline.

Data-driven predictions can succeed—and they can fail. It is when we deny our role in the process that the odds of failure rise. Before we demand more of our data, we need to demand more of ourselves.

Meanwhile, exposure to so many new ideas was producing mass confusion. The amount of information was increasing much more rapidly than our understanding of what to do with it, or our ability to differentiate the useful information from the mistruths. Paradoxically, the result of having so much more shared knowledge was increasing isolation along national and religious lines. The instinctual shortcut that we take when we have “too much information” is to engage with it selectively, picking out the parts we like and ignoring the remainder, making allies with those who have made the same choices and enemies of the rest.

Do I need to check up on you guys later? You know the rules.No sleeping in opposite-sex rooms." My face flames,and St. Clair's cheeks grow blotchy. It's true.It's a rule. One that my brain-my rule-loving, rule-abiding brain-conveniently blocked last night. It's also one notoriously ignored by the staff. "No,Nate," we say. He shakes his shaved head and goes back in his apartment. But the door opens quickly again,and a handful of something is thrown at us before it's slammed back shut. Condoms.Oh my God, how humiliating. St. Clair's entire face is now bright red as he picks the tiny silver squares off the floor and stuffs them into his coat pockets. We don't speak,don't even look at each other,as we climb the stairs to my floor. My pulse quickens with each step.Will he follow me to my room,or has Nate ruined any chance of that? We reach the landing,and St. Clair scratches his head. "Er..." "So..." "I'm going to get dressed for bed. Is that all right?" His voice is serious,and he watches my reaction carefully. "Yeah.Me too.I'm going to...get ready for bed,too." "See you in a minute?" I swell with relief. "Up there or down here?" "Trust me,you don't want to sleep in my bed." He laughs,and I have to turn my face away,because I do,holy crap do I ever. But I know what he means.It's true my bed is cleaner. I hurry to my room and throw on the strawberry pajamas and an Atlanta Film Festival shirt. It's not like I plan on seducing him. Like I'd even know how. St. Clair knocks a few minutes later, and he's wearing his white bottoms with the blue stripes again and a black T-shirt with a logo I recognize as the French band he was listening to earlier. I'm having trouble breathing. "Room service," he says. My mind goes...blank. "Ha ha," I say weakly. He smiles and turns off the light. We climb into bed,and it's absolutely positively completely awkward. As usual. I roll over to my edge of the bed. Both of us are stiff and straight, careful not to touch the other person. I must be a masochist to keep putting myself in these situations. I need help. I need to see a shrink or be locked in a padded cell or straitjacketed or something. After what feels like an eternity,St. Clair exhales loudly and shifts. His leg bumps into mine, and I flinch. "Sorry," he says. "It's okay." "..." "..." "Anna?" "Yeah?" "Thanks for letting me sleep here again. Last night..." The pressure inside my chest is torturous. What? What what what? "I haven't slept that well in ages." The room is silent.After a moment, I roll back over. I slowly, slowly stretch out my leg until my foot brushes his ankle. His intake of breath is sharp. And then I smile,because I know he can't see my expression through the darkness.

He does not depend on insider tips, crooked referees, or other sorts of hustles to make his bets. Nor does he have a “system” of any kind. He uses computer simulations, but does not rely upon them exclusively.

The River is a sprawling ecosystem of like-minded people that includes everyone from low-stakes poker pros just trying to grind out a living to crypto kings and venture-capital billionaires. It is a way of thinking and a mode of life. People don’t know very much about the River, but they should. Most Riverians aren’t rich and powerful. But rich and powerful people are disproportionately likely to be Riverians compared to the rest of the population.

It is much easier after the event to sort the relevant from the irrelevant signals. After the event, of course, a signal is always crystal clear; we can now see what disaster it was signaling, since the disaster has occurred. But before the event it is obscure and pregnant with conflicting meanings. It comes to the observer embedded in an atmosphere of “noise,” i.e., in the company of all sorts of information that is useless and irrelevant for predicting the particular disaster.

Political experts had difficulty anticipating the USSR’s collapse, Tetlock found, because a prediction that not only forecast the regime’s demise but also understood the reasons for it required different strands of argument to be woven together. There was nothing inherently contradictory about these ideas, but they tended to emanate from people on different sides of the political spectrum,11 and scholars firmly entrenched in one ideological camp were unlikely to have embraced them both.

Risk, as first articulated by the economist Frank H. Knight in 1921,45 is something that you can put a price on. Say that you’ll win a poker hand unless your opponent draws to an inside straight: the chances of that happening are exactly 1 chance in 11.46 This is risk. It is not pleasant when you take a “bad beat” in poker, but at least you know the odds of it and can account for it ahead of time. In the long run, you’ll make a profit from your opponents making desperate draws with insufficient odds. Uncertainty, on the other hand, is risk that is hard to measure. You might have some vague awareness of the demons lurking out there. You might even be acutely concerned about them. But you have no real idea how many of them there are or when they might strike. Your back-of-the-envelope estimate might be off by a factor of 100 or by a factor of 1,000; there is no good way to know. This is uncertainty. Risk greases the wheels of a free-market economy; uncertainty grinds them to a halt.

These partisan beliefs can upset the equation in which more information will bring us closer to the truth. A recent study in Nature found that the more informed that strong political partisans were about global warming, the less they agreed with one another.44

Thus, you will see apparently serious papers published on how toads can predict earthquakes,50 or how big-box stores like Target beget racial hate groups,51 which apply frequentist tests to produce “statistically significant” (but manifestly ridiculous) findings.

An admonition like "The more complex you make the model, the worse the forecast gets." is equivalent to saying "Never add too much salt to the recipe." How much complexity-how much salt-did you begin with? If you want to get good at forecasting, you'll need to immerse yourself in the craft and trust your own taste buds.

Meanwhile, if the quantity of information is increasing by 2.5 quintillion bytes per day, the amount of useful information almost certainly isn’t. Most of it is just noise, and the noise is increasing faster than the signal. There are so many hypotheses to test, so many data sets to mine—but a relatively constant amount of objective truth.

An old Cherokee man was teaching his grandson about life. “A fight is going on inside me,” he told the boy. “It is a terrible fight between two wolves. One is evil — he is anger, envy, sorrow, regret, greed, arrogance, self-pity, guilt, resentment, inferiority, lies, false pride, superiority, and ego.” After a few moments to make sure he had the boy’s undivided attention, he continued. “The other wolf is good — he is joy, peace, love, hope, serenity, humility, kindness, benevolence, empathy, generosity, truth, compassion, and faith. The same fight is going on inside of you, boy, and inside of every other person, too.” The grandson thought about this for a few minutes before replying. “Which wolf will win?” The old Cherokee man simply said, “The one you feed, boy. The one you feed…

In any contentious debate, some people will find it advantageous to align themselves with the crowd, while a smaller number will come to see themselves as persecuted outsiders. This may especially hold in a field like climate science, where the data is noisy and the predictions are hard to experience in a visceral way. And it may be especially common in the United States, which is admirably independent-minded.

On the other hand there was its culture. New Orleans does many things well, but there are two things that it proudly refuses to do. New Orleans does not move quickly, and New Orleans does not place much faith in authority. If it did those things, New Orleans would not really be New Orleans. It would also have been much better prepared to deal with Katrina, since those are the exact two things you need to do when a hurricane threatens to strike.

It is amusing to poke fun at the experts when their predictions fail. However, we should be careful with our Schadenfreude. To say our predictions are no worse than the experts’ is to damn ourselves with some awfully faint praise. Prediction does play a particularly important role in science, however. Some of you may be uncomfortable with a premise that I have been hinting at and will now state explicitly: we can never make perfectly objective predictions. They will always be tainted by our subjective point of view.

When we expand our sample to include events further apart from us in time and space, it often means that we will encounter cases in which the relationships we are studying did not hold up as well as we are accustomed to. The model will seem to be less powerful. It will look less impressive in a PowerPoint presentation (or a journal article or a blog post). We will be forced to acknowledge that we know less about the world than we thought we did. Our personal and professional incentives almost always discourage us from doing this. We forget—or we willfully ignore—that our models are simplifications of the world. We figure that if we make a mistake, it will be at the margin.

ballot you go, the more volatile the polls tend to be: polls of House races are less accurate than polls of Senate races, which are in turn less accurate than polls of presidential races. Polls of primaries, also, are considerably less accurate than general election polls. During the 2008 Democratic primaries, the average poll missed by about eight points, far more than implied by its margin of error. The problems in polls of the Republican primaries of 2012 may have been even worse.26 In many of the major states, in fact—including Iowa, South Carolina, Florida, Michigan, Washington, Colorado, Ohio, Alabama, and Mississippi—the candidate ahead in the polls a week before the election lost.