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.
There's always the risk that there are unknown unknowns.
We're not that much smarter than we used to be, even though we have much more information - and that means the real skill now is learning how to pick out the useful information from all this noise.
Data scientist is just a sexed up word for statistician.
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.
We must become more comfortable with probability and uncertainty.
Success makes you less intimidated by things.
When human judgment and big data intersect there are some funny things that happen
New ideas are sometimes found in the most granular details of a problem where few others bother to look.
I was looking for something like baseball, where there's a lot of data and the competition was pretty low. That's when I discovered politics.
We look at all the polls, not just the Gallup Poll. So, it's kind of like if you have, you know, four out of five doctors agree that reducing cholesterol reduces your risk of a heart attack, Gallup is like the fifth doctor.
The Protestant Reformation had a lot to do with the printing press, where Martin Luther's theses were reproduced about 250,000 times. And so you had widespread dissemination of ideas that hadn't circulated in the mainstream before.
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 signal or noise
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.
You can build a statistical model and that's all well and good, but if you're dealing with a new type of financial instrument, for example, or a new type of situation - then the choices you're making are pretty arbitrary in a lot of respects.
Well the way we perceive accuracy and what accuracy is statistically are really two different things.
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.
On average, people should be more skeptical when they see numbers. They should be more willing to play around with the data themselves.
Every day, three times per second, we produce the equivalent of the amount of data that the Library of Congress has in it's entire print collection, right? But most of it is like cat videos on YouTube or thirteen-year-olds exchanging text messages about the next 'Twilight' movie.
Whenever you have dynamic interactions between 300 million people and the American economy acting in really complex ways, that introduces a degree of almost chaos theory to the system, in a literal sense.
People don't have a good intuitive sense of how to weigh new information in light of what they already know. They tend to overrate it.
I don't think you should limit what you read.
I have to make sure that I make good choices and that if I put my name on it, it's a high-quality endeavor and that I have time to be a human being.
I know it's cheaper to fund an op-ed columnist than a team of reporters, but I think it confuses the mission of what these great journalistic brands are about.
A lot of the time nothing happens in a day.
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