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.
New ideas are sometimes found in the most granular details of a problem where few others bother to look.
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.
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.
When human judgment and big data intersect there are some funny things that happen
On average, people should be more skeptical when they see numbers. They should be more willing to play around with the data themselves.
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.
Every day, three times per second, we produce the equivalent of the amount of data that the Library of Congress has in its entire print collection, right? But most of it is like cat videos on YouTube or 13-year-olds exchanging text messages about the next Twilight movie.
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.
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.
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
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.
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.
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've just always been a bit of a dork.
People gravitate toward information that implies a happier outlook for them.
A lot of the time nothing happens in a day.
I don't think that somebody who is observing or predicting behavior should also be participating in the 'experiment.'
If you're keeping yourself in the bubble and only looking at your own data or only watching the TV that fits your agenda then it gets boring.
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