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.
On average, people should be more skeptical when they see numbers. They should be more willing to play around with the data themselves.
We must become more comfortable with probability and uncertainty.
Success makes you less intimidated by things.
Data scientist is just a sexed up word for statistician.
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.
When human judgment and big data intersect there are some funny things that happen
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.
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.
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.
In baseball you have terrific data and you can be a lot more creative with it.
People gravitate toward information that implies a happier outlook for them.
The key to making a good forecast is not in limiting yourself to quantitative information.
Distinguishing the signal from the noise requires both scientific knowledge and self-knowledge.
It's a little strange to become a kind of symbol of a whole type of analysis.
I'm not trying to do anything too tricky.
Midterm elections can be dreadfully boring, unfortunately.
I have to think about how to not spread myself too thin. It's a really great problem to have.
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