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-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.
New ideas are sometimes found in the most granular details of a problem where few others bother to look.
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.
Success makes you less intimidated by things.
We must become more comfortable with probability and uncertainty.
Data scientist is just a sexed up word for statistician.
On average, people should be more skeptical when they see numbers. They should be more willing to play around with the data themselves.
When human judgment and big data intersect there are some funny things that happen
Well the way we perceive accuracy and what accuracy is statistically are really two different things.
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.
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.
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.
By playing games you can artificially speed up your learning curve to develop the right kind of thought processes.
When a possibility is unfamiliar to us, we do not even think about it.
You don't want to treat any one person as oracular.
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.
You don't want to influence the same system you are trying to forecast.
Follow AzQuotes on Facebook, Twitter and Google+. Every day we present the best quotes! Improve yourself, find your inspiration, share with friends