Quotes About Data

The companies that do well are the companies that use math.

Analytics, math, science has gone into a lot of different areas of the NBA.

What I care about is maximum information.

We should see better and more direct measurements of economic well being.

Science by itself is about numbers, and it's about measuring things. It's very important but it's very dry.

All schools will end up using game metrics in the future.

While we wait for the scientific data to prove the harms of vaping, let's protect our middle and high schoolers from a lifelong addiction and a high risk of cancer.

There's nothing that cannot be found through some search engine or on the Internet somewhere.

We all want safe, secure, private data, but we also want safe and secure communities. And we can have both. I really do believe that.

We tend to assume that data is either private or public, either owned by one person or shared by many. In fact there's more to it than that, above and beyond the upsetting reality that private data is now anything but.

Most laws that we make to protect people from guns are usually ignored by the criminals and obeyed by the law-abiding people. And so I think that if you had better data, there'd be no one more in favor of it than law abiding gun owners because they don't want to be smeared and lumped in with the criminals who use guns.

The pace of digitizing life has been increasing exponentially.

I usually just try to do whatever's on the page because I've done research before - including a lot of analysis - but you end up with conflicting data. To me, the script is king.

You can get caught up in the game, just like everybody else in the park. But I can't play favorites and hope this guy gets a hit or that guy gets an out. I have to make decisions based on data and common sense. I have to manage every game to win.

Warby Parker is a data-driven company.

It's important for market participants to have a sense of how we think about the economy and the appropriate path of policy, to look at incoming data, and to form their own judgments as to whether or not changes in policy would be appropriate.

There's a lot of hand-wringing going on about the death of journalism and particularly the death of investigative journalism. What I see is that there is more need than ever to have experienced information processors - people who can look through this mass of data.

If the Numerai fund performs well, that should lead to more assets under management, which should lead to more revenue for Numerai's general partner, which should lead to higher payouts for the data scientists, which implies a higher Numeraire valuation.

But therein lies the logician's trap: past data from real life constitute a sequence of events rather than a set of independent observations, which is what the laws of probability demand.

If you can follow only one bit of data, follow the earnings—assuming the company in question has earnings. As you'll see in this text, I subscribe to the crusty notion that sooner or later earnings make or break an investment in equities. What the stock price does today, tomorrow, or next week is only a distraction.

Real cognitive science, however, is necessarily based on experimental investigation of actual humans or animals. We will leave that for other books, as we assume the reader has only a computer for experimentation.

And, if the "programming" (our beliefs) is flawed or the data is incorrect, then false conclusions will show up in place of what's true. Perceptions will be biased, but will appear to simply reflect reality.

Data that comes subliminally and is acted upon will look like luck or inspiration.

Could your data be wrong?