Ben Zobrist Quotes

I've got a lot of stats. I did a lot of stuff.

Numbers can't speak for themselves, and data sets - no matter their scale - are still objects of human design.

You'll never hear me talk about stats.

Evolving technologies that allow economists to gather new types of data and to manipulate millions of data points are just one factor among several that are likely to transform the field in coming years.

There's something that happens with the collection of a large amount of data when it's dumped into an Excel spreadsheet or put into a pie chart. You run the risk of completely missing what it's about.

The Government are very keen on amassing statistics - they collect them, add them, raise them to the nth power, take the cube root and prepare wonderful diagrams.

We have entered an era where the kinds of data and analyses that people run have gotten incredibly complicated. So you have researchers using sophisticated software programs that they probably don't understand but are generally accepted and everyone [in the field] uses.

I'm not too worried about stats and stuff like that.

A lot of people seem to think that data science is just a process of adding up a bunch of data and looking at the results, but that's actually not at all what the process is.

There's been the emergence of a philosophy that big data is all you need. We would suggest that, actually, numbers don't speak for themselves.

Data drives success. That is how we began our success with eSpeed. It was always based on the data.

The next darwin is more likely to be a data wonk than a naturalist wandering through an exotic landscape.

Data, I think, is one of the most powerful mechanisms for telling stories. I take a huge pile of data and I try to get it to tell stories.

The statistics might have a Eurosceptic cast, but they are not exactly a fun read. Few of us want to wade through ONS graphs or European Commission tables.

There is a rampant tendency in any industry where someone is trying to sell something with a bunch of data, where they cherry pick a little bit... bias a little bit. This becomes quite easy when there is an enormous amount of data to cherry pick from.

There is a reasonable concern that posting raw data can be misleading for those who are not trained in its use and who do not have the broader perspective within which to place a particular piece of data that is raw.

Big data has been used by human beings for a long time - just in bricks-and-mortar applications. Insurance and standardized tests are both examples of big data from before the Internet.

A painstaking course in qualitative and quantitative analysis by John Wing gave me an appreciation of the need for, and beauty of, accurate measurement.

All of the studies we do in my group are quantified.

There's a lot of power in executing data - generating data and executing data.

No observational problem will not be solved by more data.

On average, people should be more skeptical when they see numbers. They should be more willing to play around with the data themselves.

Go out and collect data and, instead of having the answer, just look at the data and see if the data tells you anything. When we're allowed to do this with companies, it's almost magical.

Rob Engle and I are concerned with extracting useful implications from economic data, and so the properties of the data are of particular importance.

Good data is hard to find. Hard data is good to find.