Quotes About Data

In modern terms we would say that the more strongly correlated the measurements, the less information, in Shannon's precise sense, a Bertillon card conveys.

Assessing the scale of the p-hacking problem is not so easy—

That's how the Law of Large Numbers works: not by balancing out what's already happened, but by diluting what's already happened with new data, until the past is so proportionally negligible that it can safely be forgotten.

scientists and statisticians have already been worrying about them for quite some time.

Often people think of developments in computation as arising when we make our computers more blazingly fast, so they can compute more stuff , bigger data . It's actually just as important to prune away big parts of the data that aren't relevant to the problem at hand! The fastest computation is the one you don't do.

But the significance test that scientists use doesn't measure importance.

Statistics is not an exact science. It is an investigative technique.

Today, we could maybe store the information contained in one of your toenails if we used every computer in existence.

I must take a moment and give an especially hearty cheer to anyone who champions structured data, richer data, data that gives us more handles to grab on to the things we are describing and thus enables us to serve them up in different ways within different contexts for our different constituent groups. However sophisticated the relevancy algorithms and myriad features of any discovery product might be, at the most basic level, these systems rely on the data we feed them.

Core Mechanics The core mechanics consist of the data and the algorithms that precisely define the game's rules and internal operations.

Just a maintenance tech here, folks, heading home after a long night of rebooting routers. That's all. I am definitely not an indent making a daring escape with ten zettabytes of stolen company data in his pocket. Nosiree.

All sensory input received by their brain is digitized and stored as a .oni (dot-oh-en-eye) file on an external data drive attached to their headset.

That which is not measurable is not science. That which is not physics is stamp collecting.

Richard had conducted a series of DMILS studies. While my data resulted in significant deviations between the treatment and control conditions, Wiseman consistently found chance results in his studies.

Smith v. Maryland, it held that police can obtain a list of phone numbers that a person calls, or receives calls from, without needing to get a warrant or have probable cause.

Dana followed a specific structure: Set the stage. Gather data. Generate insights. Decide what to do. Close the retrospective.

Your job here in this Workshop is to assimilate data that you have been collecting from your real-life experiences (as you have been interacting with others and moving in and out of your physi-cal environment). Your work here is to bring the data together in a sort of picture of yourself, one that satisfies and pleases you.

if you have mountains of complex data that you need to decipher, then you want the two or three best number crunchers that you can find, regardless of whether they can simultaneously walk and chew gum. On the other hand, if you are managing a big reorganization during which many sensitive decisions will have to be made, you would prefer to have someone on your team with good people skills and experience in implementing change.

In another case, a McKinsey team went in to evaluate expansion opportunities for a division of a manufacturing company. After a few weeks of gathering and analyzing data, the team realized that what the division needed was not expansion; it was closure or sell-off.

The McKinsey problem-solving process begins with research. Before a team can construct an initial hypothesis, before it can disaggregate a problem into its components and uncover the key drivers, it has to have information.

At the start of a McKinsey-ite's career, most of his time is spent gathering data, whether from one of the Firm's libraries, from McKinsey's many databases, or from the Internet. Gathering, filtering, and analyzing data is the skill exercised most by new associates. As a result, McKinsey-ites have learned a number of tricks for jump-starting their research. You can use these tricks to find the answers to your business problem too.

Each of us, actually every animal, is a data scientist. We collect data from our sensors, and then we process the data to get abstract rules to perceive our environment and control our actions in that environment to minimize pain and/or maximize pleasure. We have memory to store those rules in our brains, and then we recall and use them when needed. Learning is lifelong; we forget rules when they no longer apply or revise them when the environment changes. Learning

Now data is cheap and we are all kings and queens of our little online fiefdoms. A baby born to gadget-loving parents today can generate more data in her first month than it took for Homer to narrate the complete adventures of Odysseus.

What we lack in knowledge, we make up for in data. We