Quotes About Data

Specifically, the more data we have in our sample, the more "degrees of freedom" we have when determining the appropriate distribution against which to evaluate our results.

Statistics cannot prove anything with certainty. Instead, the power of statistical inference derives from observing some pattern or outcome and then using probability to determine the most likely explanation for that outcome.

For large samples, we can assume that the standard deviation of the sample is reasonably close to the standard deviation of the population.*

The overall lesson of this chapter is that statistical malfeasance has very little to do with bad math.

New York Times has noted, "Data is merely the raw material of knowledge.

Second, even if the underlying data could accurately predict future risk, the 99 percent assurance offered by the VaR model was dangerously useless, because it's the 1 percent that is going to really mess you up.

Statistical analysis is the detective work that crafts the raw data into some meaningful conclusion.

In the world of information science, the tricky question of where to put things is known as the "ontology problem.

The overwhelming trend of our age is to take products that were once delivered as physical goods, find ways to turn them into data, and stream them into your home.

So everything is an algorithm now. And just as every Google search uses its algorithms to produce a different result for each person searching, so can algorithms customize products for their consumers. For

Because, like Sherlock says to Dr. Watson, 'it is a capital mistake to theorize before one has data.

There is no reliable data on the number of military-style assault weapons in private hands, but the working estimate is about 1.5 million.

My mission is to bring the power of big data insights and analytics to every company.

I think there's a lot of misunderstanding about genetic information and what you can and cannot learn.

On mobile, what are the core apps? It's basically messaging, mapping and review data.

Cambridge Analytica never took receipt of any data or undertook any modelling on behalf of Leave.E.U.

The amount of control you have over somebody if you can monitor internet activity is amazing.

Health isn't only what was genetically given to you; it's also about your environment and what you do on a day-to-day basis. The more we understand that, the more we can personalize it, and really, it requires us to have more and more data about the individual.

The word "see" is often used as a direct synonym for "understand"—"I see what you mean." Yet sometimes we see but we don't understand; worse, we see, then "understand" something that isn't true at all. Done well, a picture of data is worth the proverbial thousand words.

we should ask who is missing from the data we're being shown, and whether our conclusions might differ if they were included.

Testing a hypothesis using the numbers that helped form the hypothesis in the first place is not OK.15

Hearing the anecdote, it's easy to assume that Target's algorithms are infallible—that everybody receiving coupons for onesies and wet wipes is pregnant. But nobody ever claimed that it was true.

But we can and should remember to ask who or what might be missing from the data we're being told about.

The United Nations, for example, has embraced a series of ambitious "Sustainable Development Goals" for 2030. But development experts are starting to call attention to a problem: we often don't have the data we would need to figure out whether those goals have been met.