Data Quotes
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The public trusts big data way too much.
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It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.
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We need a data network that can easily carry voice, instead of what we have today, a voice network struggling to carry data.
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One of the best things data can enable us to do is to ask questions we didn't know to ask.
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Don't measure anything unless the data helps you make a better decision or change your actions. If you're not prepared to change your diet or your workouts, don't get on the scale.
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Data is not useful until it becomes information.
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We must be careful not to confuse data with the abstractions we use to analyse them.
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Uncontrolled access to data, with no audit trail of activity and no oversight would be going too far. This applies to both commercial and government use of data about people.
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Even the best data security systems can't protect private taxpayer information from entrepreneurial foreign businesses than can make huge profits selling U.S. taxpayer information.
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Data is not intelligence.
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If someone's criticism is completely unfounded on data, then I don't want to hear it. It doesn't hold up to scrutiny.
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This is where the world is going: direct access from anywhere to any type of data, whether it's a small piece of data or a small answer but a long algorithm to create that answer. The user doesn't care about this.
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We cannot be guided by wishful thinking. We need objective data.
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In the future, I think movies are going to be more of data sets that viewers have a hand in controlling - where the narrative originates and what happens to the content.
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We have so much data on the web, almost all of it available for free, that we dive into the the data ocean hoping that magically awesome things will follow. They never do.
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I related to the whole hippie, acid-test confluence of the early Internet. The idea that we should be open and interoperate with our data resonated with me.
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When you read findings like the one above, and see that Jamal doesn’t get the job, it’s easy to shake your head at the few racist hiring managers who’ve tilted the odds against him. But the data we see in this chapter shows racism isn’t a problem of outliers. It is pervasive.
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I think big data companies only like good news. So I think they're just hoping that they don't get sued, essentially.
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If someone does a study which, for statistical reasons, I think is hopelessly underpowered or nonidentified, my best and most useful advice will not be tips on how to calculate p-values better, or how to construct an explanation for some particular data pattern. Rather, my advice will be to start over, to reconsider what you think you already know, maybe to question some prominent work in your subfield, and quite possibly to think a lot harder about measurement, and about the relation of your data to your underlying constructs of interest.
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In God we trust, everybody else bring data to the table.
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I don't use scientific data as a foundation for believing in God - I use it as an enrichment of my knowledge of God.
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I try and wake up relatively early. I listen to some music and check Twitter. I also make sure I weigh myself and check how long I slept. I do that because knowing that data seems better than not knowing it.
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We lisp in numbers, in the U.S. We are deluged by ample, often mysterious statistics. ... Like many in this country, I have come to regard statistics with doubt and merely as a hint of the probable shape of fact.
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You cannot write well without data.
