A Quote by Stephen J. Dubner
The data are what matter in economics, and the more ruthlessness that an economist can summon to make sense of the data, the more useful his findings will be.
Related Quotes
If we gather more and more data and establish more and more associations, however, we will not finally find that we know something. We will simply end up having more and more data and larger sets of correlations.
People think 'big data' avoids the problem of discrimination because you are dealing with big data sets, but, in fact, big data is being used for more and more precise forms of discrimination - a form of data redlining.
We are ... led to a somewhat vague distinction between what we may call "hard" data and "soft" data. This distinction is a matter of degree, and must not be pressed; but if not taken too seriously it may help to make the situation clear. I mean by "hard" data those which resist the solvent influence of critical reflection, and by "soft" data those which, under the operation of this process, become to our minds more or less doubtful.
Sense data are much more controversial than qualia, because they are associated with a controversial theory of perception - that one perceives the world by perceiving one's sense-data, or something like that.
scientists ... resist ... making more of the data than the data make of themselves.
Data by itself is not useful. Data is only useful if it can be applied for public benefit.
One of the myths about the Internet of Things is that companies have all the data they need, but their real challenge is making sense of it. In reality, the cost of collecting some kinds of data remains too high, the quality of the data isn't always good enough, and it remains difficult to integrate multiple data sources.
The bigger a data set that you have, the more polls, the more surveys that you have that people undertake, the more accurate your models are going to be. That's just a fact of data science.
With too little data, you won't be able to make any conclusions that you trust. With loads of data you will find relationships that aren't real... Big data isn't about bits, it's about talent.
Disruptive technology is a theory. It says this will happen and this is why; it's a statement of cause and effect. In our teaching we have so exalted the virtues of data-driven decision making that in many ways we condemn managers only to be able to take action after the data is clear and the game is over. In many ways a good theory is more accurate than data. It allows you to see into the future more clearly.
I'm going to say something rather controversial. Big data, as people understand it today, is just a bigger version of small data. Fundamentally, what we're doing with data has not changed; there's just more of it.
The biggest mistake is an over-reliance on data. Managers will say if there are no data they can take no action. However, data only exist about the past. By the time data become conclusive, it is too late to take actions based on those conclusions.
Rule 1. Original data should be presented in a way that will preserve the evidence in the original data for all the predictions assumed to be useful.
In my view, our approach to global warming exemplifies everything that is wrong with our approach to the environment. We are basing our decisions on speculation, not evidence. Proponents are pressing their views with more PR than scientific data. Indeed, we have allowed the whole issue to be politicized-red vs blue, Republican vs Democrat. This is in my view absurd. Data aren't political. Data are data. Politics leads you in the direction of a belief. Data, if you follow them, lead you to truth.
Data!data!data!" he cried impatiently. "I can't make bricks without clay.
Machine learning and artificial intelligence applications are proving to be especially useful in the ocean, where there is both so much data - big surfaces, deep depths - and not enough data - it is too expensive and not necessarily useful to collect samples of any kind from all over.
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