Nick Bostrom Quotes

The business of the statistician is to catalyze the scientific learning process.

The type of measure used placed constraints on which statistics can be used.

I love data. I think it's very important to get it right, and I think it's good to question it.

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

Definition of a Statistician: A man who believes figures don't lie, but admits than under analysis some of them won't stand up either.

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

The quality of the data and information of a social science, economics or government study is inversely proportional to its importance and social significance.

Our polling methodology has gotten outdated, and, in fact, it's not really telling us what it needs to be telling us.

Biases and blind spots exist in big data as much as they do in individual perceptions and experiences. Yet there is a problematic belief that bigger data is always better data and that correlation is as good as causation.

Hidden biases in both the collection and analysis stages present considerable risks and are as important to the big-data equation as the numbers themselves.

Big data sets are never complete.

The 'data' (given) of research are not so much given as taken out of a constantly elusive matrix of happenings. We should speak of capta rather than data.

Statistics do not speak for themselves.

Statistics: The only science that enables different experts using the same figures to draw different conclusions.

Statistics has been the handmaid of science, and has poured a flood of light upon the dark questions of famine and pestilence, ignorance and crime, disease and death.

[Statistics are] the only tools by which an opening can be cut through the formidable thicket of difficulties that bars the path of those who pursue the Science of Man.

Random search for data on ... off-chance is hardly scientific. A questionnaire on 'Intellectual Immoralities' was circulated by a well-known institution. 'Intellectual Immorality No. 4' read: 'Generalizing beyond one's data'. [Wilder Dwight] Bancroft asked whether it would not be more correct to word question no. 4 'Not generalizing beyond one's data.

Some people hate the very name of statistics, but I find them full of beauty and interest. Whenever they are not brutalized, but delicately handled by the higher methods, and are warily interpreted, their power of dealing with complicated phenomena is extraordinary. They are the only tools by which an opening can be cut through the formidable thicket of difficulties that bars the path of those who pursue the Science of Man.

Without the knowledge of the true number of the people, as a principle, the whole scope and use of keeping bills of birth and burials is impaired; wherefore by laborious conjectures and calculations to deduce the number of people from the births and burials, may be ingenious, but very preposterous.

I have a theory of statistics: if you can double them or halve them and they still work, they are really good statistics.

The TV scientist who mutters sadly, "The experiment is a failure; we have failed to achieve what we had hoped for," is suffering mainly from a bad script writer. An experiment is never a failure solely because it fails to achieve predicted results. An experiment is a failure only when it also fails adequately to test the hypothesis in question, when the data it produces don't prove anything one way or another.

Scientists construct facts by constantly making decisions about what they will consider significant, what experiments they should pursue, and how they will describe their observations.

as an economics professor I am by nature inclined to the view that the truth isn't out there, it's in here - that usually you learn a lot more by thinking really hard about the data than you do by sniffing around for supposedly inside information.

... the actual and physical conduct of an experiment must govern the statistical procedure of its interpretation.

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.