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What is your favorite statistical quote?

This is community wiki, so please one quote per answer.

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    $\begingroup$ Should this question really be "famous quotes about statistics"? $\endgroup$ – naught101 Nov 3 '12 at 4:29

149 Answers 149

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Statistics' real contribution to society is primarily moral, not technical.

Steve Vardeman and Max Morris

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  • $\begingroup$ I wonder what he meant by that... $\endgroup$ – Tal Galili Sep 19 '11 at 8:47
  • $\begingroup$ Some possible interpretations...1. Thru statistics we teach the importance of empirical testing 2. Thru statistics we teach the importance of assessing the degree of uncertainty inherent in a topic 3. Through statistics we teach the importance of looking for confounding variables, or more generally of expanding our scope as we try to identify causal relationships. Do you have more ideas? $\endgroup$ – rolando2 Sep 19 '11 at 19:55
  • $\begingroup$ I would assume the meaning is that statistics and science are fundamentally about removing your own biases from your assessment of the world, and that such a noble goal could just as well be applied to moral debates. $\endgroup$ – naught101 Mar 28 '12 at 9:49
  • $\begingroup$ My take on meaning: The search for the truth, and that anyone/everyone could be wrong about it. $\endgroup$ – probabilityislogic May 22 '15 at 2:00
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"If you put a buttock on a hot plate and another one on an ice cube, the average is good, but in reality your bottom is in trouble."

Grigore Moisil

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The Median Isn't the Message

--Stephen Jay Gould

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No statistican, but useful for the profession:

The perfect is the enemy of the good - Voltaire

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CauseWeb has a collection of statistics quotations. Many have already been repeated here, but it has plenty that haven't yet been quoted, such as

"The only statistics you can trust are those you falsified yourself."

(Falsely attributed to Sir Winston Churchill.) For the rest, follow the CauseWeb links to Resources->Fun->Quote.

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The mathematician, carried along on his flood of symbols, dealing apparently with purely formal thruths, may still reach results of endless importance for our description of physical universe

-- Karl Pearson

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  • $\begingroup$ I guess I should remove it... poor Karl Pearson, one of the inventor of hypothesis testing not understood by the 21st century ... I would vote up if I could, but it's me that put it here :) $\endgroup$ – robin girard Jul 27 '10 at 18:31
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"Winwood Reade is good upon the subject. He remarks that, while the individual man is an insoluble puzzle, in the aggregate he becomes a mathematical certainty. You can, for example, never foretell what any one man will do, but you can say with precision what an average number will be up to. Individuals vary, but percentages remain constant. So says the statistician".

(Sherlock Holmes speaking to Dr. Watson in Arthur Conan Doyle's "The Sign of the Four")

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Good statistics involves principled argument that conveys an interesting and credible point.

-- Robert P. Abelson, (1995) "Statistics as Principled Argument"

We left in our mathematical model a gap for the exercise of a more intuitive process of personal judgement

-- Egon Pearson, quoted in Abelson (1995).

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A table without stars is like champagne without bubbles! - David Giles

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Data analysis is simply a dialogue with the data

--Stephen F. Gull, 1994

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"The statistician cannot evade the responsibility for understanding the processes he applies or recommends." -- Sir Ronald A. Fisher in The Design of Experiments (1935)

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  • $\begingroup$ True, but the data scientist can. Understanding? We don't need no stinking understanding. $\endgroup$ – Mark L. Stone Jan 28 '16 at 21:28
  • $\begingroup$ Without understanding, no real progress can be made. $\endgroup$ – StatsStudent Jan 29 '16 at 5:49
  • $\begingroup$ Duplicate of an answer above. $\endgroup$ – Kenny LJ Jul 7 '16 at 4:33
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Not yet famous, but could become so.

"If a problem can not be tackled nonparametrically, it is dangerous to tackle it parametrically. But on the other hand, if it can be tackled nonparametrically, it would be better to tackle it parametrically." -- Sir David Cox

Statistics - past, present and future, Royal Statistical Society 180th Anniversary Lecture near 16:27.

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Do not make things easy for yourself by speaking or thinking of data as if they were different from what they are; and do not go off from facing data as they are, to amuse your imagination by wishing they were different from what they are. Such wishing is pure waste of nerve force, weakens your intellectual power, and gets you into habits of mental confusion.

--Mary Everest Boole

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It's not really about statistics, but I think it applies to statistics:

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.

Arthur Conan Doyle

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    $\begingroup$ @user956 - I would disagree with this. Neither theory nor experiment are "in charge" of the other - they work together. Sometimes the theory leads to an experiment, we have an untested hypothesis we want to confirm or deny with some data. $\endgroup$ – probabilityislogic Apr 3 '11 at 1:20
  • $\begingroup$ Some times data leads to an over fitted model, too. $\endgroup$ – naught101 Mar 28 '12 at 9:38
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Check out "Statistician's Blues" by Todd Snider who is an alternative-country singer-songwriter. Warning, if you are sensitive to "bad" words, don't listen to the song. If you have a good or perhaps twisted sense of humor you will enjoy.

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We're looking for long answers that provide some explanation and context. Don't just give a one-line answer; explain why your answer is right, ideally with citations. Answers that don't include explanations may be removed.

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Not really about statistics, but works perfectly:

"Science is built up of facts, as a house is with stones. But a collection of facts is no more a science than a heap of stones is a house." (Henri Poincaré)

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[T]he p-value is the probability of obtaining data at least as extreme as the ones observed, if the null hypothesis is true. This is a world apart from saying that it is the probability of the null hypothesis being true, given that you observed that extreme data! Beware! If your ability on the long jump puts you in the 99.99% percentile, that does not mean that you are a kangaroo, and neither can one infer that the probability that you belong to the human race is 0.01%. - Tomasso Dorigo

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An argument over the meaning of words is a matter of law, an argument grounded in empirical data and quantitative estimates is an argument about science.

~ Razib Khan (though he is not a statistician or famous)

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A variation on the Fisher quotation given here is

Hiring a statistician after the data have been collected is like hiring a physician when your patient is in the morgue. He may be able to tell you what went wrong, but he is unlikely to be able to fix it.

But I heard this attributed to Box, not Fisher.

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  • $\begingroup$ Edited to disambiguate 'above.' $\endgroup$ – Larry Wang Aug 3 '10 at 20:10
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Perhaps not overly famous among statisticians but reduced-form econometricians will know it well:

If you can't see the causal relation of interest in the reduced form, it's probably not there.

Angrist and Krueger (2001)

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Statistics without science is incomplete, science without statistics is imperfect.

K.V. Mardia

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Many folks know only enough statistics to be dangerous. From Statistics for Dummies II - Deborah Rumsey

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A witty statesman said, you might prove anything by figures.

~ Thomas Carlyle, Chartism (1839) ch. 2

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    $\begingroup$ an enlightened customer said "try proving anything without them..." :) $\endgroup$ – probabilityislogic May 28 '11 at 13:50
  • $\begingroup$ @probabilityislogic "cogito ergo sum"? :P $\endgroup$ – naught101 Mar 28 '12 at 9:43
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In his Fisher lecture, Box defines mathematistry as

the development of theory for theory's sake, which, since it seldom touches down with practice, has a tendency to redefine the problem rather than solve it. Typically, there has once been a statistical problem with scientific relevance but this has long since been lost sight of.

He also defines cookbookery as

The tendency to force all problems into the molds of one or two routine techniques, insufficient thought being given to the real objectives of the investigation or to the relevance of the assumptions implied by the imposed methods.

References

Box, G. E. P. 1976. Science and Statistics. Journal of the American Statistical Association, 71: 791–799.

Roderick J. Little (2013) In Praise of Simplicity not Mathematistry! Ten Simple Powerful Ideas for the Statistical Scientist, Journal of the American Statistical Association

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...Statistics used as a catalyst to engineering creation will, I believe, always result in the fastest and most economical progress.

--George Box 1992

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The Government are very keen on amassing statistics—they collect them, add them, raise them to the nth power, take the cube root and prepare wonderful diagrams. But what you must never forget is that every one of those figures comes in the first instance from the chowkidar (village watchman), who just puts down what he damn pleases [link].

-- Josiah Stamp, recounting a story from Harold Cox, Some Economic Factors in Modern Life (1929), p. 258.

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An intense preoccupation with the latest minutiae and indifference to the social and intellectual forces of tradition and revolutionary change, combine to produce the Mandarinism that some would now say already characterizes academic statistical theory and is most likely to describe its immediate future.

The statisticians of the past came into the subject from other fields - astronomy, pure mathematics, genetics, agronomy, economics etc. — and created their statistical methodology with a background of training for a specific scientific discipline and a feeling for its current needs. So for the future I recommend we work on interesting problems and avoid dogmatism.

-- Herbert Robbins in "Wither Mathematical Statistics?" as quoted by Peter J. Huber in "Speculations on the Path of Statistics".

Robbins, Herbert. "Wither Mathematical Statistics?" Advances in Applied Probability 7 (1975): 116-21. doi:10.2307/1426316.

Brillinger, D. R., L. T. Fernholz, and S. Morgenthaler, eds. The Practice of Data Analysis: Essays in Honor of John W. Tukey. Princeton University Press, 1997. http://www.jstor.org/stable/j.ctt7zthdd.

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  • $\begingroup$ Wither? That's a harsh assessment of the future of mathematical statistics! $\endgroup$ – Dilip Sarwate Mar 22 '18 at 4:09
  • $\begingroup$ This was written in the spirit of Tukey's "Future of Data Analysis" and Peter Huber was invoking others like Robbins who have said similar things. $\endgroup$ – user3303 Mar 22 '18 at 4:32
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This is a two part quote I've heard a few times:

Machine learning is statistics minus any checking of models and assumptions.

Brian D. Ripley

In that case, maybe we should get rid of checking of models and assumptions more often. Then maybe we'd be able to solve some of the problems that the machine learning people can solve but we can't!

Andrew Gelman

There's some irony in the sides taken by these two statisticians in this conversation. That is, some of Brian Ripley's early work was on Neural Networks, which is an extremely popular topic in Machine Learning, where as Andrew Gelman is exclusively known for Bayesian statistics in which he advocates taking one's statistical models very seriously.

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Let’s be clear: the work of science has nothing whatever to do with consensus. Consensus is the business of politics. Science, on the contrary, requires only one investigator who happens to be right, which means that he or she has results that are verifiable by reference to the real world. In science consensus is irrelevant. What is relevant is reproducible results. The greatest scientists in history are great precisely because they broke with the consensus.

There is no such thing as consensus science. If it’s consensus, it isn’t science. If it’s science, it isn’t consensus. Period.

Michael Crichton

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    $\begingroup$ That's because consensus is a practical means of establishing the validity of a scientific position. Sound science cannot be conducted by a single person. It requires peer review. Having achieved a popular consensus implies that the science in question has passed peer review. Whereas a theory or position held by a lone individual and opposed by the rest of the scientific community is likely to have failed the process of peer review. If you're not an expert in field X, then statistically you're better off following popular consensus within field X. $\endgroup$ – Lèse majesté Aug 5 '10 at 0:50
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    $\begingroup$ To elaborate: if you're a politician who has no background in field X, then it is far better that you simply consult the consensus of experts in that field of research than to misinterpret the data first-hand. What is problematic is when lay persons disregard overwhelming scientific consensus for data misrepresented to them by a fringe minority--especially when these lay persons are in charge of policy decisions. It's much easier to mislead a handful of politicians than thousands of expert researchers... $\endgroup$ – Lèse majesté Aug 5 '10 at 1:02
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    $\begingroup$ -1 This is an intellectually dishonest quote from a bad novelist, playing up a popular romantic myth of scientists as Rand-esque revolutionary loners in order to pander to anti-science crankery. Moreover, it has nothing whatsoever to do with statistics. What is it even doing in this list? $\endgroup$ – walkytalky Aug 7 '10 at 14:34
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    $\begingroup$ (-1) also (concur with @walkytalky), and @lese - note that "...consult the consensus of experts in that field of research than to misinterpret the data first-hand" can be cheekily rebutted by "I consult the experts so that I can mis-interpret the data second-hand". $\endgroup$ – probabilityislogic Jan 30 '11 at 10:38
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    $\begingroup$ What's the scientific consensus on the scientific validity of this quote? $\endgroup$ – Mark L. Stone Aug 4 '15 at 15:49

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