What is your favorite statistical quote?
This is community wiki, so please one quote per answer.
158 Answers
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“Statistics is much like a streetlight. Not very enlightening, but nice for supporting you”
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4$\begingroup$ Wasn't this based on an older variation? $\endgroup$ Commented May 27, 2011 at 15:26
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Statistics' real contribution to society is primarily moral, not technical.
Steve Vardeman and Max Morris
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1$\begingroup$ I wonder what he meant by that... $\endgroup$ Commented Sep 19, 2011 at 8:47
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$\begingroup$ 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? $\endgroup$– rolando2Commented Sep 19, 2011 at 19:55
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$\begingroup$ 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. $\endgroup$ Commented Mar 28, 2012 at 9:49
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$\begingroup$ My take on meaning: The search for the truth, and that anyone/everyone could be wrong about it. $\endgroup$ Commented May 22, 2015 at 2:00
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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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"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."
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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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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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[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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$\begingroup$ +1 For those who have not read it, I highly recommend Gould's 1985 The Median Isn't the Message essay in Discover. $\endgroup$– jthetzelCommented Jun 20, 2012 at 16:58
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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 :) $\endgroup$ Commented Jul 27, 2010 at 18:31
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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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"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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Data analysis is simply a dialogue with the data
--Stephen F. Gull, 1994
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Many folks know only enough statistics to be dangerous.
From Statistics for Dummies II - Deborah Rumsey
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Most of you will be familiar with the George Box quote 'All models are wrong, but some are useful'. It's presently the top answer in this thread as well.
But he stated this in different ways over the years, and the very earliest version has a different (and I think more interesting) flavour. I love the last line here in particular:
2.3 Parsimony
Since all models are wrong the scientist cannot obtain a "correct" one by excessive elaboration. On the contrary following William of Occam he should seek an economical description of natural phenomena. Just as the ability to devise simple but evocative models is the signature of the great scientist so overelaboration and overparameterization is often the mark of mediocrity.
2.4 Worrying Selectively
Since all models are wrong the scientist must be alert to what is importantly wrong. It is inappropriate to be concerned about safety from mice when there are tigers abroad.
Box, G. E. P. (1976). Science and statistics. Journal of the American Statistical Association, 71(356), 791-799. doi:10.1080/01621459.1976.10480949
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3$\begingroup$ @NickCox Serves me right for using an absolute in a stats forum. $\endgroup$– mktCommented Aug 8 at 11:13
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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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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.' $\endgroup$ Commented Aug 3, 2010 at 20:10
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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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5$\begingroup$ @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. $\endgroup$ Commented Apr 3, 2011 at 1:20
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$\begingroup$ Some times data leads to an over fitted model, too. $\endgroup$ Commented Mar 28, 2012 at 9:38
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Attributed by Andrew Gelman (2008) to Efron (1986 [I can't find the original reference]):
Bayesian theory requires a great deal of thought about the given situation to apply sensibly, and recommending that scientists use Bayes’ theorem is like giving the neighborhood kids the key to your F-16.
Gelman, Andrew. 2008. “Objections to Bayesian Statistics.” Bayesian Analysis 3: 445–50. https://doi.org/10.1214/08-BA318 (and here)
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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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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.
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Statistics without science is incomplete, science without statistics is imperfect.
K.V. Mardia
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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! $\endgroup$ Commented Mar 22, 2018 at 4:09
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$\begingroup$ 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. $\endgroup$– user3303Commented Mar 22, 2018 at 4:32
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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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A statistical procedure is not an automatic, mechanical truth-generating machine
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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
Link (Ripley is quoted as making the statement at the useR forum, Gelman responds to the quote on his blog).
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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$\begingroup$ @usεr11852: what part about it being a rant is a problem in my quote? And I think that exactly the point: for a long time statisticians (btw, I am one) ranted about ML approaches, while these approaches continuously got very positive results on problems statisticians had essentially given up on. $\endgroup$– Cliff ABCommented Jun 1, 2020 at 14:56
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$\begingroup$ @usεr11852: also I updated the post to include a link. $\endgroup$– Cliff ABCommented Jun 1, 2020 at 15:04
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$\begingroup$ I dislike/I do not find it useful as a quote. (Obviously I have nothing against your technical answers here or BDR's great achievements as a statistician.) $\endgroup$ Commented Jun 1, 2020 at 15:21
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The first principle is that you must not fool yourself—and you are the easiest person to fool.
Richard Feynman