What is your favorite statistical quote?
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A bit obscure this one, but a great quote about subjective probability:
... There is no way, however, in which the individual can avoid the burden of responsibility for his own evaluations. The key cannot be found that will unlock the enchanted garden wherein, among the fairy-rings and the shrubs of magic wands, beneath the trees laden with monads and noumena, blossom forth the flowers of probabilitas realis. With these fabulous blooms safely in our button-holes we would be spared the necessity of forming opinions, and the heavy loads we bear upon our necks would be rendered superflous once and for all.
Bruno de Finetti, Theory of Probability, Vol 2
A man who ‘rejects’ a hypothesis provisionally, as a matter of habitual practice, when the significance is at the 1% level or higher, will certainly be mistaken in not more than 1% of such decisions. For when the hypothesis is correct he will be mistaken in just 1% of these cases, and when it is incorrect he will never be mistaken in rejection. [...] However, the calculation is absurdly academic, for in fact no scientific worker has a fixed level of significance at which from year to year, and in all circumstances, he rejects hypotheses; he rather gives his mind to each particular case in the light of his evidence and his ideas.
-- Sir Ronald A. Fisher, from Statistical Methods and Scientific Inference (1956)
Another quote as a commentary: "This passage clearly is intended as a criticism of Neyman and Pearson, although again their names are not mentioned. However, these authors never recommended a fixed level of significance that would be used in all cases. [...] Thus Fisher rather incongruously appears to be attacking his own past position rather than that of Neyman and Pearson" (from Fisher, Neyman, and the Creation of Classical Statistics by Erich Lehmann, section 4.5).
These days the statistician is often asked such questions as "Are you a Bayesian?" "Are you a frequentist?" "Are you a data analyst?" "Are you a designer of experiments?". I will argue that the appropriate answer to ALL of these questions can be (and preferably should be) "yes", and that we can see why this is so if we consider the scientific context for what statisticians do.
[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.
-- Sir Francis Galton
Context: An F-test is often a poor way to justify pooling, because F-test is not robust against non-normality.
"To make a preliminary test on variances is rather like putting to sea in a rowing boat to find out whether conditions are sufficiently calm for an ocean liner to leave port." (G.E.P. Box, "Non-normality and tests on variances",
Source: Biometrika, 40 (1953), pp 318-335, quote on page 333; via from Moore & McCabe.
(props to Tim Hesterberg: https://stat.ethz.ch/pipermail/r-help/2008-February/154856.html)
Uncertainty is a personal matter; it is not the uncertainty but your uncertainty. (Dennis Lindley)
Reference: Dennis Victor Lindley (2006), Understanding Uncertainty, Wiley-Interscience, p. 1.
"He who loves practice without theory is like the sailor who boards ship without a rudder and compass and never knows where he may be cast." - Leonardo da Vinci, 1452-1519
"I cannot conceal the fact here that in the [application of probability theory], I foresee many things happening which can cause one to be badly mistaken if he does not proceed cautiously.",
Bernoulli (1713) (via ET Jaynes)
"A statistician is someone who knows what to assume to be Gaussian"
Dikran Marsupial (2009) (not famous yet ;o).
Everybody knows that probability and statistics are the same thing, and statistics is nothing but correlation. Now the correlation is just the cosine of an angle, thus all is trivial.
-- Emil Artin, according to Kai Lai Chung in Elementary probability theory (right, Artin might not been known primarily as a statistician)
The researcher armed with a confidence interval, but deprived of the false respectability of statistical significance, must work harder to convince himself and others of the importance of his findings. This can only be good.
Michael Oakes, Statistical inference: A commentary for the social and behavioural sciences (NY: Wiley, 1986)
We statisticians, as a police of science (a label some dislike but I am proud of...), have the fundamental duty of helping others to engage in statistical thinking as a necessary step of scientific inquiry and evidence-based policy formulation. In order to truly fulfill this task, we must constantly firm up and deepen our own foundation, and resist the temptation of competing for “methods and results” without pondering deeply whether we are helping others or actually harming them by effectively encouraging more false discoveries or misguided policies. Otherwise, we indeed can lose our identity, no matter how much we are desired or feared now.
“Statistics is much like a streetlight. Not very enlightening, but nice for supporting you”