Chance News 98
Quotations
"In statistics it's enough for our results to be cool. In psychology they're supposed to be correct. In economics they're supposed to be correct and consistent with your ideology."
Some other selections:
- “God created the world in 7 days and we haven’t seen much of him since.” (God draws θ from an urn and then is out of the picture)
- “People don’t go around introducing you to their ex-wives.” (why model improvement doesn’t make it into papers)
Submitted by Paul Alper
"In our lust for measurement, we frequently measure that which we can rather than that which we wish to measure...and forget that there is a difference."
“Statistics and Experimentation”, AP Statistics Reading, June 16, 2011
"'We value what we measure rather than measuring what we value' is an expression commonly heard in education circles these days."
Submitted by Margaret Cibes
From What the Numbers Say, by Niederman and Boyum, 2003:
“Unfortunately, Americans seem much better at producing numbers than making sense of them.” [p. 1]
“Distrusting numbers is not the same as disregarding them.” [p. 11]
“[G]ive more credence to a finding if there is good reason to believe it for reasons other than its statistical significance.” [p. 219]
“Elizabeth Taylor’s Law (the marital version of Pareto’s Law) reminds us that a small fraction of the population accounts for a disproportionate share of divorces.” [p. 17]
“[M]ost people not only lack a notation for dealing with small numbers, they also lack a vocabulary. If you show a man the number 3,500,000 and ask what it is, he will say, with little if any hesitation, ‘three and a half million’ … or ‘three million five hundred thousand.’ But if you show him 0.00000029, he will probably respond, ‘point oh oh oh oh oh oh two nine,’ slowly …. [J]ust imagine a politician describing the defense budget as ‘three six nine oh oh oh oh oh oh oh oh oh dollars.” [pp. 117-118]
Submitted by Margaret Cibes
Forsooth
A Google search for “apophenia” yielded the following:
(a) “the experience of seeing patterns or connections in random or meaningless data.”
(b) “an example of a Type I error … – the identification of false patterns in data.”
(c) “heavily documented as a source of rationale behind gambling, with gamblers imagining they see patterns in the occurrence of numbers in lotteries, roulette wheels, and even cards.”
(d) “an open statistical library for working with data sets and statistical models. It provides functions on the same level as those of the typical stats package … but gives the user more flexibility to be creative in model-building.” [emphasis added]
Submitted by Margaret Cibes