When Humpty Dumpty uses a word, it means just what he chooses it to mean, neither more nor less. To people not conversant in a technical specialty, it seems that all the experts are Humpty Dumptys. Statistics is no exception.
If you’re a beginner at data analysis, it will seem like there is a superabundance of esoteric statistical slang. You’ll hear it even from friendly statisticians. It gets worse when you start reading websites, books, and worst of all, journal articles. If you want to see what I mean, read some of the article titles in the Journal of the American Statistical Association (at http://pubs.amstat.org/loi/jasa). The statisticians who write those obfuscatory tracts believe they are writing to people who know as much as they do. This seems odd given that those authors are supposed to be the experts in what they are writing about. Even other statisticians can’t decipher some of those articles without spending time with the reference books. So don’t feel like you’re alone in a foreign country. We stand befuddled together.
To simplify statistical jargon, think of three distinctions—statistical concepts named after someone, special words created to convey a special meaning, and common words and phrases with alternative meanings. We’ll leave the acronyms out of it for now.
Statistical procedures, especially statistical tests, are often modified to accommodate some special circumstance or to have some desirable property. When this occurs, the new procedure is commonly named after the originators. Thus, there are statistical tests named after Dixon, Tukey, Wilcoxon, Scheffe, Kolmogorov, Fisher, Levene, Hotelling, Dunnett, and Bonferroni. And those are just some well-known ones. Dig into the literature, and you’ll find scores more.
It’s not just tests that get named. Bayesian statistics is a branch of statistics based on Bayes Theorem formulated in the 1700s by Reverend Thomas Bayes. Kriging, the interpolation algorithm of geostatistics was named after Daniel Krige, a South African mining engineer, who pioneered the field in the 1950s. The Normal distribution is also called the Gaussian distribution after Carl Friedrich Gauss, who introduced it in 1809, and the Laplacian distribution after Pierre-Simon Laplace who showed that the distribution was the basis for the central limit theorem in 1810. There are also theoretical frequency distributions named after Benford, Weibull, Rayleigh, Cauchy, Poisson, and Bernoulli.
If someone mentions a named distribution , test, or other statistical procedure, don’t panic. Nobody knows everything. Just ask what the distribution or procedure is supposed to do. If you took an introductory course in statistics and know about probability, the Normal distribution, and hypothesis testing, you’re in great shape for understanding most of the named stat terms you might run into. This type of statistical jargon could be much worse. When biologists name something after someone, they do it in Latin.
Some statistical jargon might just as well be a foreign language because the words have no common meaning in the English language outside of statistics (or math). Examples of such words include: kurtosis, leptokurtic, platykurtic, skewness, covariance, autoregressive, variogram, logit, probit, eigenvalue, median, outlier, stationarity, winsorizing, communality, multicollinearity, and my personal favorite, homoscedasticity. If you’re at a bar and you hear any of these words being bandied around, slip quietly out the door and run for your life. Any statistician who uses these words with innocent civilians without explanation either doesn’t understand his or her audience or is a sadist. Dealing with created statistical terms is straightforward; just ask the statistician using them what they mean. Preferably ask in a foreign language just to prove the point.
The most confusing statistical jargon just might be words in most people’s everyday vocabulary that have a very different statistical meaning. For example, when you hear the word mean, your mind has to sort out the word’s connotation. It can signify to intend, as in say what you mean. It can be used to associate, as in spring means flowers. It can refer to resources or methods, as in by any means. It can indicate character, as in she has a mean streak. It can imply exceptional skill, as in he has a mean fastball. And of course, in statistics, mean means average.” If you don’t realize that some words in English have different meanings in statistics, you can get confused very quickly. I’ve had well-meaning report editors change median to medium and nonsignificant to insignificant.
Here are a few more examples:
Don’t feel that you’re alone in the quagmire of statistical jargon. Like dialects of the English language, different statistical specialties have their own jargon and ways of expressing ideas. Data mining, time-series forecasting, quality control, nonlinear modeling, biometrics, econometrics, and geostatistics are all examples of statistical specialties that use terms not used in the other specialties. Imagine a Louisiana Cajun talking to a Pennsylvania Dutch. They both speak dialects of English, but it might as well be Greek.
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A good one is also ‘boot strapping’
Yup, I didn’t think of that one. It makes me think of jack knifing, too.
I’m in the process of blogging on parallel universes: Life Sciences vs. Statistics.
Some favorite stats. jargon of mine:
Heteroskedasticity–It just doesn’t get any better than that.
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I was looking into modeling some data using a multinomial logistic distribution and found this one:
“Irrelevance of Independent Alternatives” or “IIA”.
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