Don Zimmerman's

** Statistical Devil's
Dictionary**

with apologies to Ambrose Bierce

**Mean**—a statistic used by researchers who want nothing to do
with the bulk of the data.

**Variance**—a measure of the degree to which investigators with
a fetish about the mean ignore uninteresting and possibly important outlying
measures.

**Median**—a complex statistic favored by computer programmers
who enjoy nontrivial programming challenges.

**Standard deviation**—a measure devised by clinical
psychologists to describe those quantitatively-oriented colleagues regarded as
deviant and obsessive about statistics.

**Percentile**—a measure of the degree to which well-motivated
examinees, after taking a test, can justifiably look down upon their lesser
peers.

**Decile**—a percentile for people who have difficulty keeping
more than ten things in working memory.

**Normal distribution**—a distribution free from abnormalities,
inconsistencies, distortions, or features of any kind that could compromise
efficient data analysis.

**Probability of a Type I error**—the probability that an
investigator will announce an important discovery to the world.

**Probability of a Type II error**—the probability that an
investigator will continue performing experiments until a Type I error
occurs.

**Nonparametric test**—a test devised by the Albanian
statistician Imrovic Nonparametric in order to provoke the Englishman Ronald
Fisher.

**ANOVA**—(also spelled "A Nova")—the feminine counterpart
of "A Novus" (also spelled "A Novice")—A female statistician new to the
discipline.

**Population**—the grand total of every last bugger and his
brother who possibly could be measured.

**Sample**—a rag-tag, bob-tailed bunch of atypical misfits who
have volunteered to participate in an experiment.

**Null hypothesis**—an assumption about the nonexistence of even
the slightest difference between two quantities, made so that discovery of a
slight one will be an occasion for rejoicing.

**Alternative hypothesis**—an hypothesis waiting and ready to be
presented in case the manuscript with the first hypothesis is rejected for
publication.

**Sample size**—the number of observations needed to make a
study appear, at least superficially, to be well-designed.

**Skewness**—a noticeable imbalance in the shape of a
distribution resulting from failure to obtain data that conforms to
normality.

**Kurtosis**—a disease that causes populations to accumulate too
few central exemplars and too many outlying ones (or vice versa) and that in
addition often produces headaches among statisticians.

**Control group**—the unsung, forgotten participants in an
experiment who have to get along with old methods or sugar pills and are never
allowed to get in on the real action.

**Bootstrap**—a tool employed by young, modern statisticians to
intimidate older statisticians who are more set in their ways and lack
familiarity with recent computer-intensive methods.

**Jackknife**—a truly vicious intimidation tool, occasionally
wielded by investigators who fail to get results with the bootstrap.

**Reliability**—a measure of the extent to which repetition of
an experiment results in the same errors that occurred the first time.

**Validity**—a measure of the extent to which the results of a
procedure conform to the preconceptions, beliefs, and expectations of the
theorist planning and executing the procedure.

**Factor analysis**—an elegant mathematical technique for
replacing a large number of interesting variables with a small number of
hypothetical, unobservable, and uninteresting ones.

**Standard error**—the commonplace and predictable extent to
which authors of statistics textbooks copy and perpetuate the discrediited
beliefs and mistakes of earlier authors.

This dictionary is still **under
construction**