Nonparametric Statistics for the Behavioral Sciences

New York: McGraw-Hill Book Company, 1956. Presumed First Edition, First printing. Hardcover. Good. xvii, [1], 312, [4] pages. Illustrated endpapers. Glossary. Formulae. References. Appendices. Index. Ex-library with the usual markings. This is one of the McGraw-Hill Series in Psychology. Sidney Siegel (4 January 1916 in New York City - 29 November 1961) was an American psychologist who became especially well known for his work in popularizing non-parametric statistics for use in the behavioral sciences. He was a co-developer of the statistical test known as the Siegel-Tukey test. Siegel completed a Ph.D. in Psychology in 1953 at Stanford University. Except for a year spent at the Center for Advanced Study in the Behavioral Sciences at Stanford, he thereafter taught at Pennsylvania State University. Nonparametric statistics are statistics not based on parameterized families of probability distributions. They include both descriptive and inferential statistics. The typical parameters are the mean, variance, etc. Nonparametric statistics make no assumptions about the probability distributions of the variables being assessed. The difference between parametric models and non-parametric models is that the former has a fixed number of parameters, while the latter grows the number of parameters with the amount of training data. In statistics, the Siegel-Tukey test, named after Sidney Siegel and John Tukey, is a non-parametric test which may be applied to data measured at least on an ordinal scale. It tests for differences in scale between two groups. The test is used to determine if one of two groups of data tends to have more widely dispersed values than the other. In other words, the test determines whether one of the two groups tends to move, sometimes to the right, sometimes to the left, but away from the center (of the ordinal scale). The test was published in 1960 by Sidney Siegel and John Wilder Tukey in the Journal of the American Statistical Association, in the article "A Nonparametric Sum of Ranks Procedure for Relative Spread in Unpaired Samples.