Nonparametric

PROPHET StatGuide: Nonparametric Tests--Tests that do not assume normality

From http://www.basic.northwestern.edu/statguidefiles/nonpar.html

one-sample sign test (test the hypothesis that the probability of a random value from the population being above the specified value is equal to the probability of a random value being below the specified value)

two-sample paired sign test (test the hypothesis that the probability of a paired difference being above 0 is equal to the probability of a paired difference being below 0)

Wilcoxon one-sample signed rank test (test whether population median is equal to hypothesized value)

Wilcoxon two-sample paired signed rank test (test whether population median of paired differences is 0)

Mann-Whitney rank sum test (test whether two population distribution functions are identical against the alternative that they differ by location)

Kruskal-Wallis test (test whether several population distribution functions are identical the alternative that they differ in location)

Friedman's test (test whether several treatment effects ( locations) are equal for data in a two-way layout)

Ansari-Bradley test (test whether two population distribution functions are identical vs the alternative that they differ by dispersion (scale))

Cochran's Q (test whether several treatment effects ( locations) are equal --dichotomous outcome variable)

McNemar's Q (test whether population median of paired differences is 0 --dichotomous outcome variable, counted data arranged in "contingency" table)

The full version of StatGuide for nonparametric tests will be available in a future release. In the meantime, to properly analyze and interpret results of nonparametric tests, you should be familiar with the following terms and concepts:

If you are not familiar with these terms and concepts, you are advised to consult with a statistician. Failure to understand and properly apply nonparametric tests may result in drawing erroneous conclusions from your data. Additionally, you may want to consult the following references:

Brownlee, K. A. 1965. Statistical Theory and Methodology in Science and Engineering. New York: John Wiley & Sons.

Conover, W. J. 1980. Practical Nonparametric Statistics. 2nd ed. New York: John Wiley & Sons.

Daniel, Wayne W. 1978. Applied Nonparametric Statistics. Boston: Houghton Mifflin.

Daniel, Wayne W. 1995. Biostatistics. 6th ed. New York: John Wiley & Sons.

Hollander, M. and Wolfe, D. A. 1973. Nonparametric Statistical Methods. New York: John Wiley & Sons.

Lehmann, E. L. 1975. Nonparametrics: Statistical Methods Based on Ranks. San Francisco: Holden-Day.

Miller, Rupert G. Jr. 1986. Beyond ANOVA, Basics of Applied Statistics. New York: John Wiley & Sons.

Rosner, Bernard. 1995. Fundamentals of Biostatistics. 4th ed. Belmont, California: Duxbury Press.

Sokal, Robert R. and Rohlf, F. James. 1995. Biometry. 3rd. ed. New York: W. H. Freeman and Co.

Zar, Jerrold H. 1996. Biostatistical Analysis. 3rd ed. Upper Saddle River, NJ: Prentice-Hall.

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