- 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
medianis equal to hypothesized value)- Wilcoxon two-sample paired signed rank test (test whether population
medianof 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 --dichotomousoutcome variable)- McNemar's Q (test whether population
medianof paired differences is 0 --dichotomousoutcome variable, counted data arranged in "contingency" table)

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. 1996.
Beyond ANOVA, Basics of Applied Statistics.2nd. ed. London: Chapman & Hall.- 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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