Date of Award
Master of Science (MS)
Mathematics and Statistics
R. V. Canfield
Most statistical methods require assumptions about the populations from which samples are taken. Usually these methods measure the parameters, such as variance, standard deviations, means, etc., of the respective populations. One example is the assumption that a given population can be approximated closely with a normal curve. Since these assumptions are not always valid, statisticians have developed several alternate techniques known as nonparametric tests. The models of such tests do not specify conditions about population parameters.
Certain assumptions, such as (1) observations are independent and (2) the variable being studied has underlying continuity, are associated with most nonparametric tests. However, these assumptions are weaker and less in number than those commonly associated with parametric tests.
Nawabi, Frena, "Nonparametric Test of Fit" (1970). All Graduate Plan B and other Reports. 1131.
Copyright for this work is retained by the student. If you have any questions regarding the inclusion of this work in the Digital Commons, please email us at .