Disadvantages of non-parametric tests:

Losing precision: Edgington (1995) asserted that when more precise 
measurements are available, it is unwise to degrade the precision by 
transforming the measurements into ranked data.

Low power: Generally speaking, the statistical power of non-parametric 
tests are lower than that of their parametric counterpart except on a few 
occasions (Hodges & Lehmann, 1956; Tanizaki, 1997). 

Inaccuracy in multiple violations: Non-parametric tests tend to produce 
biased results when multiple assumptions are violated (Glass, 1996; 
Zimmerman, 1998). 

Testing distributions only: Further, non-parametric tests are criticized 
for being incapable of answering the focused question. For example, the 
WMW procedure tests whether the two distributions are different in some 
way but does not show how they differ in mean, variance, or shape. Based 
on this limitation, Johnson (1995) preferred robust procedures and data 
transformation to non-parametric tests. 

Hope it helps.

Chong-ho (Alex) Yu, Ph.D., CNE, MCSE
Instruction and Research Support
Information Technology
Arizona State University
Tempe AZ 85287-0101
Voice: (602)965-7402
Fax: (602)965-6317

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