"' Nonparametric "'( or " distribution-free " ) inferential statistical methods are mathematical procedures for statistical hypothesis testing which, unlike parametric statistics, make no assumptions about the frequency distributions of the variables being assessed.
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In non-parametric statistics, the theory of U-statistics is used to establish for statistical procedures ( such as estimators and tests ) and estimators relating to the asymptotic normality and to the variance ( in finite samples ) of such quantities.
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"' Istv�n Vincze "'( & ndash; ) was a Hungarian mathematician, known for his contributions to number theory, non-parametric statistics, empirical distribution, Mathematical Institute of the Hungarian Academy, and was the Head of the Statistics Department.
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:" " . . . based on simple mistakes such as lack of control groups, lack of blinding ( use of a placebo ), insufficient power ( not enough subjects to demonstrate a significant effect even if one was present ) and inappropriate statistics ( for example, multiple t-tests without correction or use of parametric statistics on non-normally distributed data ).
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Aside from the existence of the reference values, the data aren't expected to follow any given distribution, or necessarily match the variance or mean of the reference values . ( Theoretically there should be a simple transformation function from the data values to the reference values, but we can't specify " a priori " what the function should be-the only constraint that we have at the moment is that the transformation function would be monotonic and non-decreasing . ) As I understand it, the way to handle such a situation is with non-parametric statistics, and the way to tell " how well " the data match the reference values ( rank-order-wise ) is probably by using the Kendall tau rank correlation coefficient ( though I'm still a little unclear on the difference between it and the Kendall tau distance or Kendall's W, and where one is used in preference to the others ).
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