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Parametric tests assume that the data follow a particular distribution, typically a normal distribution, while non-parametric tests make no assumptions about the distribution. [7] Non-parametric tests have the advantage of being more resistant to misbehaviour of the data, such as outliers . [ 7 ]
Siegel–Tukey test, named after Sidney Siegel and John Tukey, is a non-parametric test which may be applied to data measured at least on an ordinal scale. It tests for differences in scale between two groups. The test is used to determine if one of two groups of data tends to have more widely dispersed values than the other.
In recent years, the Lepage statistic is a widely used statistical process for monitoring and quality control. In 2012, Amitava Mukherjee and Subhabrata Chakraborti introduced a distribution-free Shewhart-type Phase-II monitoring scheme [8] (control chart) for simultaneously monitoring of location and scale parameter of a process using a test sample of fixed size, when a reference sample of ...
Non-parametric (or distribution-free) inferential statistical methods are mathematical procedures for statistical hypothesis testing which, unlike parametric statistics, make no assumptions about the probability distributions of the variables being assessed. The most frequently used tests include
The squared ranks test is arguably a test of significance of difference of data dispersion not variance per se. This becomes important, for example, when the Levene's test fails to satisfy the rather generous conditions for normality associated with that test and is a default alternative under those conditions for certain statistical software ...
The Wilcoxon signed-rank test is a non-parametric rank test for statistical hypothesis testing used either to test the location of a population based on a sample of data, or to compare the locations of two populations using two matched samples. [1] The one-sample version serves a purpose similar to that of the one-sample Student's t-test. [2]
The Wilcoxon signed-rank test is a nonparametric test of nonindependent data from only two groups. The Skillings–Mack test is a general Friedman-type statistic that can be used in almost any block design with an arbitrary missing-data structure. The Wittkowski test is a general Friedman-Type statistics similar to Skillings-Mack test. When the ...
In statistics, the Brunner Munzel test [1] [2] [3] (also called the generalized Wilcoxon test) is a nonparametric test of the null hypothesis that, for randomly selected values X and Y from two populations, the probability of X being greater than Y is equal to the probability of Y being greater than X.