anchor.diff:  
     - nominal scale, ordinal scale, ratio scale or interval scale data
   
  -
   References:
   
     - E. Ostertagová,  Methodology and Application of the Kruskal-Wallis Test (2014)
   
     - Y. Chan, RP Walmsley, Learning and understanding the Kruskal-Wallis one-way analysis-of-variance-by-ranks test for differences among three or more independent groups (1997)
   
  -*H-test*
  -*)
  -
  -// input : seq{seq<float>} 
  -let groupA = seq {23.;41.;54.;66.;78.} 
  -let groupB = seq {45.;55.;60.;70.;72.}
  -let groupC = seq {18.;30.;34.;40.;44.} 
  -let samples = seq{groupA;groupB;groupC}
  -
  -// calculation of p-Value
  -HTest.createHTest samples 
  -
  -(*
  -        { Statistic = 6.72
  -        DegreesOfFreedom = 2.0
  -        PValueLeft = 0.9652647411
  -        PValueRight = 0.03473525894
  -        PValue = 0.06947051789 }
  -*)
  -// PValueRight equals the alpha level 
  -
  -(**
  -The implemented H-test is testing for double values in the data. 
  -Double values lead to ties in the ranking, and are corrected by using a correction factor. 
  +*)
  +
  +let groupA = [44.;44.;54.;32.;21.;28.]
  +let groupB = [70.;77.;48.;64.;71.;75.]
  +let groupC = [80.;76.;34.;80.;73.;80.] 
  +let samples = [groupA;groupB;groupC]
  +
  +// calculation of the H test 
  +let hResult = HTest.createHTest samples 
  +
  +(*** include-value:hResult ***)
  +
  +(**
  +_PValueRight is significant at a alpha level of 0.05_
   *)
   
   
  
positive.code: *)
  
  (** 
  ##H-Test
  The H test is also known as Kruskal-Wallis one-way analysis-of-variance-by-ranks and is the nonparametric equivalent of one-way ANOVA. 
  It is a non-parametric test for comparing the means of more than two independent samples (equal or different sample size), and therefor is an extension of Wilcoxon-Mann-Whitney two sample test.
  Testing with H test gives information whether the samples are from the same distribution.
  A benefit of the H-test is that it does not require normal distribution of the samples.
  The downside is that there is no information which samples are different from each other, or how many differences occur. For further investigation a Post Hoc test is recommended. 
  The distribution of the H test statistic is approximated by chi-square distribution with degrees of freedom - 1. 
  
  Prerequisites:
  
  
negative.code: (*** hide ***)
  // This block of code is omitted in the generated HTML documentation. Use 
  // it to define helpers that you do not want to show in the documentation.
  #I "../../bin/FSharp.Stats/netstandard2.0"
  #r @"FSharp.Stats.dll"
  #r "netstandard.dll"
  #r @"../../lib/Formatting/FSharp.Plotly.dll"
  