Peter,

While the chi-square does require binning, the K-S (Kolmogorov-Smirnov) does not.  While some do bin data for a K-S, one of the reasons many use the K-S (or the supposedly slightly better Anderson-Darling available in Minitab for example) is that it can be (& should generally be) done w/o binning.  Thus no binning decisions or underling default of a given software package to influence the outcome.

Best,

Bill

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Peter Bossew wrote:
(original point by Gregoire:)
  
Moreover, one can frequently not be "sure" about the lognormality of 
      
the analysed dataset, so why would one still take the risk of using 
log-normal kriging?

    

(Anatoly:)
  
what means "not sure"??? Pearson and Kolmogorov-Smirnov tests will be 
used :-))
    



Anatoly,
Chi2 and KS-tests also require binning the data, and different binning
gives different p values, in general !




related questions:

1) the Weibull distribution can sometimes be used to describe datasets
with heavy tails. 
(e.g., www.itl.nist.gov/div898/handbook/eda/section3/eda3668.htm )
However I don't know how to transform Weibull -> normal and back. Does
somebody have experience ?

2) What is the effect of deviations of data from normal distr., in
particular due to the presence of extrema (outliers, hot/cold spots),  to
structural analysis and to estimation, be it of kriging or simulation type
?

regards, Peter


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circumstances be regarded as stating an official position of the European
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