Such reads the caption that these days graces my website.
A few changes have been made since it was posted in 2003. What pleased me most was
that loads of facsimiles and scores of snail mails could be whittled down to links.
It didn’t take Merks and Merks long to figure out why geostatistics is an
invalid variant of applied statistics. All it took was a close look at
geostatistics when CIM Bulletin did reject

*Precision Estimates for Ore Reserves*. We did so since it was praised by and published in*Erzmetall 44 (1991) Nr 10*. It was easy to find out what was wrong with geostatistics. It matters not at all that the distance-weighted average is called a kriged estimate. What does matter is that it did somehow shed its variance. Geostatistocrats have not yet put into plain words why each and every kriged estimate has lost its variance.
Matheron’s new science of geostatistics has made landfall
on this continent in 1970. A geostatistics colloquium in North America took
place on campus at The University of Kansas, Lawrence on 7-9 June 1970. Its proceedings
were recorded by Daniel F Merriam and published by Plenum Press, New
York-London, 1970. A Maréchal and J Serra had graduated at the

*Centre de Morphologie Mathématique*at Fontainebleau, France. They had come to shed light on*Random Kriging*. The authors point to*Punctual Kriging*in Figure 10. It shows how to derive a set of sixteen (16) grades from a set of nine (9) grades. It looked a bit of a slight of hand but it seemed to make sense to Professor Dr Michel David. So he posted Maréchal and Serra’s Figure 10 on page 286 in*Chapter 10 The Practice of Kriging*of his 1977 textbook.

**Figure 10 – Grades of n samples belonging to**

nine rectangles P of pattern surrounding xnine rectangles P of pattern surrounding x

**Figure 203 – Pattern showing all points within B,**

**which are estimated from the same nine holes**
Why geostatistics is but a bogus variant of applied statistics is simple comme bonjour! Functions do have variances. No ifs or buts! That’s
why one-to-one correspondence between functions and variances is

*sine qua non*in applied statistics. Degrees of freedom are positive integers when all measured values in the set have the same weight. Degrees of freedom are positive irrationals when all measured values in the set have variable weights.
The power of applied statistics has served me well throughout
my career. It did because so much of applied statistics is intuitive. For
example, any set of measured values has a central value, a variance, a standard
deviation and a coefficient of variation. The central value is either its arithmetic
mean or some weighted average. Numbers of measured values in sets define confidence
limits for central values. Testing for spatial dependence between measured
values in ordered sets shows where orderliness in sample spaces or sampling
units dissipates into randomness. Never did it make any sense in my work to
assume spatial dependence between measured values in ordered sets. What does make sense is testing for spatial
dependence, skewness and kurtosis.

The central limit theorem defines the relationship between
a set of measured values and its central value. Even David did refer to

*“the famous central limit theorem”*. Yet, he didn’t deem it famous enough to add to his*Index*. Testing for spatial dependence between measured values in sample spaces and sampling units plays a key role in scores of applications in a wide range of disciplines. Participation in several standard committees served to make applied statistics indispensable in so many ways. I do have but a few simple questions at this stage. Why did Professor Dr Georges Matheron (1930-2000) cook up such a silly variant of applied statistics? Why was Matheron’s work deemed beyond peer review! Why didn’t anybody point out to him that all functions do have variances? Why doesn’t the mining industry care about unbiased confidence limits for metal contents and grades of reserves and resources?
Today I woke up as a certified octogenarian. I took a
ride on my stationary bike and got nowhere. Yet I felt good. But I am still sick and
tired of those who play games with other people’s money. All I want to do at this stage of my life is show how to work with
sound statistics and how to get rid of bogus science.

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