Going green and gone nuts

Is our world going green? It may be a long while before we know. That’s because scores of geoscientists have gone nuts and work with junk statistics. In Canada, too, geoscientists would rather infer than test for spatial dependence in sampling units and sample spaces. The more so since it’s all in The Inspector’s Field Sampling Manual. Nobody should have to read it. Not even EC’s own inspectors. I had to in the early 2000s because Environment Canada had taken a client of mine to court. It was about my statistical analysis of test results determined in interleaved primary samples. So I worked my way through EC’s manual and found all sorts of sampling methods. What I didn’t find was the interleaved sampling method. I had put this method on my list of smart statistics long before global warming got hot.

Here’s what I did find out when I struggled with EC’s manual. Inspectors are taught, “Systematic samples taken at regular time intervals can be used for geostatistical data analysis, to produce site maps showing analyte locations and concentrations. Geostatistical data analysis is a repetitive process, showing how patterns of analytes change or remain stable over distances or time spans.”

Geostatistics already rubbed me the wrong way long before it converted Bre-X’s bogus grades and Busang’s barren rock into a massive phantom gold resource. In fact, Matheron’s new science of geostatistics has been a thorn in my side for some twenty years. That sort of junk statistics still runs rampant in the Journal for Mathematical Sciences. Just the same, EC’s field inspectors read under Systematic (Stratified) Sampling , “1) shellfish samples taken at 1-km intervals along a shore, 2) water samples taken from varying depths in the water column.” Numerical examples are missing as much in A Sampling Manual and Reference Guide for Environment Canada Inspectors as they were throughout Matheron’s seminal work. Not all of EC’s geoscientists know as little about testing for spatial dependence in sampling units and sample spaces as do those who cooked up The Inspector’s Field Sampling Manual.

In his letter of October 15, 1992, to Dr R Ehlich, Editor, Journal for Mathematical Geology, Stanford's Professor Dr A G Journel claimed , “The very reason for geostatistics or spatial statistics in general is the acceptance (a decision rather) that spatially distributed data should be considered a priori as dependent one to another, unless proven otherwise.” He believed that my anger “arises fro [sic] a misreading of geostatistical theory, or a reading too encumbered by classical ‘Fischerian’ [sic] statistics.” JMG’s Editor advised me in his letter of October 26, 1992, “Your feeling that geostatistics is invalid might be correct.”

Each and every geoscientist on this planet ought to know how to test for spatial dependence and how to chart sampling variograms that show where spatial dependence in our own sample space of time dissipates into randomness. Following is an Excel spreadsheet template that shows how to apply Fisher’s F-test. Geoscientists should figure out why Excel's FINV-function requires the number of degrees of freedom both for the set and for the ordered set.

Of course, it’s easy to become a geostatistically smart geoscientist. All it takes is to infer spatial dependence between measured values, interpolate by kriging, select the least biased subset of some infinite set of kriged estimates, smooth its kriging variance to perfection, and rig the rules of real statistics with impunity. All but a few of those who have gone nuts and work with junk statistics have written books about geostatistics!