Colin You need to bear in mind that statistical tests such as t and F are only testing a very simple hypothesis - they do not test whether the samples are from the same population.
The F test is to check whether the standard deviations differ. If the ore is from the same genesis, it is likely that the variability will be constant and your F test will not be significant. The t test is against the hypothesis that the average values are the same. That is, one population has a higher average grade than the other. You can have the same variability around the mean, but have a zone where the minerals tend to concentrate at a higher average. Even if both tests are not significant, this does not 'prove' that the two populations are the same. You could have two sets of data with the same mean and standard deviation and completely different shapes, for example. To include the spatial element, you could try a cross validation approach where one set of samples is the 'actual' values and you try to estimate those from the other set. This will show up consistent differences in average between the two as well as differences in variability. Strictly, all of the above requires a Normal distribution but with your not-too-skewed data and thousands of samples, the Central Limit Theorem should take care of those problems. Isobel http://uk.geocities.com/drisobelclark
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