Changes

The purpose of statistics is to project or infer, from limited samples, the character of a population. In most cases, particularly in oil and gas investigations, geological information is not derived from carefully designed sample schemes but, by design, represents anomalies. What successful company would drill on a regional trend as opposed to the top of a structure, on a bright spot, or at the crest of a reef? Statistical procedures presume that sufficient data are randomly sampled from a population and that the average sample value approximates the population average. This is only possible if both high and low values are sampled without bias and enough samples are taken to stabilize the calculations. While proper sampling techniques are essential to formal statistical inference, geological samples are much too difficult or costly to obtain and cannot be discarded. Therefore, the robust testing of hypotheses and calculation of confidence intervals for statistical projections must be viewed in the restrictive light of geological data. Nonetheless, quantitative description and relationship inferences can be made with the underlying awareness of the constraint of data quality.

The purpose of statistics is to project or infer, from limited samples, the character of a population. In most cases, particularly in oil and gas investigations, geological information is not derived from carefully designed sample schemes but, by design, represents anomalies. What successful company would drill on a regional trend as opposed to the top of a structure, on a bright spot, or at the crest of a reef? Statistical procedures presume that sufficient data are randomly sampled from a population and that the average sample value approximates the population average. This is only possible if both high and low values are sampled without bias and enough samples are taken to stabilize the calculations. While proper sampling techniques are essential to formal statistical inference, geological samples are much too difficult or costly to obtain and cannot be discarded. Therefore, the robust testing of hypotheses and calculation of confidence intervals for statistical projections must be viewed in the restrictive light of geological data. Nonetheless, quantitative description and relationship inferences can be made with the underlying awareness of the constraint of data quality.

It is also important to remember the effect of resolution and precision in analyzing quantitative geological data. J. C. Davis put it eloquently in his introduction to his classic text (Davis, 1986<ref name=pt06r24>Davis, J. C., 1986, Statistics and data analysis in geology: New York, John Wiley, 646 p.</ref>):

It is also important to remember the effect of resolution and precision in analyzing quantitative geological data. J. C. Davis put it eloquently in his introduction to his classic text:<ref name=pt06r24>Davis, J. C., 1986, Statistics and data analysis in geology: New York, John Wiley, 646 p.</ref>:

<blockquote>If you pursue the following topics, you will become involved with mathematical methods that have a certain aura of exactitude, that express relationships with apparent precision, and that are implemented on devices which have a popular reputation of infallibility.

<blockquote>If you pursue the following topics, you will become involved with mathematical methods that have a certain aura of exactitude, that express relationships with apparent precision, and that are implemented on devices which have a popular reputation of infallibility.