There is a tendency for individual consultants and organizations to name their own unique approaches to quality control—a few of these have ended up in widespread use:

Software

In the late '70's the Centre de Geostatistique, Fountainebleau, began a master level program in geostatistics (two years) which attracted a steady stream of students from industry and goverment in various countries. In cooperation with Shell Oil and the Bureau de Recherche Geologie Mathematique (the French USGS), a commercial software package called BLUEPACK was developed. The early version was only ported to the VAX but the successor, ISATIS, is available on a number of workstation platforms. It is marketed in the US by GEOMATH of Houston. Geostatistics without the computer is of little interest, in many ways the developments in geostatistics parallel those in computing, particularly the appearance of PC's and workstations. 

Publications and Conferences

Two small volumes on geostatistics focused on mining appeared in English in the 70's, one by Jean-Michel Rendu and one by Isabel Clark. In the late '80's the volume by Isaaks and Srivastava appeared, subsequently a book by Noel Cressie (on the more general topic of spatial statistics but including geostatistics). 

In the summer of 1983 a second NATO ASI was held at Lake Tahoe, NV with a more international mix and including researchers from a wider set of applications. Thanks to a series of four papers by Richard Webster and some of his students (then at Rothamstead Research Center in England), geostatistics became known in the soil sciences. These appeared in the J. Soil Science (1980-1981). In 1979 in Prague, the International Association of Mathematical Geologists was founded and later began publishing the J. of the Int. Assn. Math. Geologists (later the name was officially changed to simply Mathematical Geology). While the journal was not limited to geostatistics it quickly became a principal place to publish such papers. A third international geostatistics congress was held in Avignon, France in 1988, a fourth in Troia, Portugal in 1992 and the most recent in Wollongong, Australia in 1996. Following the 1983 conference, Andre Journel and Leon Borgman (University of Wyoming) proposed an annual summer retreat in geostatistics aimed at researchers in North America. The first one was held near DuBois, Wyoming in August of 1984. The group was small and families were encouraged, the sessions were informal and no proceedings were produced but subsequently a newsletter was started which has appeared infrequently since then. A non-organization was founded in 1987 at a meeting in the Chirachaua Mtns southeast of Tucson, there were to be no dues, no membership list, no subscription price for the newsletter but volunteers would be solicited each year to organize meetings. Several have been held in Canada as well as the US and in 1996 a meeting was held in Guanajuato, MX. Following the establishment of the newsletter in North America another newsletter intended for the European community was established. 

Following the 1983 meeting several staff members at EPA-Las Vegas became interested in the application of geostatistics to environmental monitoring and assessment. In addition to research support for a number of individuals and programs, EPA commissioned a geostatistical software package, GEO-EAS, which was then released into the public domain. GEO-EAS was a DOS program but included a menuing system that made it fairly friendly and the price was right. Unfortunately for various reasons EPA has not continued to support the software and it has not been updated for a number of years. In 1992 Andre Journel and Clayton Deutsch published GSLIB which included a floppy disk. This was an extensive set of geostatistical programs (FORTRAN source code) and a users manual. Current versions of the code are available on the website at Stanford. Unfortunately the programs did not include any form of GUI and are intended to be run in batch mode. They are compilable on a variety of platforms however. In 1996 Yvan Pannatier published VARIOWIN together with a floppy disk. VARIOWIN is a MS-Windows version of two of the components of GEO-EAS. It allows for much larger data sets than in GEO-EAS and also interactive variogram modeling. 

Although natural phenomena in the real life are unpredictable, the designs of experiments are bounded to generate data that varies because of intrinsic (internal to the system) or extrinsic (due to the ambient environment) effects. How many natural processes or phenomena in the real life that have an exact mathematical closed-form description and are completely deterministic can we describe? How do we model the rest of the processes that are unpredictable and have random characteristics?

Statistics is the science of variation, randomness and chance. As such, statistics is different from other sciences, where the processes being studied obey exact deterministic mathematical laws. Statistics provides quantitative inference represented as long-time probability values, confidence or prediction intervals, odds, chances, etc., which may ultimately be subjected to varyious interpretations. The phrase Uses and Abuses of Statistics refers to the notion that in some cases statistical results may be used as evidence to seemingly opposite theses. However, most of the time, common principles of logic allow us to disambiguate the obtained statistical inference.

Design of experiments is the blueprint for planning a study or experiment, performing the data collection protocol and controlling the study parameters for accuracy and consistency. Data, or information, is typically collected in regard to a specific process or phenomenon being studied to investigate the effects of some controlled variables (independent variables or predictors) on other observed measurements (responses or dependent variables). Both types of variables are associated with specific observational units (living beings, components, objects, materials, etc.)

All methods for data analysis, understanding or visualizing are based on models that often have compact analytical representations (e.g., formulas, symbolic equations, etc.) Models are used to study processes theoretically. Empirical validations of the utility of models are achieved by inputting data and executing tests of the models. This validation step may be done manually, by computing the model prediction or model inference from recorded measurements. This process may be possibly done by hand, but only for small numbers of observations (<10). In practice, we write (or use existent) algorithms and computer programs that automate these calculations for greater efficiency, accuracy and consistency in applying models to larger datasets.

During the final stage, the findings of the initial data analysis are documented, and necessary, preferable, and possible corrective actions are taken.

Also, the original plan for the main data analyses can and should be specified in more detail and/or rewritten.

In order to do this, several decisions about the main data analyses can and should be made:

Several analyses can be used during the initial data analysis phase:

It is important to take the measurement levels of the variables into account for the analyses, as special statistical techniques are available for each level:

Almost all educational programmes in biostatistics are at postgraduate level. They are most often found in schools of public health, affiliated with schools of medicine, forestry, or agriculture, or as a focus of application in departments of statistics.

In the United States, where several universities have dedicated biostatistics departments, many other top-tier universities integrate biostatistics faculty into statistics or other departments, such as epidemiology. Thus, departments carrying the name "biostatistics" may exist under quite different structures. For instance, relatively new biostatistics departments have been founded with a focus on bioinformatics and computational biology, whereas older departments, typically affiliated with schools of public health, will have more traditional lines of research involving epidemiological studies and clinical trialsas well as bioinformatics. In larger universities where both a statistics and a biostatistics department exist, the degree of integration between the two departments may range from the bare minimum to very close collaboration. In general, the difference between a statistics program and a biostatistics program is twofold: (i) statistics departments will often host theoretical/methodological research which are less common in biostatistics programs and (ii) statistics departments have lines of research that may include biomedical applications but also other areas such as industry (quality control), business andeconomics and biological areas other than medicine.

A statistician is someone who works with theoretical or applied statistics. The profession exists in both the private and public sectors. It is common to combine statistical knowledge with expertise in other subjects.

According to the United States Bureau of Labor Statistics, as of 2010, 25,100 jobs were classified as statistician in the United States. Of these people, approximately 30 percent worked for governments (federal, state, or local). Additionally, there is a substantial number of people who use statistics in their work but have job titles other than statistician, such as actuaries, applied mathematicians, economists, financial analysts, psychometricians, epidemiologists, and quantitative psychologists. The job of statistician is considered a profession. Most statisticians work in offices and have regular working hours and can therefore be considered white-collar workers. A minority of statisticians are self-employed statistical consultants.^{[citation needed]}

Most employment as a statistician requires a minimum of a masters degree in statistics or a related field. Many professional statisticians have a PhD. Typical work includes collaborating with scientists, providing mathematical modeling, simulations, designing randomized experiments and randomized sampling plans, analyzing experimental or survey results, and forecasting future events (such as sales of a product).

A field of applied statistics, survey methodology studies the sampling of individual units from a population and the associated survey data collection techniques, such as questionnaire construction and methods for improving the number and accuracy of responses to surveys.