Computational statistics, or statistical computing, is the bond between statistics and computer science. It means statistical methods that are enabled by using computational methods. It is the area of computational science (or scientific computing) specific to the mathematical science of statistics. This area is also developing rapidly, leading to calls that a broader concept of computing should be taught as part of general statistical education.

As in traditional statistics the goal is to transform raw data into knowledge, but the focus lies on computer intensive statistical methods, such as cases with very large sample size and non-homogeneous data sets. The terms 'computational statistics' and 'statistical computing' are often used interchangeably, although Carlo Lauro a former president of the International Association for Statistical Computing) proposed making a distinction, defining 'statistical computing' as "the application of computer science to statistics", and 'computational statistics' as "aiming at the design of algorithm for implementing statistical methods on computers, including the ones unthinkable before the computer age (e.g. bootstrap, simulation), as well as to cope with analytically intractable problems" [sic]. The term 'Computational statistics' may also be used to refer to computationally intensive statistical methods including resampling methods, Markov chain Monte Carlo methods, local regression, kernel density estimation, artificial neural networks and generalized additive models. Though computational statistics is widely used today, it actually has a relatively short history of acceptance in the statistics community. For the most part, the founders of the field of statistics relied on mathematics and asymptotic approximations in the development of computational statistical methodology. In statistical field, the first use of the term “computer” comes in an article in the Journal of the American Statistical Association archives by Robert P. Porter in 1891. The article discusses about the use of Hermann Hollerith’s machine in the 11th Census of the United States. Hermann Hollerith’s machine, also called tabulating machine, was an electromechanical machine designed to assist in summarizing information stored on punched cards. It was invented by Herman Hollerith (February 29, 1860 – November 17, 1929), an American businessman, inventor, and statistician. His invention of the punched card tabulating machine was patented in 1884, and later was used in the 1890 Census of the United States. The advantages of the technology were immediately apparent. the 1880 Census, with about 50 million people, and it took over 7 years to tabulate. While in the 1890 Census, with over 62 million people, it took less than a year. This marks the beginning of the era of mechanized computational statistics and semiautomatic data processing systems In 1908, William Sealy Gosset performed his now well-known Monte Carlo method simulation which led to the discovery of the Student’s t-distribution. With the help of computational methods, he also has plots of the empirical distributions overlaid on the corresponding theoretical distributions. The computer has revolutionized simulation and has made the replication of Gosset’s experiment little more than an exercise.

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John Gresham  
journal coordinator
international journal of innovative research in computer and communication engineering