The Development of Probability and Distribution - Essay Example

What are the Historical facts Behind the Development of Probability and Distribution? Introduction Statistics is the field that is concerned with investigation, summarizing and inferring from facts relating to complex systems. History shows that various statistical tools such as probability and distribution methods were developed around the sixteenth century. The time period when these marvelous mechanisms were established was the scientific revolution. Some efforts regarding probability were also made in the 15th century in the context of dice throwing but the finished and established version came to light only in the sixteenth century (Aldrich, 2010). The word probability originated form the Latin word probare that means informal. It means that is there is nothing certain and exact associated with an event, and that happenings are uncertain and there are probabilities that are associated with them. Probability can be defined as the study that is concerned with locating certainty in uncertain events. Probability is known as the method of weighing evidence and opinion about uncertain and complex environment. Christiaan Huygens is supposed to be the first person to treat probability with scientific principles to provide a more refined form of the term in the year of 1657. There are several functions of probabilities. Distributions are considered one of them. These functions are used to assign the possible and probable events to the entire series of the events. There are several types of distribution too, that is continuous and discrete distribution. Probabilities distribution can be categorized into two classes: discrete and continuous. There are many ways to divide probability up in an equal and proportionate manner but the best possible method is to use the probability density function (Fadyen, 2003). Development in this field is taking place continuously and rapidly. The world is changing as never before due to technologies, and these technologies are dramatically affecting the speed and complexity of probability calculations. Computers and spreadsheets are just two important examples of this shift. The major gains in communication complexity and speed have also played an important role. And this is apositive aspect because the development in the field of statistical data analysis is often caused by advancement in the other fields in which the various methods of statistical data analysis are productively applied. Background of evolution of static’s and probability techniques Probability is a statistical technique which is used for analyzing the situations that are confined by random events. For quantitative analysis of many human activities, which generally contain a large set of statistical and randomized data, it was essential to evaluate a method or theory that can work on highly uncertain and complex environment (Stigler, 1986). Especially in Europe the need to develop these statistical methods was derived from facts that were generated through various population surveys, which were done in the various emerging states of the nation (Brief history of statistics, 2002). Other disciplines for evaluating uncertain data like this were complex and not appropriate. For this purpose, in the sixteenth century, a mathematical theory was evolved, which was termed probability theory. At that time, the main intention of the theory was to analyze the game of chance, typical problems with uncertain environments. Basically, game of chance was a technique of gambling. For making this gambling easy and making the predictions more accurate, Blaise Pascal and Pierre de Fermat developed the theory of probability in the 17th century. Further, in the 18th century, statistician Abraham de Moivre created the theory of binomial distribution approaches in order to compute lengthy computation. De Moivre published the Doctrine of Chances, which contained a series of solved problems of probability and distribution. At that time, the major application of these distribution techniques was to analyze the typical and complex observations related to Astronomy, in order to take into account the errors in the measurement. As time passed, and each mathematicians built upon the legacy of previous ones, the discipline grew in complexity and applicability. People Associated with the Development There are many people who have significantly contributed to the evolution and development of probability and statistics. The analysis and close examination of the history of probability and statistics suggest that it is as old as the findings of Pierre de Fermat and Blaise Pascal around the year of 1654. The concept incorporated a scientific approach by Christiaan Huygens in the year 1657. There were people who treated probability as a branch of science, and at the same time there was a school of thought which regarded the concept based on mathematical findings only. In the year of 1713, Jakob Bernoulli in his Ars Conjectandi and Abraham de Moivre in 1718, via his Doctrine of Chances, treated the subject under the light of mathematical concepts. In the year 1722, Roger Cotes added theory of errors to the scope and functionality of probability. The application of this theory in reality was done by Thomas Simpson in the year 1755 (Probability-Definition, 2010). The first person ever who tried to derive and establish a law with respect to the combination of the laws in terms of probability was Pierre-Simon Laplace. There are many other achievements that are also credited to his account. He is also known for the derivation of a formula that is capable of calculating the mean for three terms at a same time. Apart from this, he is also known for explaining the law of probability by making use of graphical representation techniques. With the use of graphical representation, the distribution of the probabilities that are associated with various events can be understood clearly and easily (Probability-Definition, 2010). This is especially important in todays day and age when graphic illustrations can be designed automatically by computer software to a level of accuracy that would have been unheard of even 20 years ago. In addition to this, other authors, including James and Nicholas Bernoulli, Pierre Rémond de Montmort, and Abraham De Moivre calculated the various odds in complicated games, developed a powerful, strong and reliable mathematical tool which was termed the theory of probability. Lately, in the seventeenth century around 1778, Daniel Bernoulli added to the findings and the scope of application regarding the concept of probability. He devised a principle that was concerned with the product of the probabilities that were associated with the system errors. The method of least squares was developed by Adrien-Marie Legendre in the year of 1805. This is regarded as a key feature in the theory of probability. There were several laws that are regarded as the baseline for the theory of probability, which were devised and formulated in the eighteenth century. There are many such laws. One such law is for the facility of errors and was developed by Robert Adrain in 1808. There are several other contributors that added to the development and establishment of the concept in other ways. Many contributors tried and succeeded in giving proofs for the facility of errors. For example, John Herschel, Gauss and James Ivory, who were all geniuses in their own rights. In the nineteenth century also, many authors tried to contribute to the development and establishment of probability and distribution methods (Probability-Definition, 2010). The built upon work done by earlier authors, each placing brick upon brick to build the edifice that is modern day probability. Issues related to the development of statistical applications The statistical applications witness drastic changes in the current scenario. Various advanced technical aspects have been linked with statistical applications like SPSS analysis software. In various mathematics, statistical and engineering organizations like AMS, SIAM IMS, The Bernoulli Society, IEEE and INFORMS, various sponsor journal and research papers show how statistics play a vital role. The other issue related to this topic is the difference between practical and theoretical application of the probabilistic techniques. The rule of Bernoulli for combining probabilities did not achieve his goal of making probability a tool for everyday life and judicial affairs (Shafer). But a major issue related to this subject relates to how everyone is interested in it but no one is responsible for it (Powell, 1997). Still, the development and application of the probability technique is disordered in the organizational system. Organizations and universities are suffering with diffuse environments for the development of the statistical applications. There is no central organization to maintain standards. The setting under which probability is researched and studied in organizations and universities may lead to isolation. For the development of new application of statistics, the free flow exchange of ideas and methods is required. The span of the application of statistical measures is emerging in various broad areas like algorithms, statistical physics, dynamical and physical systems, complex networks, mathematical finance risk and dependency, perception in artificial and natural systems and genetics and ecology (Current and Emerging Research Opportunities in Probability). Impact of development of the statistical measures The impact of various statistical measures like probability and distribution techniques is having a greater impact in various different fields than ever before. For computing and analyzing complex and uncertain situations, various probability and distribution models are used widely. During 20th century, these measures became a strong and scientific framework for research in fields like education, agriculture, economics, biology, and medicine. Furthermore, it also influenced the various fields of hard science including astronomy, geology, and physics. Apart from this, the impact of the development of the probability and distribution techniques have left their impact on important management-related aspects like decision-making. Under the conditions of uncertainty, the decision-making demands for the probabilistic risk assessment of the decision is vitally important. These methods help the manager to enhance his capabilities for taking appropriate decisions under uncertain conditions (Topics in Statistical Data Analysis). There are innumerable examples of how this works in our modern world. Leaders are more and more under time constraints, they need be able measure risk accurately and quickly. Probability theory is one effective way of doing it. Without it, we would not know where we stand in the world. It helps tell us where and who we are. References Aldrich, J. (2010). Figures from the history of probability and statistics. Retrieved 06 September, 2010, From http://www.economics.soton.ac.uk/staff/aldrich/Figures.htm Brief history of statistics. (2002). Retrieved 06 September, 2010, From http://folk.uib.no/ngbnk/kurs/notes/node4.html Current and Emerging Research Opportunities in Probability.(n.d.). Retrieved 06 September, 2010, From http://stat-www.berkeley.edu/~peres/report/Research-opp.html Fadyen, D. M. (2003). An Historical Survey of the Development of Probability and Statistics based on the Appearance of Fundamental Concepts. Retrieved 06 September, 2010, From http://www.probability.ca/jeff/ftpdir/mcfadyenessay.pdf Probability-Definition. Retrieved 06 September, 2010, From http://www.wordiq.com/definition/Probability Powell, R R.(1997). Basic research methods for librarians. Greenwood Publishing Group. Shafer, G. (n.d.). The Early Development of Mathematical Probability. Retrieved 06 September, 2010, From http://www.glennshafer.com/assets/downloads/articles/article50.pdf Stigler, S M. (1986). The history of statistics: the measurement of uncertainty before 1900. Harvard University Press. Topics in Statistical Data Analysis. (n.d.). Retrieved 06 September, 2010, From http://home.ubalt.edu/ntsbarsh/stat-data/topics.htm Read More