History of statistics and its significance

History of Statistics and its Significance

Statistics is a relatively new subject, which branched from Probability Theory and is widely used in areas such as Economics and Astrology. It is a logic and methodology to measure uncertainty and it is used to do inferences on these uncertainties (Stigler, 1986). The history of Statistics can be firstly traced back to the 1600’s. John Graunt (1620-1674) could be considered as the pioneer of statistics and as the author of the first book regarding statistics. He published Natural and Political observations on the Bills of Mortality in 1662 whereby he was studying the plague outbreak in London at the time requested by the King. Graunt was asked to come up with a system that would allow them to detect threats of further outbreaks, by keeping records of mortality and causes of death and making an estimation of the population. By forming the life table, Graunt discovered that ‘statistically’, the ratio of male to females are almost equal. Then in 1666, he collected data and started to examine life expectancies. All of this was fundamental as he was arguably the first to create a condensed life table from large data and was able to do some analysis on it. In addition, this is widely used in life insurance today, showing the importance and significance of Graunt’s work (Verduin, 2009). Another reason why this is significant is because of his ability in demonstrating the value of data collection (Stigler, 1986). Then in 1693, Edmond Halley extended Graunt’s ideas and formed the first mortality table that statistically made the relationship between age and death rates. Again, this is used in life insurance (Verduin, 2009).

Another contributor to the formation of statistics is Abraham De Moivre (1667-1823). He was the first person to identify the properties of the normal curve and in 1711, introduced the notion of statistical independence (Verduin, 2009). In 1724, De Moivre studied mortality statistics and laid down foundations of the theory of annuities, inspired by the work of Halley. This is significant as annuities are widely used in the Finance industry today, in particular, when forming actuarial tables in life insurance. De Moivre then went on to talk about the idea of the normal distribution which can be used to approximate the binomial distribution (O’Connor and Robertson, 2004).

William Playfair (1759-1823) was the person who invented statistical graphics, which included the line graph and the bar graph chart in 1786 and the pie chart in 1801. He believed that charts were a better way to represent data and he was “driven to this invention by a lack of data”. This was a milestone as these graphical representations are used everywhere today, the most notable being the time-series graph, which is a graph containing many data points measured at successive uniform intervals over a period of time. These graphs can be used to examine data such as shares, and could be used to predict future data (Robyn 1978).

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Adolphe Quetlet (1796-1874) was the first person to apply probability and statistics to Social Sciences in 1835. He was interested in studying about human characteristics and suggested that the law of errors, which are commonly used in Astronomy, could be applied when studying people and through this, assumptions or predictions could be in regards to physical features and intellectual features of a person. Through Quetlet’s studies, he discovered that the distribution of certain characteristics when he made a diagram of it was in a shape of a bell curve. This was a significant discovery as Quetlet later went on to form properties of the normal distribution curve, which is a vital concept in Statistics today. Using this concept of “average man”, Quetlet used this to examine other social issues such as crime rates and marriage rates. He is also well known for the coming up with a formula called the Quetlet Index, or more commonly known as Body Mass Index, which is an indication or measure for obesity. This is still used today and you could find out your BMI by calculating. If you get an index of more than 30, it means the person is officially obese (O’Connor and Robertson, 2006).

Other members who made little but significance contributions to Statistics are Carl Gauss and Florence Nightingale. Gauss was the first person who played around with the least squares estimation method when he was interested in astronomy and attempted to predict the position of a planet. He later proved this method by assuming the errors are normally distributed. The method of least squares is widely used today, in Astronomy for example, in order to minimise the error and improve the accuracy of results or calculations (O’Connor and Robertson, 1996). It was also the most commonly used method before 1827 when trying to combine inconsistent equations (Stigler, 1986). Nightingale was inspired by Quetlet’s work on statistical graphics and produced a chart detailing the deaths of soldiers where she worked. She later went on to analyse that state and care of medical facilities in India. This was significant as Nightingale applied statistics to health problems and this led to the improvement of medical healthcare. Her important works were recognised as became the first female to be a member of the Royal Statistical Society (Cohen, 1984).

One of the greatest contributors was Francis Galton (1822-1911) who helped create a statistical revolution which laid foundations for future statisticians like Karl Pearson and Charles Spearman (Stigler, 1986). He was related to Charles Darwin and had many interests, such as Eugenics and Anthropology. He came up with a number of vital concepts, including the regression, standard deviation and correlation, which came about when Galton was studying sweet peas. He discovered that the successive sweet peas were of different sizes but regressed towards the mean size and the distribution of their parents (Gavan Tredoux, 2007). He later went on to work with the idea of correlation when he was studying the heights of parents and the parent’s children when they reach adulthood, where he made a diagram of his findings and found an obvious correlation between the two. He then performed a few other experiments and came to the conclusion that the index of the correlation was an indication to the degree in which the two variables were related to one another. His studies were significant as they are all fundamental in Statistics today and these methods are used in many areas for data analysis, especially with extracting meaningful information between different factors (O’Connor and Robertson, 2003).

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