What is statistical analysis? It might sound pretty naïve, to many, to ask this question. In his introduction to ‘Statistical Analysis Handbook’ Dr. M.J de Smith contrasts how the meaning of the words statistics has changed over time by quoting the definitions offered by some of the greatest personalities in the discipline. Professor Maurice Kendall known for his work that lead to the development of the Kendall’s tau rank correlation measure is one of the personalities. The British government knighted him for his contribution to the theory of statistics in 1974 but his debut started in the early 1930s, carried on through the World War II leading to some book publications such as The Advanced Theory of Statistics. In the year 1943 Kendal defined statistics as “… the branch of scientific method which deals with the data obtained by counting or measuring the properties of populations of natural phenomena. In this definition ‘natural phenomena’ includes all the happenings of the external world, whether human or not.”

 

The second personality is Professor David Hand. He is a senior research investigator and emeritus Professor of Mathematics at Imperial College, London. He is a Fellow of the British Academy and a recipient of the Guy Medal of the Royal Statistical Society, and has served twice as President of the Royal Statistical Society. His latest contribution is a book by the title ‘The Improbability Principle’. Here is what he says: “Statistics is: the fun of finding patterns in data; the pleasure of making discoveries; the import of deep philosophical questions; the power to shed light on important decisions, and the ability to guide decisions….. in business, science, government, medicine, industry…”

 

Indeed the use of statistics has changed over time and its range application now spans disciplines that were hitherto not encompassed in its uses. In Dr. Smith’s words, the discipline of statistics has moved from being grounded firmly in the world of measurement and scientific analysis into the world of exploration, comprehension and decision-making.

 

Statistical analysis should be used as a tool to generate insights from data. The data might come from any field of study and can be structure of unstructured, and the insights generated can aid comprehension and/or assessment of a particular relationship of interest, decision-making in business and industry and among other uses depending on the nature of the problem at hand. Broadly speaking, a statistical analysis can either be descriptive or inferential. In descriptive statistics, the purpose is to represent or summarise the data in the simplest form possible to enable, for example, the observation of patterns and the reduction of the data in to a few summary quantities that are easier to assimilate. In inferential statistics, further considerations such as sampling and random errors are made. The aim is to enable the user to draw conclusions about the population of units from which the data used in the statistical analysis is drawn from.

Because of the growing relevance of statistical analysis in many academic disciplines there is demand for increased proficiency in analysis, both for individuals who are trained to be statisticians and those who are not. Don’t be left behind, learn something!