“Statistics is the grammar of Science,” a famous quote by Karl Pearson who was the British statistician and leading founder of the modern field of statistics. Pearson highlights the importance of statistics and particularly emphasizes the significance of quantification for various fields of scientific study in his publication, The Grammar of Science .
Statistics is defined as the study of the collection, analysis, interpretation, presentation, and organization of data by the Oxford Dictionary of Statistical Terms . Since the data grow faster than ever and information is increasing tremendously nowadays, the role of statistics becomes more crucial.
In general, we can group statistical analysis into two parts: (i) descriptive statistics and (ii) inferential (analytical) statistics. Descriptive statistics is used to summarize and/or describe a collection of data. Therefore, descriptive statistics provides a powerful way to summarize what already exists in data. However, inferential statistics focuses on the patterns in the data and then draws inferences from these patterns. In other words, by analyzing data gathered from samples (smaller subsets of the entire population), statistical methods infer about populations.
The field of statistics is the science of learning from data. In other words, statistics is the tool we use to convert data into information. Decisions based on data and information will provide better outcomes than those just based on intuition or gut feelings. In our daily life, there is almost no human activity where the application of statistics is not needed. Therefore, application of statistics plays a very significant role in almost every field such as Mathematics, Physics, Chemistry, Biology, Botany, Medicine, Economics, Education, Public Policy, Psychology, Astronomy, Zoology, Bio-Technology, Information Technology, Manufacturing, Service Industry, Business, and Commerce, among many other fields.
Since the application of statistics is very wide, different and multidisciplinary fields have evolved over time. These are some examples of application of statistics to other disciplines: Astrostatistics, Biostatistics, Econometrics, Business Analytics, Environmental Statistics, Statistical Mechanics, Statistical Physics, Actuarial Science, and so on. For example, Astrostatistics is the field which applies statistics to astronomical data which indicate that astrostatistics is a combination of astrophysics and statistical analysis. Biostatistics is the application of statistics to a wide range of topics in biology. Econometrics is the field where statistical tools are used to explain economic theories, and business analytics is the branch in which the statistical analysis applied to understanding of business performance and opportunities. Statistical physics is the branch which uses statistical methods to answer physical problems, and actuarial science is the field that uses statistical methods to analyze the risk insurance and some other financial issues.
To underline the importance of statistics in our daily life, we can look at the following examples: (i)
Among many others, some benefits of statistical analysis can be summarized as follows. First of all, it helps to present and compare the facts from data in a definite form. In other words, expressing results and/or conclusions in numbers develops a necessary and common form of communication for scientists, policy makers, and many others. Secondly, it helps us to formalize our thinking. In particular, statistical methods are used in formulating/testing the hypotheses or a new theory. By using these methods, we can determine the likelihood that a hypothesis should be either rejected or not. Thirdly, statistical methods help us to draw conclusions about populations based only on sample results. Last but not least, statistics is very important especially when it comes to the conclusion of the research, and in this sense statistical methods allow us for forecasting and policy making.
Among many benefits, of course, there are also some misuses of statistics. Main examples of misuses among others are overgeneralization, biased samples, insufficient sample size, and spurious correlations. All these mistakes may give us misleading conclusions. Let me just give some intuition about these misuses. (i)