During data analysis, quite often, either of descriptive statistics or inferential statistics methodologies is employed. Descriptive statistics (which unlike inferential statistics is not developed on the basis of probability theory) basically refers to the discipline of quantitatively describing the main features of a collection of data while inferential statistics on the other hand denotes the process of arriving at conclusions from data open to random or sampling variations (such as observational errors) (Graham, pg.40).
Statistics is a wide field of study, probably one of the broadest disciplines available, with a plethora of applications too. The topic of ‘Fields of Application of Statistics’ is a rich topic in itself and one that sparks a lot of interest both to scholars and professions globally. Some of the sub-topics herein include actuarial science, astro-statistics, business analytics, chemo-metrics, demography, econometrics, environmental statistics, epidemiology, geo statistics, operations research, population ecology, statistical thermodynamics, and biostatistics and so on (Graham, pg.82). This paper deals with the biostatistics aspect of statistics.
Biostatistics, commonly referred to as biometry, is the use of statistics in a wide range of topics in biology. Biostatistics deals with the design of biological experiments (such as in medicine, agriculture, pharmacy, fishery, etc), the collection, summarization and analysis of collected information from the experiments, followed by the interpretation of, and interference from, the results. Biostatistics is a combination of mathematics and reasoning, it considers how research questions are generated, studies are designed, data are collected and results interpreted. If the samples one takes are representative of the population of interest, they will provide good estimates concerning the overall population (Hubert).