The study of statistics refers to the collection, presentation, analysis, and utilization of numerical data to infer and make decisions in the face of uncertainty, where the actual population data is unknown. There are two branches in the study of statistics: descriptive statistics, where data is summarized and described, and inferential statistics, where the population is generalized through a small random sample, making it useful for making predictions or decisions when the population characteristics are unknown.
A sample can be defined as a subset of the population being measured, while the population can be defined as all possible observations of interest of a variable. For instance, if one is interested in the voting practices of all U.S. registered voters, the entire pool of a hundred million registered voters is considered the population while a small survey of one thousand registered voters taken from several small towns across the nation is the sample. The calculated characteristics of the sample (e.g., mean, median, standard deviation) are termed statistics, while parameters imply that the entire population has been surveyed and the results tabulated. Thus, in decision making, the statistic is of vital importance considering that sometimes the entire population is yet unknown (e.g., who are all your customers, what is the total market share, and so forth) or it is very difficult to obtain all relevant information on the population because it would be too time- or resource-consuming.
In inferential statistics, the following are the usual steps in conducting research:
