Inference from Samples

The very idea of sampling is meaningful only when the popula­tion is large. When the population is very large, sampling is done to economize on time and expense. When the population involved is not only very large but also not practically accessible, as, for example, when finding the average height of tenth-grade boys in the United States, sampling is unavoidable. Then, we are left with the only choice of drawing the inferences about the pop­ulation, based on the statistical measures of samples. In drawing inferences from sampling and applying those to the entire popu­lation lies the strength of probability and statistics. When the entire population is accessible and reasonably small, computa­tions for the population of such statistical measures as mean, vari­ance, and standard deviation are done using the same formulas as in case of samples. But to distinguish statistical measures of sam­ples from those of the population, different sets of symbols, shown in Table 17.1, are very often used in literature.

Source: Srinagesh K (2005), The Principles of Experimental Research, Butterworth-Heinemann; 1st edition.