Sampling: Sampling is the selection of a subset (a statistical sample) of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt for the samples to represent the population in question. There are two types of sampling, probability sampling, and non-probability sampling. It is a subset drawn from a larger set to represent it. The characteristics of a 'good sample' in research: Goal-oriented: oriented to the research objective and fit to the survey and data collection process A true representative of the universe: Representative of population characteristics Proportion: it is not as large as the universe but large enough to understand the population characteristics Practical: simple and follow the field condition Accuracy and precision: it is valid from every perspective The sample design should be such that it results in a small sampling error It should be viable in the context of budgetary constraints of the research study In a good sampling, the systematic bias can be controlled. There should be freedom from bias. The sample must be such that the results of the sample study would be applicable, in general, to the universe at a reasonable level of confidence.