It's the selection of observables to make predictions about unobservables. Sampling, at bottom, is a matter of reducing, of simplifying. There is no automatic need for large sample sizes, and the type of questions asked or relationships predicted will, in part, help determine the sampling plan. If one is going to infer causality, then random sampling , or some variant, is warranted. In general, the more one knows about the wider population or context of the research problem, the easier it is to justify use of nonprobabilistic sampling.
Measurement is a research step related to sampling and the estimates derived from it.
In general, one should attempt to obtain measures that are meaningful, and this means interval or ratio level , especially if one is going to infer causality. Validity and reliability refer, respectively, to whether one is measuring what one intends to measure and if one is doing it consistently. These qualities of research, as well as the general idea of sampling, demonstrate inference by streamlining a project into manageable and meaningful proportions. Design issues depend, in large part, upon the expertise and creativity of the researcher. What one wants is a good tradeoff between a parsimonious design and one that provides the highest level of confidence.