In any research study, the population is the complete set of units about which the researcher wants to draw conclusions. Because studying all units is often impractical, a smaller group called a sample is selected from this population. The sample should represent the population so that inferences from sample data can be generalized appropriately. This distinction is fundamental to sampling theory.
Option A:
Option A incorrectly claims that population is always smaller than sample, which is logically impossible because a sample is drawn from the population. Samples are subsets and therefore cannot exceed the population in size.
Option B:
Option B clearly describes population as the full group of interest and the sample as a subset selected for actual study. This matches standard methodological definitions and underscores the logic of generalizing from sample to population.
Option C:
Option C states that sample and population are identical, which contradicts the very meaning of sampling. If they were identical, there would be no need to use sampling techniques.
Option D:
Option D incorrectly restricts the term “sample” to qualitative data and “population” to quantitative data. In reality, both populations and samples can involve any type of data; the distinction is based on whether we consider all units or only a subset.
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