UGC NET Questions (Paper – 1)

Statement D is wrong because probability sampling typically requires a sampling frame or some representation of the population to ensure known selection probabilities. Statements A, B, C and E are correct: the sampling frame is the list from which samples are drawn, its quality affects representativeness, outdated frames cause coverage errors and an ideal frame covers the entire target population. Therefore, the only wrong statement is D.

Option A:
Option A is incorrect because it also labels A, which correctly defines the sampling frame, as wrong along with D. This misrepresents a basic concept in sampling.

Option B:
Option B is wrong because it groups C with D as wrong, even though C accurately states that outdated frames cause coverage errors. Coverage error is a standard threat to representativeness.

Option C:
Option C is correct because it isolates D as the sole statement inconsistent with probability sampling principles and implicitly accepts A, B, C and E as true.

Option D:
Option D is incorrect because it treats B and E as wrong along with D, despite both B and E properly describing the relationship between frame quality and sample representativeness.

The population in research is the complete set of individuals, events or objects that share certain characteristics and about which the researcher wants to draw conclusions. It forms the universe from which the sample is selected. When findings from the sample are generalised, they are said to apply to this population. Because the stem describes the entire group to which results are to be generalised, the correct term is population.

Option A:
A sample is a subset of the population selected for actual observation or measurement. While inferences are drawn from the sample, the broader group that these inferences target is the population. Since the question explicitly refers to the entire group, not the subset, sample is not the correct choice.

Option B:
An element is a single member of the population, such as one individual respondent or one school. It does not refer to the whole group to which the researcher wishes to generalise. Therefore, element cannot complete the stem properly.

Option C:
A stratum is a homogeneous subgroup of the population created when using stratified sampling. Each stratum contains members who share certain characteristics, but it is only a part of the population, not the entire group. Consequently, stratum is not the right answer.

Option D:
Population encompasses all units that meet the specified criteria, whether they are people, institutions or events. Identifying the population clearly is crucial for designing sampling strategies and interpreting generalisations. This broad target group is exactly what the stem refers to, so population is the appropriate completion.

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.