UGC NET Questions (Paper – 1)

Statements A, B, C and D correctly distinguish internal and external validity and the roles of random assignment and random sampling. Internal validity is about attributing effects to the treatment rather than confounds, while external validity is about generalising beyond the study context. Random assignment strengthens internal validity by equalising extraneous variables across groups, and random sampling supports external validity by making the sample more representative. Statement E is false because a study can have strong internal validity yet limited generalisability, so high internal validity does not guarantee high external validity.

Option A:
Option A is incomplete because it omits D, which correctly highlights the relation between random sampling and external validity. Without D, the link between sampling and generalisability is not fully represented.

Option B:
Option B leaves out statement A, overlooking the fundamental definition of internal validity as ruling out alternative explanations. Even though B and D address external validity and sampling, omitting A weakens the conceptual pair.

Option C:
Option C is correct because it includes all four accurate statements about internal and external validity and their methodological supports. It deliberately excludes E, which wrongly assumes that internal and external validity always go together. Thus this combination precisely reflects textbook distinctions.

Option D:
Option D is incorrect because it includes E, which overstates the relationship between internal and external validity, and it omits B, which defines external validity itself. This mixture of omission and error makes the option unacceptable.

Option E is also wrong because it incorporates E, the false automatic link between internal and external validity. A correct option must not contain this overgeneralisation.

Statements A, B, C, E and F together provide a sound comparison between experimental and survey research. A is true because manipulation of an independent variable is central to experiments, and B correctly notes that surveys often use structured questionnaires with relatively large samples. Statement C is accurate in highlighting that experiments focus on cause–effect links, while E is true since both designs may use random sampling. Statement F reflects that surveys are commonly used to study attitudes, opinions and self-reported behaviours, whereas D is false because surveys are usually conducted in natural or field settings rather than always in laboratories.

Option A:
Option A excludes statement F, even though surveys routinely investigate attitudes and self-reports, making F a correct statement. Omitting F means that the description of survey research is incomplete and does not fully portray its scope. Therefore Option A cannot be the correct answer.

Option B:
Option B leaves out statement E, which recognises that random sampling can be applied to both types of designs. While A, B, C and F are correct, failing to include E results in a partial characterisation of sampling possibilities. This makes the combination incomplete.

Option C:
Option C omits statement A, ignoring the basic defining feature of experimental research, namely manipulation of an independent variable. It also includes B, C, E and F but fails to capture the full set of true statements about both designs. Hence this option is not acceptable.

Option D:
Option D is correct because it includes A, B, C, E and F, all of which are accurate descriptions of experimental and survey research. It properly excludes statement D, which wrongly confines surveys to laboratory contexts. As a result, this combination presents all and only the true statements in the item.

Probability sampling methods ensure that every element in the population has a known, non zero chance of being selected. Simple random sampling is a fundamental probability method where each member has an equal chance of inclusion, often achieved by lottery or random number procedures. This enhances representativeness and allows the use of statistical inference.

Option A:
Option A, convenience sampling, is a nonprobability method where respondents are chosen purely on ease of access. There is no known probability of selection for each member of the population, so it does not qualify as probability sampling.

Option B:
Option B, purposive sampling, involves selecting cases based on researcher judgment and is also nonprobability in nature. It is suitable for specialized studies but does not provide equal chance to all population elements.

Option C:
Option C clearly matches the definition of probability sampling because it selects from a complete list using random procedures. This ensures each unit has a known probability of selection and supports generalization within calculated error limits.

Option D:
Option D, quota sampling, fixes proportions for certain categories but usually relies on convenience within those categories. Because selection within quotas is not random, it is considered nonprobability sampling.

Random sampling gives each unit in the population a known, often equal, chance of being selected, which makes the selection process impartial. This greatly reduces systematic bias in the composition of the sample because personal judgment and convenience are minimised. While it does not completely eliminate all error, it helps ensure that differences between the sample and population are mainly due to chance. Therefore, the correct effect on selection bias is that random sampling tends to reduce it.

Option A:
If random sampling were to increase bias, it would defeat its main purpose in scientific research. The entire rationale for using random methods is to minimise systematic distortions in the sample. Thus, the option suggesting an increase in bias is conceptually incorrect.

Option B:
Random sampling reduces the likelihood that any particular type of individual is overrepresented or underrepresented for systematic reasons. It supports the use of probability theory to estimate sampling error and build confidence intervals. These features correspond directly to the idea of reducing selection bias as stated in the question.

Option C:
Creating bias would mean deliberately skewing the sample in favour of certain subgroups, which is characteristic of convenience or judgement sampling, not random sampling. Therefore, the suggestion that random procedures create bias is misleading.

Option D:
Ignoring bias does nothing to protect the quality of inferences and would be a sign of poor methodological practice. Random sampling is chosen precisely because it addresses concerns about bias rather than ignoring them, so this option does not fit the role described in the stem.