UGC NET Questions (Paper – 1)

P-hacking refers to manipulating data analysis or selectively reporting results to obtain statistically significant p-values, often without a strong theoretical rationale. Researchers may try multiple models, subsets or transformations and only present analyses that produce significant findings. This practice inflates Type I error rates and undermines the credibility of research. Because the stem describes repeatedly analysing the same data until significance is found, p-hacking is the correct term.

Option A:
Stratification is a sampling technique that divides a population into homogeneous subgroups for more precise representation and is unrelated to manipulating analyses for significance. It is a legitimate methodological procedure rather than a questionable practice. Hence, stratification does not fit the stem.

Option B:
Triangulation involves using multiple methods, data sources or investigators to improve the credibility of findings and is generally considered a strength, not an unethical practice. It seeks converging evidence rather than repeated fishing for significance. Therefore, triangulation is not appropriate here.

Option C:
Pruning may refer to trimming decision trees or simplifying models in statistical or machine learning contexts, and is not commonly used to describe unethical searching for significant results. It also does not capture the selective reporting dimension implied in the stem. Thus, pruning is not the correct completion.

Option D:
P-hacking undermines the integrity of scientific literature because published significant findings may simply reflect exploratory searching rather than real effects. It highlights the need for transparent preregistration and replication. These concerns align with the scenario described in the question, confirming p-hacking as the accurate term.

Statements A, B, C, D and E describe core features and concerns of systematic reviews and meta-analyses. A is true because systematic reviews use explicit protocols for searching and synthesising evidence, and B is correct that meta-analysis statistically combines results. C is accurate in emphasising transparent inclusion and exclusion criteria, while D rightly points to publication bias as a threat. E is also true since effect sizes are typically weighted by sample size or precision, whereas F is false because narrative reviews vary widely in rigour and often lack the structured protocol of systematic reviews.

Option A:
Option A is correct as it brings together all the statements that match standard descriptions of systematic reviews and meta-analyses and excludes F, which overstates the rigour of narrative reviews. It therefore contains all and only the true statements.

Option B:
Option B wrongly adds statement F, implying that narrative reviews always match the rigour of systematic approaches. In reality, many narrative reviews are selective and unsystematic. Including F makes the combination invalid.

Option C:
Option C omits statement B, ignoring the defining statistical element of meta-analysis, namely combining quantitative results across studies. Without B, the explanation fails to capture a key aspect of research synthesis.

Option D:
Option D excludes statement A and thus neglects the procedural foundation of systematic reviews. Although it includes B, C, D and E, such an omission makes the description incomplete and the option incorrect.