UGCNET Questions

Quantitative research relies on numerical data that must be organized and interpreted systematically. Statistical techniques allow researchers to describe patterns, relationships and variability in the data. Through procedures such as estimation, significance testing and confidence intervals, researchers can test hypotheses and draw inferences about populations from samples. Thus statistical analysis is central to quantitative data interpretation.

Option A:
Option A minimizes the use of numerical structure by emphasizing personal impressions, which undermines objectivity. While interpretation is necessary, it should be grounded in statistical evidence.

Option B:
Option B correctly states that quantitative analysis uses statistics for both description and inference. It captures the dual roles of summarizing data and testing hypotheses, reflecting standard practices in quantitative methodology.

Option C:
Option C suggests ignoring numerical patterns, which defeats the purpose of collecting quantitative data. Qualitative description has its place but cannot substitute for statistical analysis in quantitative studies.

Option D:
Option D indicates selective use of data to support preconceived opinions, which is unethical and methodologically flawed. Proper analysis should consider all relevant data, not just convenient subsets.

Statistical analysis in quantitative research serves several purposes. Descriptive statistics summarize data through measures like mean, median and standard deviation. Inferential statistics help examine relationships and differences, and test hypotheses about populations based on sample data. Together, these techniques provide an evidence based basis for interpreting findings.

Option A:
Option A suggests replacing numbers with only qualitative description, which ignores the advantages of numerical precision and patterns that statistics reveal.

Option B:
Option B is correct because it combines summarization, exploration of relationships and hypothesis testing, which are central tasks of quantitative data analysis. It recognizes that statistics support but do not replace substantive interpretation.

Option C:
Option C claims that statistics eliminate the need for interpretation, which is false. Researchers must still relate numerical results to theory, context and practical implications.

Option D:
Option D assumes automatic acceptance of hypotheses, whereas statistical testing may lead to either rejection or failure to reject, depending on evidence.

Measures of dispersion indicate the extent to which scores differ from each other and from the central tendency. Standard deviation summarizes the average distance of scores from the mean; a larger value implies greater variability, while a smaller one indicates tighter clustering.

Option A:
This option is correct because standard deviation directly quantifies spread around the mean. It is routinely reported in research articles to describe variability and underpins many inferential statistics.

Option B:
This option, the arithmetic mean, is a measure of central tendency, not dispersion. It reports the average level of scores but not how spread out they are.

Option C:
The median is also a measure of central tendency. It indicates the middle score but does not describe whether the other scores are close together or widely scattered.

Option D:
The mode shows which score occurs most frequently, but it gives no information about how far other observations deviate from that value, so it is not a measure of dispersion.