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.
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