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.
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