What best distinguishes descriptive statistics from inferential statistics?

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The distinction being tested is between describing the data you have and using that data to generalize to a larger group. Descriptive statistics summarize and describe the data set itself—measures of center, spread, and overall pattern—without making claims about a broader population. Inferential statistics take those sample results and extend them to a population, incorporating sampling uncertainty to make estimates or tests about that larger group.

This statement fits best because it clearly separates summarizing the data from making inferences about a population. For example, you might report the average test score and the variability among the 200 students you sampled (descriptive). But you’d use inferential methods to say something about all students in the district, with a margin of error or a confidence interval.

Other ideas mix up roles: hypothesis testing is a tool used in inferential statistics, not a characteristic of descriptive ones; describing future outcomes is predictive rather than descriptive or inferential; and both descriptive and inferential methods can apply to different data types, so limiting descriptive to qualitative data or inferential to quantitative data isn’t accurate.