What distinguishes descriptive statistics from inferential statistics?

Descriptive statistics are fundamentally focused on summarizing and describing the features of a dataset. They provide a way to present large amounts of data in a more manageable form, often using measures such as means, medians, modes, ranges, and standard deviations. By organizing and simplifying data, descriptive statistics allow researchers to understand patterns and trends within the data itself without making broader conclusions or predictions about a larger population.

This process is distinct from inferential statistics, which are used to make predictions or generalizations about a population based on sample data. Inferential statistics involve the application of statistical tests and models that help researchers draw conclusions or test hypotheses beyond the data at hand.

Other options like predicting population behavior and providing findings based on a hypothesis pertain to inferential statistics, which aim to extend results beyond the immediate dataset. Similarly, the assertion that inferential statistics only work with qualitative data is inaccurate, as inferential statistics can apply to both qualitative and quantitative data. Therefore, the distinguishing feature of descriptive statistics is that they summarize and describe the data being analyzed.