What does descriptive statistics primarily focus on?

Descriptive statistics primarily focuses on summarizing and describing the features of a dataset. This branch of statistics provides a means to present quantitative descriptions in a manageable form, enabling a clearer understanding of the data's main characteristics. Consequently, it involves measures such as mean, median, mode, standard deviation, and various graphical representations like histograms and pie charts. These tools help in conveying the central tendency, dispersion, and overall distribution of data, making it easier to interpret and analyze without making inferences beyond the data presented.

In contrast, making predictions about future trends relates more closely to inferential statistics, where models are built to forecast outcomes based on existing data. Similarly, analyzing relationships between variables is typically the domain of correlational or regression analysis, which seeks to identify how changes in one variable may affect another. Lastly, testing hypotheses about data is a key function of inferential statistics as well, aiming to determine the validity of assumptions made about population parameters based on sample data. Thus, the option that correctly identifies the focus of descriptive statistics is the one concerning the summarization and description of dataset features.