Descriptive statistics are primarily used to:

Descriptive statistics play a crucial role in summarizing and portraying the characteristics of a dataset. They provide a way to present data in a clear and understandable manner, using measures such as mean, median, mode, and standard deviation to convey insights about the distribution, central tendency, and variability within the data.

By focusing on summarizing data features, descriptive statistics allow researchers to gain a foundational understanding of their dataset, enabling them to identify patterns, trends, and potential anomalies at a glance. This is particularly important in sociology, where understanding the distribution of social phenomena can lead to deeper insights into societal structures and behaviors.

Other options relate to different statistical approaches. Making predictions about future trends generally falls under inferential statistics, which involve using sample data to infer conclusions about a larger population. Testing hypotheses often requires experimental data analysis, where researchers assess the validity of their proposed explanations for observed phenomena. Generating qualitative insights typically involves thematic analysis or grounded theory, which are more concerned with the richness of data and human experiences rather than numerical summaries.