IBM Data Science Practice Test

The main purpose of descriptive statistics is indeed to summarize and describe the characteristics of a dataset. This branch of statistics provides essential insights into the data by utilizing measures such as mean, median, mode, range, and standard deviation. These measures give a comprehensive overview of the data's central tendency, variability, and distribution shape, making it possible to understand the data's overall pattern and characteristics at a glance.

Descriptive statistics are fundamental in data analysis as they help researchers and analysts frame their observations based on actual data without making predictions about the underlying population or future trends. It gives a clear and concise summary of what the data presents, enabling better interpretations and informed decision-making.

While comparing two sets of data is useful, it falls under the realm of inferential statistics, which often requires statistical tests. The prediction of future data trends aligns more with predictive analytics rather than descriptive statistics, while visualization is a complementary tool that can help present descriptive statistics but is not the primary function of this statistical approach. Hence, summarizing and describing characteristics remains the core focus of descriptive statistics.