What is the primary purpose of descriptive statistics?

The primary purpose of descriptive statistics is to summarize and describe the main features of a dataset. This involves using various metrics such as measures of central tendency (mean, median, mode) and measures of variability (range, variance, standard deviation) to convey essential characteristics of the data in an understandable format. By summarizing the data, descriptive statistics provide a way to present a large amount of numerical information in a compelling way that highlights trends, patterns, and exceptions within the dataset. This foundational understanding allows researchers and practitioners to grasp the underlying structure and nature of the data before moving into more complex analyses.

Other options relate to different aspects of statistics. For instance, predictive insights involve inferential statistics that assess potential future outcomes based on existing data, which is not the role of descriptive statistics. Identifying causes of observed phenomena typically aligns with inferential statistics or experimental design that aims to establish understanding through hypothesis testing. Finally, establishing a theoretical framework is more associated with qualitative research or theoretical developments rather than the summarization and description of existing data, which is the focus of descriptive statistics.