Which of the following best describes the term "anomalies" in exploratory data analysis?

The term "anomalies" in exploratory data analysis refers specifically to unusual data points that deviate from expected patterns. These anomalies, also known as outliers, can indicate variability in the data, a different underlying process, or even errors in data collection. Identifying these anomalies is crucial because they can have significant implications for data analysis and modeling. They may represent rare events, require further investigation, and can influence aggregated metrics if not appropriately addressed.

In contrast, common occurrences in datasets suggest a level of frequency or normality that does not align with the concept of anomalies. Patterns that confirm existing theories represent typical findings that reinforce current understanding rather than highlight deviations. Average data values that represent the norm indicate the central tendency of a dataset, which again contrasts with the idea of anomalies representing exceptional cases. Recognizing and interpreting anomalies allows analysts to gain deeper insights and improve the accuracy of their conclusions.