What characterizes an outlier in a dataset?

An outlier in a dataset is characterized as a data point that is significantly distant from other observations. This means that it deviates considerably from the pattern or distribution of the rest of the data. Outliers can occur due to variability in the data or may indicate a measurement error, experimental error, or a novel occurrence that is worth investigating further. Identifying outliers is important because they can affect statistical analyses and skew results, offering insights into anomalies within a dataset.

In contrast to outliers, the other choices describe characteristics of data points that are more aligned and typical of the overall distribution of the dataset. A data point similar to other observations or representing average values indicates that it falls within the expected range of the dataset, while a common observation suggests it occurs frequently and does not stand out. Understanding the nature of outliers helps in making informed decisions and ensuring data integrity during analysis.