What characterizes an outlier in statistics?

An outlier in statistics is characterized as a data point that significantly differs from other observations in a dataset. Outliers can arise due to variability in the data, measurement errors, or they may indicate a novel phenomenon. Identifying outliers is crucial because they can heavily influence statistical analyses, such as means and standard deviations, potentially skewing results or leading to incorrect conclusions.

Unlike the other options, which either describe common values in a dataset or refer to specific measures of central tendency (like averages or medians), the defining characteristic of an outlier is its relative deviation from the central tendency of the data. This distinction is essential for tasks such as data cleaning and exploring relationships within datasets. Recognizing outliers helps ensure more accurate analyses and interpretations.