What are "outliers" in data analysis?

Outliers in data analysis are defined as data points that differ significantly from other observations in the dataset. These are values that lie far away from the majority of data points, which can indicate variations due to measurement error, variability in the data, or potentially interesting phenomena that warrant further investigation.

Identifying outliers is crucial because they can have a substantial impact on statistical analyses, such as the calculation of the mean and standard deviation. In many cases, the presence of outliers may distort the results of analytical processes, leading to incorrect conclusions. For example, in a dataset of human heights, a height measurement of a person who is abnormally tall or short compared to the average may be classified as an outlier. This specific characteristic helps researchers identify anomalies, errors, or significant variations that might reveal deeper insights into the data or require attention for predictive modeling.

The other options do not accurately describe outliers. For instance, data points consistent with the dataset would not be considered outliers, as they align with the expected range of values. The average of the dataset refers to a central tendency measurement, not an outlier. Missing data points are simply non-existent in the dataset, and therefore cannot be classified as outliers. Understanding how to identify and interpret