What is an outlier in data analysis?

An outlier in data analysis is defined as a data point that significantly differs from the rest of the data within a dataset. This difference can manifest as a value that is much higher or lower than the other observations. Outliers can arise due to variability in the data, measurement errors, or they may indicate a novel situation that requires further investigation.

Identifying outliers is crucial because they can skew statistical analyses and might affect the results of calculations such as mean, standard deviation, and others. By recognizing and appropriately handling outliers, analysts can ensure more reliable insights from their data.

The other definitions provided are not accurate representations of what an outlier is. A common data point represents regular trends or values, while a data point identical to others would indicate a lack of variability. A typical average value, likewise, describes a central tendency in the data rather than a deviation from it. Understanding the concept of outliers allows data analysts to make more informed decisions based on the data they are evaluating.