What is an outlier in the context of data analysis?

In the context of data analysis, an outlier is defined as a data point that significantly differs from other observations in a dataset. This distinction is important because outliers can occur due to variability in the data, measurement errors, or they may indicate a novel or interesting phenomenon that warrants further investigation.

Identifying outliers is crucial for ensuring the integrity of data analysis, as they can skew results and affect statistical models, such as linear regression. For instance, if a dataset on house prices includes one property selling for an unusually high price due to unique features, this value would be considered an outlier. It could potentially mislead analysts into drawing incorrect conclusions about the overall market trend.

In contrast, other options refer to different concepts. A missing value in the dataset pertains to data that was not recorded, which does not relate to the deviation from typical values. A common observation reflects data points that occur frequently and are expected within the dataset, while a data point with high frequency indicates it is representative of the norm rather than an anomaly. Understanding what constitutes an outlier helps analysts make more robust decisions based on their data.