What is the term for a data point that falls far from the rest of the data in a dataset?

An outlier is a data point that falls far from the rest of the data in a dataset.

Outliers can occur due to variability in the data or due to measurement errors.

They can significantly affect the results of data analysis and statistical modeling.

Identifying and handling outliers is an important step in the data cleaning process.

Here are some key points about outliers: Identification: Outliers can be identified using statistical methods such as the Z-score or the IQR (Interquartile Range) method.

Impact: Outliers can skew the results of statistical analyses, such as mean and standard deviation, and can also affect machine learning models.

Handling: Once identified, outliers can be handled by removing them, transforming them, or using robust statistical methods that are less sensitive to outliers.

Types: Outliers can be classified into univariate outliers, which are outliers in a single variable, and multivariate outliers, which are outliers in the context of multiple variables.