3. Outlier

An outlier is a data point that is significantly different from the other data points in a dataset. It is an observation that lies an abnormal distance away from other values in a random sample from a population. Outliers can occur due to various reasons, such as measurement errors, natural variations in the data, or extreme values in the underlying population.

Outliers can have a significant impact on statistical analyses and can skew the results if not handled properly. Therefore, it is important to identify and handle outliers appropriately based on the context and purpose of the analysis.

There are different approaches to detecting outliers, including visual inspection of the data using scatter plots or box plots, or using statistical methods such as the z-score or the interquartile range (IQR) method. Once outliers are identified, they can be either removed from the dataset or treated separately, depending on the analysis goals and the reasons for their occurrence.

It is worth noting that outliers may sometimes be valid and meaningful data points, indicating rare events or important phenomena. Therefore, it is crucial to thoroughly understand the data and the context before deciding how to handle outliers in any given analysis.