The correct answer is: B. An unusually extreme data point that deviates from the overall pattern.
An outlier is a data point that is significantly different from the other data points in a set. Outliers can be caused by errors in data collection, measurement, or processing, or they may represent genuine but unusual values. Outliers can be difficult to identify, and there is no single definition of what constitutes an outlier. However, there are a number of methods that can be used to identify outliers, such as box plots, interquartile range, and z-scores.
Outliers can have a significant impact on the results of statistical analysis. If outliers are not properly identified and handled, they can bias the results of the analysis. There are a number of ways to handle outliers, such as removing them from the data set, replacing them with more representative values, or using robust statistical methods that are less sensitive to outliers.
In data science, outliers are often used to identify potential problems with the data. For example, if an outlier is found in a data set that represents the heights of people, it may indicate that the data set is incomplete or that there is an error in the measurement process. Outliers can also be used to identify interesting patterns in the data. For example, if an outlier is found in a data set that represents the prices of houses, it may indicate that there is a unique property in the data set that is worth more than the other properties.
Outliers can be a valuable tool for data scientists, but they can also be a source of confusion and error. It is important to understand what outliers are, how to identify them, and how to handle them.