What is the definition of an outlier in a dataset?

An outlier is defined as a data point that significantly deviates from other observations in a dataset. This means that it lies outside the typical range of values and can be either much higher or much lower than the majority of data points. Outliers are important to identify because they can affect the results of data analysis, statistical calculations, and machine learning models. Understanding their implications helps in making informed decisions on whether to include or exclude them based on the context of the analysis.

For example, if a dataset represents the ages of participants in a study, and one participant is 100 years old while the rest are between 20 to 40 years, the 100-year-old can be considered an outlier. This significant difference can potentially skew the average age of the group. Therefore, recognizing outliers helps in ensuring that analyses derive useful and accurate insights.