An outlier is a data point that lies an abnormal distance from other values in a random sample from a population. In other words, it is an extremely high or extremely low value that stands out greatly from the overall pattern of values in a dataset or graph. Outliers can have a big impact on statistical analyses and skew the results of any hypothesis test if they are inaccurate. It is important to identify outliers in a dataset to ensure that they are not errors or bad data. Outliers can be identified using various methods, including:

• Sorting: Sorting values from low to high and checking minimum and maximum values.
• Visualizing: Using a box and whiskers chart (boxplot) to show outliers.
• Calculating: Using the interquartile range (IQR) to calculate the lower and upper bounds for outliers.

It is important to investigate outliers carefully, as they may contain valuable information about the process under investigation or the data gathering. However, outliers that do not represent true values can come from measurement errors, data entry or processing errors, or unrepresentative sampling. Therefore, it is necessary to determine the source of the outlier and decide whether to keep or remove it from the dataset.