Which measure is used to identify outliers in data?

The interquartile range (IQR) is a robust measure used to identify outliers in data. It is calculated by taking the difference between the third quartile (Q3) and the first quartile (Q1), which gives the range of the middle 50% of the data.

To detect outliers using the IQR, boundaries are established: any data points that fall below Q1 - 1.5 * IQR or above Q3 + 1.5 * IQR are considered outliers. This method is particularly effective because the IQR is not affected by extreme values or the distribution shape, making it a reliable indicator for spotting anomalies within the dataset.

Other measures like the mean and standard deviation are useful in descriptive statistics and identifying characteristics of data distributions, but they can be sensitive to outliers themselves, leading to misleading interpretations. The median is a measure of central tendency but does not provide information about the spread or range of the data, nor does it have a specific method for identifying outliers like the IQR does.