Using the median in reports is beneficial when dealing with skewed data or outliers. For instance, when reporting on full resolution time, using the median can provide a clearer picture because some tickets may have been under investigation for a long time, skewing the average.
Similarly, for metrics like first reply time, where the data is generally consistent, the median can help filter out anomalies, such as proactive tickets created by agents that might have a high first reply time. These examples show how the median can offer a more accurate representation of central tendency in certain scenarios.
The average and median are both measures of central tendency, but they differ in how they handle data. The average, or mean, is calculated by adding all the values in a dataset and dividing by the number of values. It is best used for datasets with…
You should use the median instead of the average when your dataset contains outliers or is skewed. The median is a better measure of central tendency in these cases because it is not influenced by extreme values, unlike the average. For instance,…
Calculating the average and median involves different methods. To find the average, add up all the values in your dataset and divide the sum by the total number of values. This gives you the arithmetic mean, which is useful for normally distributed…
The average and median can differ significantly in datasets with outliers or skewed distributions. The average is sensitive to extreme values, which can inflate or deflate the mean, making it less representative of the central tendency. For…