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 a normal distribution and few outliers. The median, on the other hand, is the middle value in a dataset when the numbers are arranged in order. It is more suitable for skewed distributions with outliers, as it is not affected by extreme values.
For example, in a normal distribution like 2, 3, 3, 5, 8, 10, 11, the average is 6, while the median is 5. In a skewed distribution like 2, 2, 3, 3, 5, 7, 8, 130, the average is 20, but the median is 4. This shows how the median can provide a more accurate central tendency in the presence of outliers.
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…
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…