Mean deviation is a statistical measure that is used to describe the dispersion of a dataset. It is calculated by taking the absolute difference between each data point and the mean of the data, and then taking the average of those differences.

For example, let’s say we have a dataset of 5 numbers: 1, 2, 3, 4, and 5. The mean of this dataset is 3. To calculate the mean deviation, we first need to find the absolute difference between each data point and the mean. This would give us:

|1 – 3| = 2

|2 – 3| = 1

|3 – 3| = 0

|4 – 3| = 1

|5 – 3| = 2

Next, we take the average of these differences, which gives us a mean deviation of 1.2. This tells us that, on average, the data points in our dataset are 1.2 units away from the mean.

Another example of mean deviation is if we have a dataset of stock prices over a period of time. Let’s say the mean stock price is $100. If we calculate the mean deviation, it would tell us how much the stock prices deviate from the mean on average. For instance, if the mean deviation is $10, it means that the stock prices typically fluctuate by $10 from the mean.

Overall, mean deviation is a useful measure for understanding the dispersion of a dataset. It can help us understand how much the data points vary from the mean and give us a sense of the spread of the data. This can be useful for making predictions or making decisions based on the data.