# Mean absolute deviation (MAD) review

## Mean absolute deviation

The mean absolute deviation of a dataset is the average distance between each data point and the mean. It gives us an idea about the variability in a dataset.

Here's how to calculate the mean absolute deviation.

**Step 1:**Calculate the mean.

**Step 2:**Calculate how far away each data point is from the mean using positive distances. These are called absolute deviations.

**Step 3:**Add those deviations together.

**Step 4:**Divide the sum by the number of data points.

Following these steps in the example below is probably the best way to learn about mean absolute deviation, but here is a more formal way to write the steps in a formula:

### Example

Erica enjoys posting pictures of her cat online. Here's how many "likes" the past pictures each received:

, , , , ,

**Find the mean absolute deviation.**

**Step 1:**Calculate the mean.

The sum of the data is total "likes" and there are pictures.

The mean is .

**Step 2:**Calculate the distance between each data point and the mean.

Data point | Distance from mean |
---|---|

**Step 3:**Add the distances together.

**Step 4:**Divide the sum by the number of data points.

likes

On average, each picture was about likes away from the mean.

*Want to learn more about mean absolute deviation? Check out this video*.

### Practice problem

*Want to practice more problems like these? Check out this exercise*.