Statistics Definitions > Mean Error

## What is Mean Error?

**The mean error is an informal term **that usually refers to the average of all the errors in a set. An “error” in this context is an uncertainty in a measurement, or the difference between the measured value and true/correct value. The more formal term for error is measurement error, also called observational error.

## Why It’s Seldom Used

**The mean error usually results in a number that isn’t helpful** because positives and negatives cancel each other out. For example, two errors of +100 and -100 would give a mean error of zero:

**mean = sum of all values/number in the set**

= (+100 + -100) / 2

**= 0 / 2 = 0.**

Zero implies that there is no error, when that’s clearly not the case for this example.

## Use the MAE Instead!

To remedy this, **use the mean absolute error (MAE) instead.** The MAE uses absolute values of errors in the calculations, resulting in average errors that make more sense.

The formula looks a little ugly, but all it’s asking you do do is:

- Subtract each measurement from another.
- Find the absolute value of each difference from Step 1.
- Add up all of the values from Step 2.
- Divide Step 3 by the number of measurements.

For a step by step example, see: mean absolute error.

## Other Terms that are Very Similar

While the “mean error” in statistics *usually* refers to the MAE, it could also refer to these closely related terms:

**Mean absolute deviation**(average absolute deviation): measures the average standard deviation, which is a spread of values around the center of a data set. The terms sound similar, but they have practically nothing to do with each other because a*standard deviation*is a unit of spread and an*error*is a difference in unit measurements.**Mean squared error**: used in regression analysis to show how close a regression line is to a set of points. “Errors” in this context are distances from the regression line.