The assumed mean takes a ballpark guess at the mean, then uses math to calculate a number close to the mean. It is assumed because it is not an actual mean calculation. It is important to remember that the only time you should use assumed mean is if you have very small amounts of data (i.e. less than 20 entries) in your data set.

1. Sort Data

Sort your data set from smallest to largest. For example, assume your data set is 43, 45, 46, 48 and 49.

2. Assume Mean

Assume a mean. This should be a number that you feel is a close representation of your data set. In a simple example, take the number in the center of your data set; in this case 46.

3. Subtract Assumed Mean

Subtract your assumed mean from each data entry. In the example:

(43 – 46 = -3, 45 – 46 = -1, 46 – 46 = 0, 48 – 46 = 2 text{ and } 49 – 46 = 3)

4. Add Difference

Add together each difference from the mean. In the example,

((-3) + (-1) + 0 + 2 + 3 = 1)

5. Divide by Number of Data Points

Divide the sum of the differences from assumed mean by number of data points. In the example,

(1 ÷ 5 = 0.2)

6. Add Result to Assumed Mean

Add the result of the division to your assumed mean. In the example:

(text{Assumed mean} = 46 + 0.2 = 46.2)

TL;DR (Too Long; Didn’t Read)

“Assumed mean” is also known as “average.”