Statistics Definitions > Trimmed Mean
A mean is an average of a set of data. A trimmed mean (sometimes called a truncated mean) is similar to a mean, but it trims any outliers. Outliers can affect the mean (especially if there are just one or two very large values), so a trimmed mean can often be a better fit for data sets with erratic high or low values or for extremely skewed distributions.
These means are expressed in percentages. The percentage tells you what percentage of data to remove. For example, with a 5% trimmed mean, the lowest 5% and highest 5% of the data are excluded. The mean is calculated from the remaining 90% of data points.
What is the Highest Percentage?
Any percentage of data points can be excluded, although the highest % possible is 50%. If you remove 50 percent of data points from the left and 50 percent of data points from the right you theoretically have zero data points left but for practical purposes you would have the median. Don’t worry though — you probably won’t ever be asked for a trimmed mean that high (what would be the point?!).
How to Find a Trimmed Mean
Example: Find the trimmed 20% mean for the following test scores: 60, 81, 83, 91, 99.
Step 1: Trim the top and bottom 20% from the data. That leaves us with the middle three values:
60, 81, 83, 91, 99.
Step 2: Find the mean with the remaining values. The mean is (81 + 83 + 91) / 3 ) = 85.
Warning: This is one of those challenging terms that seem to have different meanings to different authors. Perform a Google search for trimmed mean and you’ll find some authors who state a trimmed mean of 10% is where a TOTAL of 10% of data points is removed (i.e. 5% from either side) as opposed to trimming 10% from each side. Your teacher/professor/boss will likely use the term exactly as it’s stated in this article, but you may want to clarify before you actually do any calculations.
If you prefer an online interactive environment to learn R and statistics, this free R Tutorial by Datacamp is a great way to get started. If you're are somewhat comfortable with R and are interested in going deeper into Statistics, try this Statistics with R track.Comments? Need to post a correction? Please post on our Facebook page.