# How to calculate trimmed mean

Trimmed mean is a crucial measure of central tendency, often utilized in statistical analysis to minimize the effect of outliers or extreme values on the data. It presents a more accurate representation of the data set by eliminating the highest and lowest values before calculating the mean. This guide will walk you through the process of calculating the trimmed mean for a given data set.

**Steps to Calculate Trimmed Mean**

**1. Choose Your Trim Percentage**

First, decide what percentage of high and low values you want to eliminate from your sample data. This decision may be based on your domain knowledge or specific requirements of your study. The proportion trimmed should be equally distributed at both ends for more balanced trimming.

For example, assuming you want to trim 10% of data points, that means you’ll discard 5% off each end.

**2. Arrange Data Points in Ascending Order**

Sort all data points from your sample in ascending order (smallest to largest). This step makes it easier for you to identify and remove outliers on both ends.

**3. Trim Values from Data Set**

Once you’ve sorted your data points, calculate how many numbers to remove from each end based on your chosen trim percentage. Multiply the trim percentage by the total number of observations (N) and round off to the nearest whole number.

For instance, if there are 100 observations and you want to trim off 10%, multiply this by 0.1 (100 x 0.1 = 10). This means you’ll remove five values from both ends (10 / 2 = 5).

**4. Calculate Remaining Sum**

After trimming the desired number of data points from both ends, sum up the remaining values in your dataset.

**5. Compute Trimmed Mean**

To compute the trimmed mean, divide this sum by the remaining number of observations after trimming (N – trimmed values).

**Examining an Example**

Suppose you have the following dataset and would like to calculate a 10% trimmed mean:

**Data set**: [4, 6, 10, 12, 15, 22, 23, 25, 29, 34]

**1. Choose the percentage to be trimmed:** In this example, we will use 10%.

**2. Arrange the data points in ascending order (smallest to largest):** The data set is already in ascending order.

**3. Trim values from the data set:** Since there are ten data points and we’ve decided on trimming off 10%, we remove one value off each end. The dataset will then look like this:

**Trimmed data:** [6, 10, 12, 15, 22, 23]

**4. Calculate the remaining sum**: Adding up these remaining numbers gives us a sum of (6+10+12+15+22+23) = 88.

**5. Compute the trimmed mean:** Divide this sum (88) by the number of remaining values (6) to obtain a trimmed mean of approximately 14.67.

In conclusion, calculating a trimmed mean is an invaluable way to mitigate outlier effects when dealing with data sets containing high or low extremes. By following these straightforward steps, you can accurately assess central tendency and achieve insightful results in your statistical analysis.