A trimmed mean is a robust measure of central tendency that is less sensitive to outliers than the traditional mean. To calculate the trimmed mean, you first order the data from smallest to largest. Then, you remove a specified percentage of the smallest and largest values from the dataset. Finally, you calculate the mean of the remaining values.
The trimmed mean is a useful statistic when you have a dataset that contains outliers. Outliers are data points that are significantly different from the rest of the data. They can be caused by measurement errors or by unusual events. Outliers can have a large effect on the mean, making it an unreliable measure of central tendency in these cases.
The trimmed mean is less sensitive to outliers than the mean because it removes the extreme values from the dataset. This makes it a more reliable measure of central tendency when there are outliers present.
The trimmed mean is often used in statistics and data analysis. It is particularly useful in fields such as finance and economics, where outliers can have a large impact on the analysis.
Here are some of the advantages of using the trimmed mean:
- It is less sensitive to outliers than the mean.
- It is a more robust measure of central tendency.
- It is a more reliable measure of central tendency when there are outliers present.
A trimmed mean is the mean of a dataset that has been calculated after removing a specific percentage of the smallest and largest values from the dataset.
To find the trimmed mean of a dataset, simply enter a list of the comma-separated values for the dataset along with the percentage of values to trim, then click the “Calculate” button:
Dataset values:
Trimmed Mean Percentage (%):
Trimmed Mean: 27.0833