TRIMMEAN Google Sheets function

The TRIMMEAN function in Google Sheets is a powerful statistical tool that allows users to compute the mean of a dataset by excluding a certain percentage of both the highest and lowest values. This helps in obtaining a more accurate representation of the central tendency by eliminating outliers that could skew the results.

Syntax

TRIMMEAN(data, percent)
  • data: The range of cells or array containing the numbers from which the mean will be calculated.
  • percent: The percentage of data points to exclude from each end of the dataset, expressed as a decimal. For example, to exclude 10%, you would use 0.1.

Example #1

=TRIMMEAN(A1:A10, 0.2)
This function calculates the mean of the values in the range A1 to A10 while omitting the lowest 20% and the highest 20%. For instance, if A1:A10 contains the values {10, 20, 30, 40, 50, 60, 70, 80, 90, 100}, the TRIMMEAN would yield a result of 55.

Example #2

=TRIMMEAN(B1:B5, 0.4)
In this example, the function computes the mean of the values in B1 through B5, excluding 40% of both the extremes. If B1:B5 holds the numbers {2, 3, 4, 5, 100}, the resulting TRIMMEAN would be 3.67.

Example #3

=TRIMMEAN(C1:C12, 0.1)
Here, the TRIMMEAN function calculates the mean from the range C1 to C12, excluding 10% from each end of the dataset. For example, if C1:C12 contains {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, the mean would be computed as 6.5, after excluding the lowest and highest values.

Error handling

  • NUM!: This error occurs if the percent value is not between 0 and 1 or if there are not enough data points to trim the specified percentage.
  • VALUE!: This error indicates that the data provided is not a valid range or array for the TRIMMEAN function to process.

Conclusion

In summary, the TRIMMEAN function is an essential tool for analysts and data scientists who seek to derive meaningful averages from datasets while minimizing the influence of outliers. By adjusting the percentage of data trimmed from both ends of the dataset, users can tailor their analysis to fit their specific data needs, leading to more reliable insights.

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