Which statement describes trimming in data analysis?

Which statement describes trimming in data analysis?

• A. It removes extreme values from the middle of the distribution.
• B. It computes the mean after removing a certain percentage of the extreme values at both ends.
• C. It replaces extreme values with the mean.
• D. It uses all data points to compute the mean.

Answer: (B)

Trimming is a way to reduce the influence of outliers by discarding a portion of the extreme values at both ends of a data set, then computing the mean on the remaining data. A p% trimmed mean means you remove p% of the smallest and p% of the largest observations before averaging what’s left. For example, with a 20% trim on a data set of six values, you would drop the smallest and largest value and then average the remaining four. This is why the correct statement describes trimming as computing the mean after removing a certain percentage of extreme values at both ends. It’s different from replacing extremes with another value (that’s more like winsorizing) and from using all data points to compute the mean (that’s the regular mean).

Trimming is a way to reduce the influence of outliers by discarding a portion of the extreme values at both ends of a data set, then computing the mean on the remaining data. A p% trimmed mean means you remove p% of the smallest and p% of the largest observations before averaging what’s left. For example, with a 20% trim on a data set of six values, you would drop the smallest and largest value and then average the remaining four.

This is why the correct statement describes trimming as computing the mean after removing a certain percentage of extreme values at both ends. It’s different from replacing extremes with another value (that’s more like winsorizing) and from using all data points to compute the mean (that’s the regular mean).