## How do you use Winsorizing?

This is specified as a total percentage of untouched data. For example, if you want to Winsorize the top 5% and bottom 5% of data points, this is equal to 100% – 5% – 5% = 90% Winsorization. A 80% Winsorization means that 10% is modified from each tail area (see Tips on Cut-Off Point Selection below). Mean = 33.405.

## What is the difference between trimming and Winsorizing?

Winsorizing data means to replace the extreme values of a data set with a certain percentile value from each end, while Trimming or Truncating involves removing those extreme values.

**What is Winsorizing data transformation in statistics?**

Winsorizing or winsorization is the transformation of statistics by limiting extreme values in the statistical data to reduce the effect of possibly spurious outliers. It is named after the engineer-turned-biostatistician Charles P. Winsor (1895–1951). The effect is the same as clipping in signal processing.

**When should you trim data?**

Data trimming is applied to data sets when dealing with outliers. Outliers are extreme values that disrupt distributions in a data set. Cutting extreme values can be useful for the mean but not for the median. There is no single accepted standard for dealing with outliers in statistical processes.

### How do you find Winsorized mean?

The winsorized mean is achieved by replacing the smallest and largest data points, then summing all the data points and dividing the sum by the total number of data points.

### Does Winsorizing affect median?

Note that the median did not change at all. In all but the most extreme cases, the median is robust to outliers and unaffected by Winsorizing because the extreme values stay on their side of the median .

**When should you Winsorize data?**

Trimming: It makes sense to trim data values when some values seem completely unreasonable, i.e. they’re a result of a data entry error. Winsorizing: It makes sense to winsorize data when we want to retain the observations that are at the extremes but we don’t want to take them too literally.

**Should I Winsorize data?**

You should decide whether or not to winsorize data after collecting the data, not before. You should see if there actually are extreme outliers before you decide to perform winsorization. If no extreme outliers are present, winsorization may be unnecessary.

## How do you do winsorization in R?

Another approach to winsorization is to try to just move the datapoints that are likely to be troublesome. That is, only move data that are too far from the rest. Here is such an R function: Figures 3 and 4 show the results of this function using the same data as in Figures 1 and 2.

## How do I use the mean function in R for winsorization?

The mean function in R has a trim argument so that you can easily get trimmed means: Trimming removes a certain fraction of the data from each tail. One approach to winsorization is just to copy trimming, but replace the extreme values rather than throw them out. Here is an R function that does this: Figures 1 and 2 show this function in action.

**What is an example of winsorization in statistics?**

For example, a 90% winsorization sets all observations greater than the 95th percentile equal to the value at the 95th percentile and all observations less than the 5th percentile equal to the value at the 5th percentile. In effect, to winsorize data means to change extreme values in a dataset to less extreme values.

**What does it mean when a vector is winsorized?**

View source: R/DescTools.r Winsorizing a vector means that a predefined quantum of the smallest and/or the largest values are replaced by less extreme values. Thereby the substitute values are the most extreme retained values. a numeric vector to be winsorized.