Discard the lowest and the highest 3 values: We now have 6 of the 12 observations remaining; next, we calculate the arithmetic mean of these numbers: This is the interquartile mean.
For comparison, the arithmetic mean of the original dataset is due to the strong influence of the outlier, 38.
The above example consisted of 12 observations in the dataset, which made the determination of the quartiles very easy.
We can adjust the method of calculating the IQM to accommodate this.
So ideally we want to have the IQM equal to the mean for symmetric distributions, e.g.: has a mean value xmean = 3, and since it is a symmetric distribution, xIQM = 3 would be desired.