Yes, a lower inner fence can be negative even when all the data are strictly positive. If the data are all positive, then the whisker itself must be positive (since whiskers are only at data values), but the inner fences can extend beyond the data.

The first quartile, denoted by Q1 , is the median of the lower half of the data set. This means that about 25% of the numbers in the data set lie below Q1 and about 75% lie above Q1 . The third quartile, denoted by Q3 , is the median of the upper half of the data set.

Well, as you might have guessed, the number (here 1.5, hereinafter scale) clearly controls the sensitivity of the range and hence the decision rule. A bigger scale would make the outlier(s) to be considered as data point(s) while a smaller one would make some of the data point(s) to be perceived as outlier(s).

So any value less than 0 or greater than 8 would be a mild outlier. Any data point outside these values is an extreme outlier. For the example set, 3 x 2 = 6; thus 3 – 6 = –3 and 5 + 6 = 11. So any value less than –3 or greater than 11 would be a extreme outlier.

There are two things we should never do with outliers. The first is to silently leave an outlier in place and proceed as if nothing were unusual. The other is to drop an outlier from the analysis without comment just because it’s unusual.

A commonly used rule says that a data point is an outlier if it is more than 1.5 ⋅ IQR 1.5/cdot /text{IQR} 1. 5⋅IQR1, point, 5, dot, start text, I, Q, R, end text above the third quartile or below the first quartile. Said differently, low outliers are below Q 1 − 1.5 ⋅ IQR /text{Q}_1-1.5/cdot/text{IQR} Q1−1.

– If our range has a natural restriction, (like it can’t possibly be negative), it’s okay for an outlier limit to be beyond that restriction. – If a value is more than Q3 + 3*IQR or less than Q1 – 3*IQR it is sometimes called an extreme outlier.