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Note: Applied to a normal population the outlier rule would label observations more than about 2.7 SDs from the mean as outliers. Samples do not precisely emulate populations, but normal tails have enough probability that it is not rare for moderately large samples to have some outliers. Boxplot 'outliers' are worth a second look, but they are by no means necessarily 'errors'.

Note: Applied to a normal population the outlier rule would label observations more than about 2.7 SDs from the mean as outliers. Samples do not precisely emulate populations, but normal tails have enough probability that it is not rare for moderately large samples to have some outliers.

In real data, boxplot 'outliers' are worth a second look, even though they are by no means necessarily 'errors'. (For example, some investigation might show an outlier arose from data entry error or equipment failure.)

Outliers are common in exponential data. It is a characteristic of samples from right-skewed distributions to show numerous 'outliers'. Below are boxplots for 20 samples of size $n = 100$ from an exponential distribution with mean 10.

Outliers are common in exponential data. It is a characteristic of samples from right-skewed distributions to show numerous 'outliers'. Below are boxplots for 20 samples of size $n = 100$ from an exponential distribution with mean 10. (About 99% of such samples will show at least one outlier.)

Boxplots for 20 of the 100,000 normal samples from the simulation above are shown below.

Boxplots for 20 of the 100,000 normal samples from this simulation are shown below.