Common and Rare

Using distributions to examine which values are common and which are rare.

Thomas Kinzeler , Daniel Kaplan

Alternative document formats: Word & PDF


English has many words to describe what statistician’s call frequency: common, unusual, rare, infrequent, uncommon, and so on. There’s no precise, numerical meaning to these words; they are used to create an impression.

In statistics, it’s helpful to have standard ways to refer to frequency. The standard deviation provides a widely accepted measure of commonality or rareness. It’s not just statisticians who use this measure. They are used in psychology and social science, in physical science (where the standard deviation is often called “sigma”). In medicine, the standard deviation underlies a surprising number of diagnostic criteria. In criminal cases in court, the usual standard for evidence is “beyond a reasonable doubt.” But in civil discrimination cases, for instance employment discrimination or jury selection, the US Supreme Court has described compelling evidence as lying outside “two or three standard deviations.”

In this lesson, you are going to explore the use of the standard deviation as a kind of ruler for expressing frequency. For simplicity of speech, we’ll adopt the English words “common,” “uncommon,” and “rare” to refer to specific intervals:

Since it’s so long-winded to say “within 2 standard deviations of the mean,” statisticians have adopted a scale called the z-score. In the language of the z-score, “within 2 standard deviations of the mean” is written \(|\ z\ | < 2\). Similarly, “rare on the left size of the mean” is \(z < -3\).


Open up the Little App Density Little App. (See footnote1). In the Data tab in the top tool bar, set the Source Package to Little Apps, and the Data set to NHANES2. Set the response variable to height_adults.

  1. The graphic shows a traditional plot of the distribution of the response variable, called a density plot. If you’re familiar with a histogram, you might like to think about a density plot as a kind of smoothed histogram without the jagged, abrupt changes from bar to bar.

  2. Using your everyday experience, write down a range of human heights that you think of as “common.” Similarly, write down a tall height that’s rare and a short height that’s rare.

  1. In the Data tab, switch the Data set to Natality_2014. Use mager, the age of the mother when she gave birth, as the response variable. Go back to the Graph tab, Drag the bars so that about 10% of the distribution is to the left of common and 10% is to the right.

    • At what age is the leftmost boundary of ‘common’? . . .

    • At what age is the rightmost boundary of ‘common’? . . .

  2. As set in (3), common covers about 80% of the distribution. If you place the cursor on the lower common bar and leave it there for a few seconds, the z-score will be displayed above the graph, next to the bookmark icon in the tool bar. You can do this at any point on the curve.

    • At what standard deviation measure is the leftmost boundary of ‘common’? . . .

    • At what standard deviation measure is the rightmost boundary of ‘common’? . . .

  3. Consider blood pressure. A high systolic blood pressure is generally defined to be at or above 130 mmHg. Switch back to the NHANES2 data set and select systolic as the response variable.

    • *What fraction of the people in NHANES2 have a systolic pressure above 130 mmHg? . . .