# How to Describe Data Patterns in Statistics

Graphic displays are useful for seeing patterns in data. Patterns in data are commonly described in terms of: center, spread, shape, and unusual features. Some common distributions have special descriptive labels, such as symmetric, bell-shaped, skewed, etc.

## Center

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Graphically, the center of a distribution is located at the median of the distribution. This is the point in a graphic display where about half of the observations are on either side. In the chart to the right, the height of each column indicates the frequency of observations. Here, the observations are centered over 4.

The spread of a distribution refers to the variability of the data. If the observations cover a wide range, the spread is larger. If the observations are clustered around a single value, the spread is smaller.

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Consider the figures above. In the figure on the left, data values range from 3 to 7; whereas in the figure on the right, values range from 1 to 9. The figure on the right is more variable, so it has the greater spread.

## Shape

The shape of a distribution is described by the following characteristics.

• Symmetry. When it is graphed, a symmetric distribution can be divided at the center so that each half is a mirror image of the other.
• Number of peaks. Distributions can have few or many peaks. Distributions with one clear peak are called unimodal, and distributions with two clear peaks are called bimodal. When a symmetric distribution has a single peak at the center, it is referred to as bell-shaped.
• Skewness. When they are displayed graphically, some distributions have many more observations on one side of the graph than the other. Distributions with fewer observations on the right (toward higher values) are said to be skewed right; and distributions with fewer observations on the left (toward lower values) are said to be skewed left.
• Uniform. When the observations in a set of data are equally spread across the range of the distribution, the distribution is called a uniform distribution. A uniform distribution has no clear peaks.

Here are some examples of distributions and shapes.

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Symmetric, unimodal,
bell-shaped
Skewed right   Non-symmetric, bimodal

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Uniform   Skewed left   Symmetric, bimodal

## Unusual Features

Sometimes, statisticians refer to unusual features in a set of data. The two most common unusual features are gaps and outliers.

• Gaps. Gaps refer to areas of a distribution where there are no observations. The first figure below has a gap; there are no observations in the middle of the distribution.
• Outliers. Sometimes, distributions are characterized by extreme values that differ greatly from the other observations. These extreme values are called outliers. The second figure below illustrates a distribution with an outlier. Except for one lonely observation (the outlier on the extreme right), all of the observations fall between 0 and 4. As a "rule of thumb", an extreme value is often considered to be an outlier if it is at least 1.5 interquartile ranges below the first quartile (Q1), or at least 1.5 interquartile ranges above the third quartile (Q3).
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Gap   Outlier