One of the more commonly used pictorials in statistics is the frequency histogram, which in some ways is similar to a bar chart and tells how many items are in each numerical category. For example, suppose that after a garage sale, you want to determine which items were the most popular: the high‐priced items, the low‐priced items, and so forth. Let's say you sold a total of 32 items for the following prices: $1, $2, $2, $2, $5, $5, $5, $5, $7, $8, $10, $10, $10, $10, $11, $15, $15, $15, $19, $20, $21, $21, $25, $25, $29, $29, $29, $30, $30, $30, $35, and $35.

The items sold *ranged* in price from $1 to $35. First, divide this **range** of $1 to $35 into a number of categories, called **class intervals**. Typically, no fewer than 5 and no more than 20 class intervals work best for a frequency histogram.

Choose the first class interval to include your lowest (smallest value) data and make sure that no *overlap* exists so that one piece of data does not fall into two class intervals. For example, you would not have your first class interval be $1 to $5 and your second class interval be $5 to $10 because the four items that sold for $5 would belong in both the first and the second intervals. Instead, use $1 to $5 for the first interval and $6 to $10 for the second. Class intervals are mutually exclusive.

First, make a table of how your data is distributed (see Table 1). The number of observations that falls into each class interval is called the **class frequency**.

Note that each class interval has the same width. That is, $1 to $5 has a width of five dollars, inclusive; $6 to $10 has a width of five dollars, inclusive; $11 to $15 has a width of five dollars, inclusive; and so forth. From the data, a frequency histogram would look like what you see in Figure 1.

Figure 1.Frequency histogram of items sold at a garage sale.

Unlike in a bar chart, the class intervals are drawn immediately adjacent to each other.