A frequency distribution is a table of data that details the rate at which certain characteristics appear in a sample population. For example, it may be a frequency distribution of the heights of major league basketball players. Construct the table after collecting heights for each member of the sample population (i.e., the number of players) and include the class width. The class width is the range of data values in each section of your chart. In this example, you might have one class representing heights of 60 to 69 inches, the next of 70 to 79 inches, and so on for as many classes as you want in your frequency distribution. Use a mathematical method to determine the range of values for class widths.

## Find Largest Data Value

Determine the largest data value in your sample data set. For the basketball player height example, this is the height of the tallest basketball player.

## Find Smallest Data Value

Determine the smallest data value in your set. In this example, use the height of the shortest basketball player.

## Subtract Smallest Value From Largest Value

Subtract the smallest data value from the largest data value. In this example, subtract the shortest player's height from the tallest player's height. If the tallest player is 200 centimeters tall and the shortest player is 188 centimeters tall, work out 200 - 188 = 12.

## Divide Difference by Number of Classes

Divide the difference between the shortest and tallest players' heights by the number of classes that you wish to have in your frequency distribution. For example, if you want to make a frequency distribution with four classes, divide the difference by five. In this example, work out 12 รท 4 = 3.

The wider the range of data values you have accumulated, the more classes you should select.

If necessary, round up the dividend to the next whole number. If your dividend is 3.4, round it up to 4. Note that this is not the same as the normal rules of rounding. This number is the class width.

#### TL;DR (Too Long; Didn't Read)

If you are determining the class width from an already-constructed frequency table, simply subtract the bottom value of one class from the bottom value of the next-highest class.