Statistics How To

Class Width

Frequency Distribution Table >

Class width refers to the difference between the upper and lower boundaries of any class (category). It may also be the difference between the upper limits of two consecutive (neighboring) classes, or the difference between the lower limits of two consecutive classes.

Note that this is different than the difference between the upper and lower limits of a class.

Calculating Class Width in a Frequency Distribution Table

In a frequency distribution table classes must all be the same width. This makes it easy to calculate class width. Simply calculate the range of the entire data set by subtracting the lowest point from the highest, and then divide it by the number of classes. Round this number up (usually, to the nearest whole number).

Example of Calculating Class Width

Suppose you are analyzing data from a final exam given at the end of a statistics course. The number of classes you divide them into is somewhat arbitrary, but there are a few things to keep in mind. First, you want to make few enough categories so that you have more than one item in each category. You also want a number that is easy to manipulate; usually, something between five and twenty is a good idea. In this case, since you are analyzing a relatively small class of 25 students, you might decide to create a frequency table with five classes.

The student scores might be 52, 82, 86, 83, 56, 98, 71, 91, 75, 88, 69, 78, 64, 74, 81, 83, 77, 90, 85, 64, 79, 71, 64, and 83. Then the range, the difference between the highest and lowest score, would be 92 – 52, or 46. So class width would be 46/5, so 9.2, or rounded up to a whole number, 10.

References

Gleaton, James U. Lecture Handout: Organizing and Summarizing Data. Retrieved from http://www.unf.edu/~jgleaton/LectureTransCh2.doc on August 27, 2018.

Jones, James. Statistics: Frequency Distributions & Graphs. Retrieved from https://people.richland.edu/james/lecture/m170/ch02-def.html on August 27, 2018.

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Class Width was last modified: September 11th, 2018 by Stephanie