A histogram is a graph that counts the number of occurrences of data points in a series of ranges or bins. An example directly applicable to your lives is the assignment of grades to a class. Consider the following table containing grades for 50 students:
| 55 | 77 | 76 | 94 | 65 |
| 73 | 64 | 75 | 78 | 75 |
| 97 | 69 | 82 | 82 | 85 |
| 67 | 83 | 80 | 91 | 70 |
| 82 | 85 | 70 | 90 | 70 |
| 91 | 81 | 66 | 68 | 71 |
| 88 | 48 | 63 | 63 | 69 |
| 71 | 92 | 81 | 72 | 82 |
| 72 | 94 | 74 | 71 | 83 |
| 58 | 71 | 60 | 88 | 91 |
To assign grades on a curve, we need to know how the grades are distributed between the best and worst scores. To make a histogram, we count up the number of data points between, for instance, 45 and 50, 50 and 55, 55 and 60, and so forth up to 95 to 100. Each of these ranges defines a 'bin' and the value in the 'Score' column reports the highest value in the bin:
| Score | Number or Scores |
| 50 | 1 |
| 55 | 1 |
| 60 | 2 |
| 65 | 4 |
| 70 | 8 |
| 75 | 10 |
| 80 | 4 |
| 85 | 10 |
| 90 | 3 |
| 95 | 6 |
| 100 | 1 |
The graph of this table is the histogram where bin #1 corresponds to scores of 46-50 up to bin #11 which tells us that one student had a score between 96 and 100 points.
The peaks in the histogram identify the most common scores that occur in the data. Note that the graph has been plotted as a series of rectangles, to suggest the 'binning' process that occurs when the score data are counted. These rectangles are often omitted when the number of elevation ranges is large (since it makes the graphy very messy). The resulting graph then looks like:
