Bin width

Bin width sets the resolution of a histogram: wide bins smooth the picture and can bury real structure, while narrow bins expose detail and can promote random noise into a fake peak. For example, 40 exam scores running from 52 to 98 fill 5 bins of width 10 (50 to 60, 60 to 70, and so on up to 100), but at width 5 those same scores fill 10 bins and one broad hump can split in two, so the shape now reads as bimodal. A rough starting point is the range divided by the number of bins you want, here $(98 - 52) / 5 = 9.2$, which you round to a readable 10. Because the choice can change your conclusion, check a second bin width before you commit to a description of the shape.