Histograms are adept at revealing the distribution of data values, especially the shape of the
distribution and any outlier values. They are included in introductory statistics texts, research
methods texts, and in the popular press, yet students often have difficulty interpreting the
information conveyed by a histogram. This research identifies and discusses four
misconceptions prevalent in student understanding of histograms. In addition, it presents pre and
post-test results on an instrument designed to measure the extent to which the
misconceptions persist after instruction. The results presented indicate not only that the
misconceptions are commonly held by students prior to instruction, but also that they persist
after instruction. Future directions for teaching and research are considered.
The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education