A First Look at Graphing Distributions of Quantitative Data

[This post was supposed to be published last week, but I got really busy and forgot I had put this post together. Then, I realized that I didn't have this post to refer back to as I was writing my next post. So here it is. Just realize there were several lessons that took place between this post and the next even though both posts were published on the same day.]

Up to this point, the data we've been working with in my Inferential Probability and Statistics class has been categorical data. We are beginning to transition to working with quantitative data. To that end we conducted an observational study on name popularity and conducted an experiment on the melting time of chocolate chips.

The name popularity activity comes from Making Sense of Statistical Studies. Students found the ranking of their given name for their birth year and for the most recent year data was available using the Social Security Administration's Baby Name database.One of the questions asked students "to construct a graphical display that shows the distribution of the [year] ranks data."

Students either had no idea how to proceed or started to create graphs displaying categorical characteristics of the data set, such as year versus gender or high versus low ranks. When I asked my classes about histograms, only a handful indicated they knew about or how to use histograms. This was interesting since students had been taught (note: I am not using the term learned, as it is evident that learning did not take place) histograms since the sixth grade.

I wonder why students have so much exposure to histograms yet seem so ignorant as to their use. Is it possible that so much time is spent teaching students how to make "pretty" graphs without really thinking about what the graph is telling you about the data distribution? Even with an AP Statistics class, it is difficult to move students away from just making a graph to considering what the graph tells you about your data.

I will be working primarily with stem plots, histograms and box plots to analyze data sets. It will be interesting to see how well students move off of here is how to make a graph to here is what the graph is telling you about the data distribution.