My students are wrapping up the part of the course where we cover descriptive statistics. I gave them two sets of data (test scores from two different versions of the same exam) and they spent the day in class computing sample statistics and creating graphs for each sample. Their overall goal was to analyze their results and determine whether there was a significant difference between the two versions or not.

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Students compared measures of central tendency and the 5-number summaries and I asked them to share their observations. They went on to compute measures of dispersion and then we talked about whether the dispersion of each sample was similar. Finally they created histograms, pie charts, and boxplots and we discussed what they felt the graphs were telling them.

We had another great opportunity to discuss the fact that a perceived difference may not be significant unless we can determine whether the observed difference (the means were 4.8 points apart) would be unusual through some sort of repeated sampling.

This leads into our sixth project of the semester where we will use the randomization test for two means to determine whether the observed difference was significant. We will use StatCrunch for this test, although there are many other tools out there that can be used. My students will then move on to apply this test to two sets of data they collected. I will blog about the outcomes of that project in my next post.