For Day 2, students compare estimates made using a simple random sample, a cluster sample, and a systematic random sample. Students discover that the cluster and systematic random sampling methods both produce estimates with lower variability than estimates made using an SRS. You can now use the Justin Timberlake context to help students understand the difference between stratified and cluster samples.

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Based on the work that students did yesterday, have them predict in advance which of these methods will be good and bad. After your our conversations yesterday, we really want the students to be focusing on if the sampling method gets people people from all different distances from the stage. 

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Teaching Tip:

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Make sure to have students fix the cluster sample! While clustering by rows is not ideal, after analyzing the data ask them how we could make this method better. We want the students to tell us to cluster by column so we get individuals from every row. During the debrief we can now strategically tie this to the idea that clusters should be "mini populations".  

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Finish off talking about all of the methods presented over the past two days. We want students to take away that each method (when used properly) can produce good results. Each one comes with different advantages such as speed / ease of data collection or decreased variability. 

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