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Showing posts from January, 2021

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Statistics hiding in plain sight: How this four-minute long interview on polling data can be used in class

Statistical literacy is important. I love examples of folks using statistical concepts without being snobby statistical gatekeepers.  For instance, see jargon-free statistical literacy that helps people make important decisions about their children's health or informs the  immigration debate .   As stats teachers, I think we are remiss if we don't talk about political polling in our classes. At least a little bit. Here is a brief, nonpartisan interview about political polling you can use in class to see whether students can identify the concepts they've learned. Here is a short  NPR i nterview o n  how to think properly about political polling. If you use the interview in class, you could ask your students to identify the statistics and research methods mentioned.  I clocked margin of error,  sampling error,  and research methodology evaluation. If using an audio interview isn't your thing, check out t his summary piece from NPR, based on the...

Chi-square Test of Independence using CNN exit polling data

If you are trying to explain the Chi-Square Test of Independence to your students, here are some timely examples that are political and not polarizing. Well, I don't think it is polarizing. I'm sure there are people out there that disagree. Maybe some of the questions are polarizing? Regardless, it is nice to have an example that uses a current event with easy to understand data.  The example comes from  CNN. The network conducted exit polling during the 2020 presidential election . I'm sure they didn't intend to provide us with a bunch of chi-square examples, but here we are. Essentially, CNN divided Biden and Trump voters into many categories with not a parameter to be had. I have included a few of the tables here, but there are many others on the website .  They illustrate different designs (2x2, 2x3, 2x4, etc.) and different magnitudes of difference between expected and observed values.