The Economist: Election predictions, confidence intervals, and measures of central tedency.

The Economist created interactive visualizations for various polling data related to the 2020 U.S. Presidential election. 

While illustrating this data, they used different measures of central tendency and different confidence intervals. Like, it is one thing to say that Candidate A is polling at 47% with a margin of error of 3.2%. I think it is much more useful to illustrate what the CI is telling us about the likely true parameter, based on what we have collected from our imperfect sample. The overlap in confidence intervals when polling is essential to understanding polling. 

How to use in class:

1) Electoral college predictions, illustrated with median, 60%, and 95% confidence intervals.

Also, I like how this illustrates the trade-off between precision and the size of a confidence interval. The 60% CI is more narrow, but you are only 60% confident that it contains the true number of electoral college votes. Meanwhile, the 95% confidence interval is much wide but also more likely to contain the true portion of votes. 

2). Popular vote illustrated with mean and a 95% confidence intervals:

3) I think these visualizations do a great job of really emphasizing what CIs give us: In an unpredictable world, they provide space in which our best guess of the truth. 

4) Data visualization example of how to visualize a measure of central tendency and confidence interval with repeated measure data. 

5) There are multiple ways to gather data about an underlying question. The question here: Who will win the 2020 US presidential election? Here, popular vote and electoral college votes are both taken into consideration when weighing chances.