Learning Goals
During this activity, you will:
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video placement
[Intro: The data set used in this activity comes from the independent Tax Policy Center, a non-partisan group. We’ll look at the Tax Policy Center’s distributional tables of tax cuts by income levels, which were constructed in February 2018, in order to apply public policy analysis to a tax reform bill signed into law in 2017. Specifically, we’ll use this situation to explore how boxplots can be used to compare distributions across several populations and draw inferences about them. As you read the two quotes at the start of the activity, consider the differences in them and ways in which you might investigate the accuracy of their claims.]
Good Tax Policy?

Read these two quotes about the U.S. 2017 Tax Reform bill:[1]
“We’re doing everything we can to reduce the tax burden on you and your family. By eliminating tax breaks and loopholes, we will ensure that the benefits are focused on the middle class, the working men and women, not the highest income earners. Our framework includes our explicit commitment that tax reform will protect low-income and middle-income households, not the wealthy and well connected.” – President Donald Trump in 2017, prior to the bill’s enactment[2]
“You remember just a few years ago when Trump and my Republican colleagues voted for almost $[latex]2[/latex] trillion in tax breaks for the wealthiest people in this country and the largest corporations.” – Bernie Sanders in 2021, after the bill was enacted[3]
question 1
In this activity, you’ll see how to use a boxplot to provide a visual summary of a quantitative variable, and how boxplots can be used to compare the distributions of multiple populations.
- Data and lesson context adapted from Skew the Script, www.skewthescript.org ↵
- Administration of Donald J. Trump. (2017, September 27). Remarks in Indianapolis, Indiana. Govinfo.gov. https://www.govinfo.gov/content/pkg/DCPD-201700693/html/DCPD-201700693.htm ↵
- Cooper, A. (Interviewer). (2021, January 29.). CNN. ↵