Unlike the Chartr newsletter, I love this as it feeds my fascination with data and provides interesting examples for the class. As I sit here writing (5/11/24), I am enjoying my other favorite stats newsletter, How to Read This Chart . The current newsletter discusses data visualizations used on the front page of the Post. Such as: Philip Bump lovingly curates this newsletter. One time, he found historic, unlabeled charts and asked readers for help interpreting them . I also thought this one, which compared the margin of error and sample sizes used by major national polling firms, fascinating .

The Ingrham, writing for The Washington Post, used data to investigate the claim that undocumented  immigrants are a large source of crime.  You may hit a paywall when you try to access this piece, FYI. Ingraham provides two pieces of evidence that suggest that undocumented immigrants are NOT a large source of crime. He draws on a  policy brief from the Cato Institute and a research study by Light and Miller  for his arguments. The Cato Institute policy brief   about illegal immigration and crime is actually part of a much larger study . It provides a nice conceptual example of a 3 (citizenship status: Native born, Undocumented Immigrant, Legal Immigrant) x 3 (Crime Type: All crimes, homicide, larceny) ANOVA. I also like that this data shows criminal conviction rates per 100K people, thus eliminating any base rate issues when comparing groups. From: https://www.washingtonpost.com/amphtml/news/wonk/wp/2018/06/19/two-charts-demolish-the-notion-that-i...

Elahe Izadi, writing for The Washington Post, shared polling data from the 1960s. The data focused on public opinion about different aspects of the civil rights movement (March on Washington, freedom riders, etc.). The old data was used to draw parallels between the mixed support for the Civil Rights Movement of the 1960s and the mixed support for current civil rights protests, specifically, Black Lives Matter. Here is the  Washington  Post  story on the polling data, the civil rights movement, and Black Lives Matter. The story is the source of all the visualizations contained below. H ere is the original polling data . https://img.washingtonpost.com/wp-apps/imrs.php?src=https://img.washingtonpost.com/blogs/the-fix/files/2016/04/2300-galluppoll1961-1024x983.jpg&w=1484 https://img.washingtonpost.com/wp-apps/imrs.php?src=https://img.washingtonpost.com/blogs/the-fix/files/2016/04/2300-galluppoll1963-1024x528.jpg&w=1484 I think this is timely data. And...

Elahe Izadi, writing for the Washington Post, did a report on this article by Eichstaedt et. al, (2015) . The original research analyzed tweet content for hostility and noted the location of the tweet. Data analysis found a positive correlation between regions with lots of angry tweets and the likelihood of dying from a heart attack. The authors of the study note that the median age of Twitter users is below that of the general population in the United States. Additionally, they did not use a within-subject research design. Instead, they argue that patterns in hostility in tweets reflect on underlying hostility of a given region. An excellent example of data mining, health psychology, aggression, research design, etc. Also, another example of using Twitter, specifically, in order to engage in public health research ( see this previous post detailing efforts to use Twitter to close down unsafe restaurants ).

http://www.washingtonpost.com/blogs/the-fix/wp/2014/09/29/ believe-it-or-not-some-members-of-congress-are-accountable-to-voters/ Philip Bump (writing for The Washington Post) illustrates the linear relationship between a U.S. House of Representative Representative's politics and their home district's politics. Yes, this is entirely intuitive. However, it is still a nice example of correlations/linear relationships for the reasons described below. Points for class discussion: 1) How do they go about calculating this correlation? What are the two quantitative variables that have been selected? Via legislative rankings (from the National Journal) on the y-axis and voting patterns from the House member's home district on the x-axis. 2) Several outliers' (perhaps not mathematical outliers, but instances of Representative vs. District mismatch ) careers are highlighted within the news story in order to explain why they don't align as closely with their distric...