Amanda Aronczyk's "Cancer Patients And Doctors Struggle To Predict Survival"

Warning: This isn't an easy story to listen to, as it is about life expectancy and terminal cancer (and how doctors can best convey such information to their patients). Most of this news story is dedicated to training doctors on the best way to deliver this awful news.

 But Aronczyk, reporting for NPR, does tell a story that provides a good example of high-stakes applied statistics. Specifically, when explaining life expectancy to patients with terminal cancer, which measure of central tendency should be used? See the quote from the story below to understand where confusion and misunderstanding can come from measures of central tendency.

"The data are typically given as a median, which is different from an average. A median is the middle of a range. So if a patient is told she has a year median survival, it means that half of similar patients will be alive at the end of a year and half will have died. It's possible that the person's cancer will advance quickly and she will live less than the median. Or, if she is in good health and has access to the latest in treatments, she might outlive the median, sometimes by many years.

Doctors think of the number as a median, but patients usually understand it as an absolute number, according to Dr. Tomer Levin, a psychiatrist who works with cancer patients and doctors at Memorial Sloan Kettering Cancer Center in New York. He thinks there is a breakdown in communication between the doctor and patient when it comes to the prognostic discussion."

A couple of ways this could be used as a discussion starter:

1) How could a doctor best describe life expediencies? What may be more useful? Interquartile range? A mean and standard deviation? Range? What is the simplest way to explain these measures to a person receiving horrible news?

2) This could also be useful in a cognitive/memory class, as the story refers to research that has found that cancer patients retain little of the information they receive when they get their diagnosis. How can statistical information be conveyed in an understandable manner to individuals who are experience enormous stress?

Philip Bump's "How closely do members of congress align with the politics of their district? Pretty darn close."

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.

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 in order to explain why they don't align as closely with their districts.
3) Illustrates a linear relationship. Illustrates outliers. Illustrates political data. Accessible example for your students.

Pew Research Center's "Major Gaps Between the Public, Scientists on Key Issues"

This report from Pew  highlights the differences in opinions between the average American versus members of the American Association for the Advancement of Science (AAAS). For various topics, this graph reports the percentage of average Americans or AAAS members that endorse each science related issues as well as the gap between the two groups. Below, the yellow dots indicate the percentage of scientists that have a positive view of the issue and the blue indicate the same data for an average American.

If you click on any given issue, you see more detailed information on the data.

In addition to the interactive data, this report by Funk and Rainie summarizes the main findings. You can also access the original report of this data (which contains additional information about public perception of the sciences and scientists).

This could be a good tool for a research methods/statistics class in order to convince students that learning about the rigors of the scientific method/hypothesis testing do change the way people evaluate information. It is also a good example of simple descriptive data that students can play with via the interactive interface.