The thing about my way of teaching stats is that my cool, novel examples get old really fast. Like, pop culture is on a thirty-day cycle. Anyway, YEARS ago, 2014 to be exact, I blogged about illustrating bimodal data distributions using baby name data from the Social Security Administration. Specifically, when an older name comes back into vogue, it has a bimodal distribution when you plot time on the X axis. I stand by this example. However, all of the data illustrations come from 2014. When our traditionally aged college students were 6 or 7. Which makes this example feel a little stale. However, here is some data, courtesy of Redditor fevaah , showing old names that are back in vogue through 2025 ! And Eleanor, Violet, Nora, and Hazel are here, illustrating bimodal distributions for a new generation. If you used my old baby-name data, this is a quick, easy refresh for your lectures (or a fun new addition if you didn't). If this idea was help...
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.
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?
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?