I have blogged about this issue before and use many of these blog posts to guide this discussion: This video by John Oliver is hilarious and touches on p-hacking in addition to more obvious problems in science reporting, this story from NPR demonstrates what happens when a university's PR department does a poor job of interpreting research results. The Chronicle covered this issue, using the example of mis-shared research claiming that smelling farts can cure cancer (a student favorite), and this piece describes a hoax that one "researcher" pulled in order to demonstrate how quickly the media will pick up and disseminate bad-but-pleasing research to the masses.
When my students and I discuss this, we usually try to brain storm about ways to fix this problem. Proposed solutions: Public shaming of bad journalists, better editing of news stories before they are published, a prestigious award system for accurate science writing. And another idea my students usually arrive upon? Better training for journalists.
So, you can imagine how pleased I was to discover that such classes already exist via Sense about Science USA.
They support this mission in a few different ways. They advocate for registering all medical trials conducted on humans. They are training scientists to more effectively communicate their findings to the public. And, apropos of this blog, they are also training journalists to better understand statistics AND offer one-on-one consulting to journalists trying to understand data.
Here is their description of why it is important to better train journalists.
How to use in class:
1) Instead of just showing students the problems associated with poor science writing, let's show them a possible solution as well.
2) Statistics isn't just for statisticians, statistics are for anyone who wants to better understand policy issues, emerging research, and evidence-based practices in their field.
3) Show your students some examples of poor science writing. Have them develop a brief presentation that would address the most common statistical mistakes made by science writers.