In my Honors Statistics class, we have days devoted to discussing thorny issues surround statistics. One of these days is dedicated to the disconnect between science and science reporting in popular media. 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 r esearch 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. Pro...

This headline is a good example of a) journalists misrepresenting statistics as well as b) confidence intervals/margin of error more broadly. See the headline below: In actuality, Bernie didn't exactly take the lead over Hillary Clinton. Instead, a Quinnipiac poll showed that 41% of likely Democratic primary voters in Iowa indicated that they would vote for Sanders, while 40% reported that they would vote for Clinton. If you go to the original Quinnipiac poll , you can read that the actual data has a margin of error of +/- 3.4%, which means that the candidates are running neck and neck. Which, I think, would have still been a compelling headline.  I used this as an example just last week to explain applied confidence intervals. I also used this as a round-about way of explaining how confidence intervals are now being used as an alternative/compliment to p -values.