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Updated example of using baby names to illustrate bimodal distributions.

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...

Free online research ethics training

Back in the day, I remember having to complete an online research ethics course in order to serve as an undergraduate research assistant at Penn State. I think that such training could be used as an exercise/assessment in a research methods class or an advanced statistics class. NOTE: These examples are sponsored by the American agencies and, thus, teach participants about American laws and rules. If you have information about similar training in other countries (or other free options for American researchers), please email me and I will add the link. Online Research Ethics Course from the U.S. Health and Human Service's Office of Research Integrity. Features: Six different learning modules, each with a quiz and certificate of completion. These sections include separate quizzes on the treatment of human and animal test subjects. Other portions also address ethical relationships between PIs and RAs and broader issues of professional responsibility when reporting results. ...

Dread Fall 2015 Semester

It's coming, guys. But let's get ahead of it. I thought I would re-share some resources that you may want to consider working into your curriculum this year. I picked out a few lessons and ideas that also require a bit of forethought and planning, especially if they become assessment measures for your class. Center for Open Science workshops: As previously discussed on this blog , COS offers f ree consultation  (face-to-face or online) to faculty and students in order to teach us about the open framework for science. They provide guidance about more more traditional statistical issues, like power calculations and conducting meta-analysis in addition to lessons tailored to introducing researchers to the COS framework. Take your students to an athletic event , talk about statistics and sports : I took my students to a baseball game and worked some statsy magic. You can do it, too. If not a trip to the ballpark, an on-campus or televised athletic event will w...

Ben Blatt's "Bad Latitude" and "You Live in Alabama. Here’s How You’re Going to Die"

Ben Blatt of Slate mined through Center for Disease control data in order to provide us with 13 different maps of the United States and mortality information for each state . Below, information on disproportionately high cases of death in each state. While the maps are morbid and interesting, the story behind the maps ( read the story here about how data can be easily misrepresented by maps ) make this a good example of how easily data can be distorted. The story along with the maps unveils several issues that statisticians/researchers must consider when they are presenting descriptive statistics. In this instance, Blatt had to sort through the data to eliminate the most common causes of death (heart disease, cancer, etc.) in order to uncover unique data for each state. Relatedly, he highlights the fact that "disproportionately" does not mean "most": "But this map—like many maps which purport to show attributes meant to be “distinct” or “disproporti...