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...
So, I am very pro-science, but I have a soft spot in my heart for medical research that improves medical outcomes without actually requiring medicine, expensive interventions, etc. And after spending a week in the NICU with my youngest, I'm doubly fond of a way of helping the littlest and most vulnerable among us. One example of such was published in the journal Pediatrics and written up by NPR. In this case, they found that fewer mistakes are made when not-yet-named NICU babies are given more distinct rather than less distinct temporary names. The unnamed baby issues is an issue in the NICU, as babies can be born very early or under challenging circumstances, and the babies' parents aren't ready to name their kids yet. Traditionally, hospitals would use the naming convention "BabyBoy Hartnett" but several started using "JessicasBoy Hartnett" as part of this intervention. So, distinct first and last names instead of just last names. They measured patient mistakes by counting the number of Retract-and-Reorders, or how often a treatment was listed in a patient’s record, then deleted and assigned to a different patient (due to a mistake being corrected). They found that the number of retract-and-reorders decreased following the naming convention change.
This researcher DID NOT use paired t-tests their analyses. However, this research presents a good conceptual example of within-subject t-tests. As I often do around this blog, I created fake t-test data that mimicked the findings with fewer R-and-R's for doubly named babies. The data was created via Richard Lander’s data generator website:
This researcher DID NOT use paired t-tests their analyses. However, this research presents a good conceptual example of within-subject t-tests. As I often do around this blog, I created fake t-test data that mimicked the findings with fewer R-and-R's for doubly named babies. The data was created via Richard Lander’s data generator website:
| Before intervention | After Intervention | |
| NICU 1 | 47 | 36 |
| NICU 2 | 45 | 26 |
| NICU 3 | 52 | 38 |
| NICU 4 | 50 | 32 |
| NICU 5 | 46 | 42 |
| NICU 6 | 38 | 20 |
| NICU 7 | 63 | 41 |
| NICU 8 | 40 | 27 |
| NICU 9 | 37 | 26 |
| NICU 10 | 40 | 29 |